time_series_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=Warning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Return Forecasting: Time Series Analysis & Modelling with CAD-JPY Exchange rate data.\n",
    "In this notebook, you will load historical Canadian Dollar-Yen exchange rate futures data and apply time series analysis and modeling to determine whether there is any predictable behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1982-01-05</th>\n",
       "      <td>184.65</td>\n",
       "      <td>184.65</td>\n",
       "      <td>184.65</td>\n",
       "      <td>184.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-06</th>\n",
       "      <td>185.06</td>\n",
       "      <td>185.06</td>\n",
       "      <td>185.06</td>\n",
       "      <td>185.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-07</th>\n",
       "      <td>186.88</td>\n",
       "      <td>186.88</td>\n",
       "      <td>186.88</td>\n",
       "      <td>186.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-08</th>\n",
       "      <td>186.58</td>\n",
       "      <td>186.58</td>\n",
       "      <td>186.58</td>\n",
       "      <td>186.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-11</th>\n",
       "      <td>187.64</td>\n",
       "      <td>187.64</td>\n",
       "      <td>187.64</td>\n",
       "      <td>187.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price    Open    High     Low\n",
       "Date                                      \n",
       "1982-01-05  184.65  184.65  184.65  184.65\n",
       "1982-01-06  185.06  185.06  185.06  185.06\n",
       "1982-01-07  186.88  186.88  186.88  186.88\n",
       "1982-01-08  186.58  186.58  186.58  186.58\n",
       "1982-01-11  187.64  187.64  187.64  187.64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Currency pair exchange rates for CAD/JPY\n",
    "cad_jpy_df = pd.read_csv(Path(\"cad_jpy.csv\"), index_col=\"Date\", infer_datetime_format=True, parse_dates=True)\n",
    "cad_jpy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>126.37</td>\n",
       "      <td>126.31</td>\n",
       "      <td>126.37</td>\n",
       "      <td>126.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>125.30</td>\n",
       "      <td>125.24</td>\n",
       "      <td>125.30</td>\n",
       "      <td>125.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>123.46</td>\n",
       "      <td>123.41</td>\n",
       "      <td>123.46</td>\n",
       "      <td>123.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>124.54</td>\n",
       "      <td>124.48</td>\n",
       "      <td>124.54</td>\n",
       "      <td>124.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-08</th>\n",
       "      <td>124.27</td>\n",
       "      <td>124.21</td>\n",
       "      <td>124.27</td>\n",
       "      <td>124.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price    Open    High     Low\n",
       "Date                                      \n",
       "1990-01-02  126.37  126.31  126.37  126.31\n",
       "1990-01-03  125.30  125.24  125.30  125.24\n",
       "1990-01-04  123.46  123.41  123.46  123.41\n",
       "1990-01-05  124.54  124.48  124.54  124.48\n",
       "1990-01-08  124.27  124.21  124.27  124.21"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trim the dataset to begin on January 1st, 1990\n",
    "cad_jpy_df = cad_jpy_df.loc[\"1990-01-01\":, :]\n",
    "cad_jpy_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initial Time-Series Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Start by plotting the price. Do you see any patterns, long-term and/or short?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a2bfa9cc10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Rf3jPinF35oEuHtyZfh2aeh7321cDm7VP0/LTShzktHKvCS9fcwzz77OCf1o3qUfPGJKfFCUW9pZWsMfuBNatVfQaR0s37w1Yv/Tf01MiVzaza79WaK0pqtyDKMz3BRQLu9yVADW4m3dEg1I3uXniHB58d2HM+w+6fzJz7YS4u87oE2VvBeCbFfq0UlNyOlomGbRvWs+/vCdM/1WlbvLWXKuyxlkD2tOvQ1PyfMLvXv2O12ev89y/zJVv4T6vlPDs2Fd9zRljtAVhHKjlHgV3/Y/Fm7SYkxLK2U98xamPTeWpqSvCKvbpQRZoUYxZ03Uddx/kxz5elkFJah+q3KMwsHMz+nWobtihjRQUL5Zv2cvY9xaF3X7Rk98ErDeuYZ+AtXaUTV3Bfb3NW6c1nuJBlXsUGhTm8+5NQ/zr63cdyKA0SrYQaynoAo+CYK0aFQUUDYuGuzTwkD99GvNxucA+V47ASVp/Jy5UuceJUylSqdvsi7GJy8m9W4eMbYtQTqBNk6KQsW//34jYBcsxDpRVf8/qb48PVe4xMqRnKwBenrEmw5Io2UAsHbr+dH7/sDVjwjH5tydx3dDuAWNN6hWE2Tv32e+y3EsrtCprPKhyj5F2dnSDu5GxUnf58WB05V7cqIhLj+4SdT83TeoVcO4Rnp0p6yRu5b43hu9cqSaWeu7PiMgWEZnvGmshIpNFZJn92ty17Q4RWS4iS0TkVO93rX3ccdphABytse4K8OPB6GGxgzo3p34Nspx9LvfDc1ceFffx2ca+0goWb9pTo2P3u9wypa5Q0q5jJtF1zKSEZctlYrHcnwNGBY2NAaYYY3oCU+x1RKQPcDHQ1z5mvIjkRMxXgyLr36jQaBkF2B0h5+GQ4ob849JBNG1Qs6e8yhw7x65/cTajHvuC8hj7Krj5anl1CGl5Rej3smLr3pAxxSKWZh1TgeBZxLOBCfbyBOAc1/hEY0ypMWYlsBwYnCRZM4pTIbImJ6iSe3y8aHPYbb8b2YvTDm8XMNakXnXoY6tGoZOmbtz+/FxQ8zPsIIRYJ6HduKPTyipDfe6PTF5ac8FynJr63NsYYzYC2K9OSEAHYK1rv3X2WAgiMlpEZorIzK1bt9ZQjPTh8wl5PuGxj5fxqJ5QdZ5XZ3onKwG8P39jwPr0O4cz9bZh/vVo/VLzXGGSvhyIECm0Qzljmadw821QmV8n/PS+dxb4x356ZMcEpctdkj2h6nUmehofxpgnjTElxpiS4uLaEb+ab190f52yjHU79zN1afbflOLlk8WbeeVbjQiKl2VjT/MvH9u9ZcC2Nk3q0czVn7dRvcgJTPku5V5gL5/Qo1UyxMwI4ZR7eWUVd735fdjckQtcZX47t2jgV+7PfrXKP54LN79UUVPlvllE2gHYr1vs8XWAu+1RR2BDzcXLLtxNs0/446dc/syMEOuitnPVczO5/fXvMy1GraMgz8dRXa24guO7R1bEAzo2i7jdrbDy86xLdOy5/RKUMDPs3l/OVrvpSHD46JfLt/Hi9DXc+9YCr0P9DO1VjAjs8bD8y2NMJquL1FS5vw1cYS9fAbzlGr9YRIpEpBvQE5iRmIjZjdOUQak7hPMdv/DLo/nfDcfTNUw53+7F1vgvhxwS8f3d9VT6trdKXzQorJ01/m5/fZ5/eW9p4CS0U87XK1nX3Zgk3+dj9fb9fLJ4S8h+OgcWnlhCIV8GvgZ6icg6EbkaGAeMFJFlwEh7HWPMAuBVYCHwAXCDMSanMw+Or8WPy0rN+Ok/pnmOF+XnMbBTeKvcabDeIEp4pNtyd5q2x1GtIKv4fv1u/3KwW2b5FivSxcu1Ul7pVu7V251IojPsCesyVe5hiWoOGGMuCbNpeJj9xwJjExGqNuFcsErdwV0d9Lqh3WOu8/+3i4/gi+Xb6BSl0Xpbj3LAebVUu7v96bv2l7P7QLk/EfCJT38AoFOL0Gvo3XnV3lx3K2MnQ3z6SitEMtYaP3URzVBNkFp6zUVlzfb9WgEzCh2b1+f2Ub0Z1iu0fowXzRsWxtTPt5FHxcjaWoLAndF979sLGHDfR3QdM4mPFmzyjzvzCm427j7oX3bH/TtN6uvZJZPDlVhWVLknzOrtuVmC9cSHP+UnT3yZaTGymhuH9UjZex/XvSW/Prn6/X0+4bxBHehQy54Uw5XrGP2fWf7lg+WhntsprjyCrT+WMqpvWwB6tWkMwJjTegPwzYrcCmhIJqrcE+ThD5dkWoSUMX99zVLG6wqF+am7fF665hh+e0qvgLF8n1BlatfTVCyJS27lXllluOONebRpUu2amr1ml991c5s9Qeuet8i1jN5kocq9Blw3tDsPn98/02IkHS8LSgnPmf2ju1iSSZ5Pal35i31lsSj3ar/5kk0/8vKMtbw/f1PAPsGTru5jfv/adwlKmZuoco+DEYe1oWfrRtw+qjej+rX1j1fkwIz9B/M3aSp3DLhD9FJpuXvhE6l18yBuJRyON+es9y8fKA+9GZw3qEOI4dG6cXUJhzdmrw8+REEbZMfFk5cd6V9u7Jrg+tfUFdyQQv9rOrj2hVnRd1L8iWxdWkaOeEkFeT6hspa5ZeLltVmhirooP48JX68OGCuJs05+XUQt9zjw+cSzPVou+92VQEptS/TyY7um/bN9IjX2L+/eX87SzdnX4N2ZIN2w6wCzVu/g0DaNPPYynDdIa9zHiyr3Os5bc9f7MwWD0aYRoRy0uwHVK0j/pZPnq7lb5qzHv+SUR6fy5pz0hA7u3FdG33s+iLqf4948btwn/PQfX3PfOwtD9jmlT1t/lNAJPVrxhV2EbfqdVqpNn3ZNQo5RVLnXadZs38/NE+fy64lzPbfn52oQfwK8+q1V9HTLnvB9UFNFIm6ZNTuskN3fvJKeyccFG/YENLcOR6R5C5/AjLuGM6x3a3+y0pCerfxJYG2a1GNAx6YUN45cQrmuosq9DnPAnqTaGKYqX6lm/4XwF3vSefaanWn/bMtyT/vH1giv2uvBDO/dmiIP5X6+Xca3T/smtG5shUQ652LwzSDPJ2z9sZRHJy/lhhdna60ZFzqhWoepsDXFsi2B3Ww+vXUo1/5nljYkDuKQO6rbul15fNe0f36eiP83y3b2lUY+d+bcPZKGRfk88enykG1XHd+Ngjxh9InVjcIjKfeFG/ewcKOVkzGkZysuHtw5UfFzArXck8CgzpFLuGaK3736HVMWbeZAWaVnDPvHC0Or7AF0a9WQegU+tdyDcLu7ixuF1n9JNT6fUGUCwzGzleCa9X+9eCDDell9G0Yc1prmDQspzPeFzPc8f9Vg+rRvwkPn9aebq7qmE/rYpnHg9/7tqsAnqDFvaLlqB7XcE2D+fady7hNf0SRMinUm2bznIK/PXuevvdG+aT2m3RFY6+3Rj8PHtRfl5/kjQxSyIr48z07kqTIQpZlTxgkW7+yBHTh7YOgEfXCI44mHejfuufHkHvRq25jhh8VWx0dRyz0hGhXl07Aonyy47kOYOGNtwPoGVyGmWCgq8PkjQ5TQxuj9OqQ/QsOpr1Ub0u0/W1LdpezXw3uG3W9Iz9hKZhfk+Tj98HZIlM5L5wxMPGv4iU+X8/FCq7bNB/M3he0Ule0kpNxF5GYRmS8iC0TkFnushYhMFpFl9mvz5IianZRXVrF9b/ojJ6Ixd21iE35F+T613F0EK9RoSiYVODkWNakv0zBKDflk89y0Vf7lERGs7dNdjcSTEXqb6O9SUVnFwx8u4ZfPz8QYw7UvzGLUY1MTlisT1NgtIyL9gGuAwUAZ8IGITLLHphhjxonIGGAMcHsyhM1GFmzIzuJany6J3N81XB3s4b2tC7GoII+DFZUYYzKiyLKNFduqJ51bZyj0znHLRLLcP5i/iWG9iynKt5T5oo17aN+sfkxhianikGKvxCSLnw7qyKrt+7jsmC60a5p4xctE67u7W/mN/8yqNx9vY+9sIRHL/TDgG2PMfmNMBfA5cC5wNjDB3mcCcE5iItYOdh8oj75TGqlfENlSc6ILgnnkooGAZbmv2LqP3782z3O/XGVvaQXrdoaWcX7v+43+5fdvHpJOkfw4DTvCxbp/u2oH174wi4feW+wfO+2vX3Chq9E0pH/+wKs+vUNhvo87TjuMjs0bJNyQ5LB2TRLuzOQOPKjtmeeJKPf5wIki0lJEGgCnYzXHbmOM2Qhgv3o+k4nIaBGZKSIzt26NbGXWBhZmmQV/0/DwtW52HyjnnCe+Chn/7t5T/PW3HWvltVl1qxnCT8dP44Q/fhoy3rut5WO/7JgutGyUIcvdUe6V3sp5937LwFgeFNq6JKjsQK7WpynMk4Ti3Jdv2ZtT0TY1Vu7GmEXAH4HJWP1SvwNifn4xxjxpjCkxxpQUF3vPkNcGLjumCwCN62VX4NH363YHrLsnAC/4p3cPULeFNXnhZs99aguVVYZXZ66Nu2JnsCJcs30/Hy3Y5P99zzkivWV+3USz3F+cbkWefLl8GxA+ZLLnXe+H/YzlW370t7KrKZlqfVeQ50tIuY945HOmLvU2NGesDG0KMmXRZsa8nr1PtglNqBpjnjbGDDLGnAjsAJYBm0WkHYD96h1MnSMMtWN3sy2CIbgetvs6X7q52rJz1+WorX06vZj47Rpue21ewMReNA54+KXP/PsXjP7PLH7YarV3a5zBdndOTXMvt8qNL80OmWfZtte7ZhCEr91/zhPTuOON7xMqY+1WsH+5YECN3ycW3r3pBP700/58cdswZq7eyVfLt/u3zVy1wzPSZdrybdz/zsK4rtmPF4UaO1dPmMnEb9d67J0dJBot09p+7QycB7wMvA1cYe9yBfBWIp+R7TgZc09+sSLDkkQm3Il8WI4WXbrrzfkAbIojBPTqCd/6lx2r15lge+Bdq6BVwwj+41QTznI3xvDuvOo5gRYNC3nl2zWs9Zg7cPC6kUH1ebL5x5pHgLnDRn9qlxJIFf06NOXCozqFNB2vqKzi/H9+zfHjPgk55mf/ns4zX61k6J9D3W/hiOc8yhYSjXN/XUQWAu8ANxhjdgLjgJEisgwYaa/nPJPmbfSciMsWnIv2lolzAsaL7OqGPzs6N1O2D8TRXWraD9VWX7js3GgT1akkXLRMeZAPfse+Mm5//XvGvb+YcIRzXzjF4soTcK048l0/tHuUPVNHtPIHAGt3xB6/7jyhe5GtGcOJumWGGGP6GGMGGGOm2GPbjTHDjTE97dec7mDr9i9+uCB7/NTHdW9Jk3r5freLc8H9b+6GgP2+W7sLgIMZDJVLJTUtoRCu8XmLhoWJiJMQ/jj3oH8pXL0ZLz+xQ3kUl0QiUSc77ZICG9Kc/OOc69v2lmKITeFe8cwMgJA6Sq9fd2zAeiT9na2tDzVDNUGceGKAByeF1qLOFAfKKxnQqRnv3TyEcwa2D3sCOnH68zfs9txeG/nKnlCE8BZqNG57fR4rtu6NvmMacTJUt+0LdJkEW+6xENYyt6ddEpkU3bHPUu6xtNhLJk54b8mDHwfMK0Xic3sCdW9QLPuAjoH1okorqti1v4wbXprtj0pycIfJZhOq3BPk+B4t/csmi4o67T1Y4e8Qn+fzUVllIsbix3ox1Ab+YSefALw1dwMrt+2L+z32l1bw0vTEokaSjZMxfN74wGinmkx+hrP2nRDYM//+Zdzv6VDPNnhS7W8P5hfHdfUvX/ivr8Pv6MHBoJtZfp6Pd248gc9uHQrAU1+s4F9TVzBp3kZemL6ah95f5N/35olzsy6gAlS5J4yI8P/OOMy/7p6tB1i1bV9aFf7sNTuZvWYnew6W08SO7Mj3We3ZHo2jAfas/zciVSKmnB8PBt7Ehv35M36I0wpft/MAM1Zll0fRKQIXTE0sd3eiU7Jx5jnSPT9xc4QaNm5aerjW3BPMTtDY4R2b0q6ZVYVy9fZ97C+1bnwrt+3jX58HBlAc/YePayJySlHlngQGdKp+hPv509P9y8u3/MjQP3/GPz9PXyTNeeOncd74aewrrfSH7fl8QkWVCdtOD6BVo8ATvmWjIs47ogMdmyeeEp5uvJ5QYolHbt6gOszxQHkl89Zll6sqXKbn+WHyFoK5YVj1BOeUxaERysmq3z9rtVXXKFKXpVQQ6+dt3xd4HezeXx4QGnpy7zb+5aL8PLpyNz8AACAASURBVNo0KeLCkk7+CpZeiX2Rwk4zhSr3JOALU3tlwy4rfOqPH6TOSgrH3tIKGhVZllO+T9i2t5RNe0LDub68fRgdmtVn0q9DU+rD9eysqjKs3h6/q6MmGGPY8mN8YWheF3nH5g089qzmh6172bm/POYqhZkgnPJatzO2ics8X+TL/YYXZ/uXj0igR4Fzvu9Jc0mOcN9PNPfauA8W+W9sd51+GH+/5IiA7fvLKrM6nj0cqtyTQLjkn0wnBa22+2Y6bopvVlS7GSZcNZjPfz+Ujs0b8NWYk2nTJLT5RJ5t8Qfz9JcrOenhz1i8KfUlF56btorBY6ewZNOP0Xe2CZ4Mg8CJ1f/OXEvXMZPYY38v367awfC/fA7AF8u2hRzr8E1QPfx0kx9FOUfjb1OWBax/GfS/fryo2po/UFZJ1zGTeHLqD3ixdPOPYROhHHq1bVxDSWtGuJ6///fOgoD1egU+TrMbc4P1pHGgzDo/BnRqRv2gCpqRCod1alH9ZLti617+8/WqiDJu3H2Al6avCfnuU4Eq9yQQPNPukO5iisGTOs7jcXD4I8BJhxbTpWXDkHE3VuefUOXuRBikI7HjyamWS+vUx6YyxSNL0Iv/ejw25/uErmMmMXHGGn/W6upt1s3vAldhrdEnHuLpkwVIULcmjC9BYyFY+X26pFqZvzoz0DJdbN9M/+Dhm9++t5RTHp1K///7yPNzWtm1d4ITi1JNuOqlwZE/B8ur6Ni8PtPvtG7WZ/Vvz8MfWv9nvPNjF5V08i+f/JfPufutBdz71vywxdluemkOd775fYD7NlWock8CbZuGFpK6683v+dlTqf8B3QQXL7suwSSSPBHPKIBdByz/YoPC1GdrbnTdQK6eMDOuY3u7LEfnBjfmje/94Z/7y0JvyreM6MnQXt71x1s0yFyMO4S3TGOlbdPAp7Pv1+/mYHkllz09ndviqP7puPfCxcIf1bU5PVuHL/ObSZZvsW5aT32x0v+0+pfJS/nOnl8piGOeoHFRPtee1J3OQTexCV+v5pA73wsYq6wyHCirZObq9DVWV+WeBLp71Kt+McjPl46ImVVBfvBLjrKyTmvaNUgEz1SQ+est5Xjhv75mrp0ElQw27DoQNaQsnkm/D245MeJ2L5dT/YI8Hjynn3+9pEt1r5n8vMxeLolkfPbv2JRnfnFUwNiMlTsYPPbjiK6omrBjXxkNMlimwYvgchLhGNQ5tLfQJYM7hYw9dXkJ3993Kvl5Pi4Nk91dWWX8n9v9zvc47J4P4hU7IVS5J4Hgx0GvR7KahKvFS9OgXq7OY3y9/JqFpAmRM/MArn9hVo3eO5iNuw9w3LhPeGRydQ1trxviv79YGfF9zn48MD57yYOjwu7rpdxFhPqFebx2rZWh+OyVlkKMlH6eLpo2qHnRsh6tG3Fom8a8c+MJAePRlJ0X7mikYL/7D1v3Mn3lDn/mc7bwtV1awok4ct/Ao/G/OdVuzQ7NLB+7+6mvXpiQz+53vke3O94LCc1NF6rcU4BXI4xESpHGyuV2KjVYvmOHgiCL86wBsZWtFZGoTxzx9mYNx+Y9VtblE59WT+C99/2mkP2iNVD4Lih8sSjCje2zJVvoOmaSf33Z2NP8yyVdW7Bq3Bk0rlfAkgdH8fQVR3m9RVrxisr6aEHod+Smaf0C7j6zD/efbSmz/Bp01g42Vq79T/UN/ZrnA11li8I0gck0TicqJxEsnm5a7mJxW+2CaptdkWdFUVw5XhUl04Eq9xTg5TpIh3J3WPzAKO48vTqx6pGLAsuujjmtd0zvIxLdck8WEzxK83653NtdsCqGjNNfntAt6j7PfhX4mcE3QYei/LyMRz6BvzJAANFcKkX5Pq4+oZvfYu3VJnoES3Cl0OBG6W5rP/jzi+3JVC9XRib5x2fLgWqL21HYVx0f/TxpWFRtIMy9dyTXDe3ONUOqjactUSpovvBNZjKdVbknifGXDvJPIpVVhGrEmhawqgnBj4nu3pTnHtHB/2gZDUECfO6zVu8MSeU/oUdy4sK/WVGd2bvFtorCNY0Y+ufPor7fXa6s4ViINbsxk3hZ7m/NXR8y9umtQ/2Fr4JvSj6fRC3zXL8gUC2EKw/shZPGf36aSw94sWrcGf7l2WssN5GTPetcIw1iaBx++6hqY6hBYT63j+od4IqdFFRuOZhZaZxEdZNoPfffiMgCEZkvIi+LSD0RaSEik0Vkmf0aOkORg5x+eDuW2e3NLnnqm5DtE2ekJgli295Suo6ZxGuz1tGvQxNO7h2+0zzAIxfG3jzBstxdtbn/MY1hQYq1OEnNot03CedCdBjVt23w7p64J2PdF98/Lh0UEDnjRZ/22V/X3ivSz8tn3q1VQ7q1sgyNc47oELL9zeuP83x/p6uY8x4OsZZN3rW/zK/IIrnDMolTzKyefQM75pCWkXYH4OhuLSJud891zb57ZNj9BnZqxie/O4mr7afKVPeyrbFyF5EOwK+BEmNMPyAPuBgYA0wxxvQEptjrdRZHqTStn5roAWfi6tb/fsf89XvYVxp5gixcLLDnvnhHy7hJRku1+et3B8Smf2j7kbu0tELMtu2NrXFEuIzI0w5vxwe3nBgy4ewm3anyNSGe365Fw0K+vWsEvz+lV8g2rwnAxQ+M8rcQNMAnvzvJXzNpawyNO7bvLWXg/ZP9iVLBiUDZguMydb4Dxzjp2rJB2CQ1J+R3xGFtPLf/9pRDAfylOl791bGe+z3zi6M4pLiR37ovq6ziymdn8OC7qakmm+gZnQ/UF5F8oAGwATgbmGBvnwCck+Bn1BqCa0AX5fv4s91mLDjGOFG27DnIf75eFRL7PT1CDe94EbGU929fmcuCoJLAjg830W7zny3ZElKB8HjbindqqjthdY9e5P3UsW7nfsorq9gVJd39qctLwm7LVN/PeIg3Ka64cVHMiU+WsrP2Nca6kTjdnR7/ZHnU4498MLBwVrgIknTx9o3Hh4yt3bHfH93jyNezdSN+N/JQXr322LDXaP3CPObcPZInLzvSc3tbO17eech1h8+6cYwLZwK2tKKKH7bui9l4iZcam5PGmPUi8mdgDXAA+MgY85GItDHGbLT32ei04qsLBKfwf/zbk/zhdvuT3Axj7HuLeMsj8zQcf7/kiLgvOBGr/MAbc9aHzBlcN7Q7Hy3YlLBS/MWz34aMOa4gn1gyPHLhAF6ftY5zBnbgN698B1ghdy0aFHLMQ1MorahiSM9WYS0rh8HdWjCkZyvPScjaoNzD1TBKFs7bO09rZ/Zvx9y1uzgqilvCi2gRJKli5UOnU2Wq5xquH9qd8XYJ6CF/+pRzbTeVU7HS5xNuimG+pXmEJi3BrRd9PqEgT7hkcGeet4uNNamX75fJ+W7KKqoorahMmQsrEbdMcywrvRvQHmgoIj+P4/jRIjJTRGZu3erdcby2ERxt0bRBgX/C5oVvVif1s8Ip9nCZgWcNaM/IPpGVXzBuVbJhd2BxqtKKqoS6zS/YsNufLRjM7+1syU4tGjCqb1taNSriVyd1R0T8CVmVVYYFG/b4bzpfLNvGvW9bNUT+evHAsJ8bLrqkpGv2Tw25f4+FG/b4a+O4KUwg0cr//vbN1fFH9whK0mvRsDCgsbpXpm+qb0ThEJGASeQ5QfM3b86xJqDrFSTv5tOqUSE3Duvhz4kAWDb2dO77SV//+kvXHONfdlyAZZVVlFVU+VtdJptE3nUEsNIYs9UYUw68ARwHbBaRdgD2a2htUcAY86QxpsQYU1JcnPkEkWQQnB7epF6B/0QLniRMFR9GycqMC9e/ExwS2bF5fQrzfTW2eM/425eMeGRqwFiBHYPtfI+rt+9nUlCXm7P6W37hRz5aGtZP/pMIcfxXHt81ZGz52NMCIoqyFbfCPP1vX/hbxDm8dM3RfHLrSTV+f8en7/zUjrES/Bs0a1BAt+KG/uYYZz/+Vch7ucMHM0n/jk09x2ua2OeFiHDrqb04NCjMVEQ46dBimtTLp69rwt6x1EvLKymtqErohhyJRGb51gDHiEgDLLfMcGAmsA+4Aqsx9hXAW4kKWVvwqkuRlwILJlJiUaLFpdyIS7sHFxC74MiOTJq3kWk/bGfnvrKIj62x0qJhIZv3lHL1kG7+/zE42cSpsf7Bgk1sDlMKONLE443DerBh1wFuOrkn/5uznhtP7pHxsgKxEvxvua3SV0Yfw9ExRH44zL57JKu37+PGl+b48x76tW/CeYM6cP3QHkD1zfbNOevp16Eplx7dmXoFeZSWV1GU7/NXfXSixAAeOKdfQNRNpgl3PSTzOonEhKsGh4w5Rsm8dbvZX1aZ8LxVOBLxuU8XkdeA2UAFMAd4EmgEvCoiV2PdAC5IhqC1gQJX2cAnfjYICLS25q3bRX+PcrTxMGHaKr/7IdW4lUlpUD9MEfG7Ba59YRavhIkQiAcnS/Vfn6/gGztd/ApX6zSAS4/pzKTvN1KY5wt55I6Flo2K+Ndl1sRqvw7eVl22EslOiEexg3UjbdGwkK/GnOwfy8/z8ciF1S4tt5vxgXcX8vQXK/js98NYv+sAM1ftZGdQ04vGRflZpdghc77/SOyye7De8spcIPZosHhJ6D83xtxrjOltjOlnjLnMGFNqjNlujBlujOlpv2ZXr7IU4k7tPqN/OyCwTOxPPB5fY2Xj7gPs2FfmqdgdP/vdZ/ap8ft74dYl7izFY21F4myPt0dpLD0/nTICwdE/x3SzPvuaE70zC50yrrmIeOaopo5g63bD7oP+qKk1O/YH9AcAGNk3vjmddHB1DJnK6Wbtzv0B67HE2teE7Lut1WK8SrImY2Kpsspw7EOfMOiByZ7b/3De4UC10k0We10x827feku7JZ8zjxAt/TrS+wJcM8S6AK89KbTqYb+g5CKfTyjM9/HtKu+sv3hqhtQ20l0BoYFHdJVT+75n60YhUTRjzzk8LXLFg9NqMpsoCPohpwX1XU4WqtyTiOPrLXBZ8O6Z+2G9iuk6ZhJ3vfl9zO/548FyugfVhg7mKLvIVbKzLJ0wLgisqx4t5HD3gfKQR3Y3B4NcPL87pRerxp3hWfOmb/tQ10lRvo8ZYeL540n0qW2k+39r3rCQ54N8xk5o79UndGP8pYMCttWGRLBsIHiOp6IqNT53/TWSzIPn9OOdm6rLqrqvR6dVXHCt90hs9uh7mmm8UtrdDLjvI44I85QBVuKSG3f8fXCj7hF9QtMkIrU9y2UyUbvsxEO9I9l8ItQLUubZUFzNi1XjzuC7e07xr3/yu5pHFCWD4MqcNwzrkZLPUeWeZH5+TBd6t622oN1umZqUx/1sSfgcgAX3ncp3954SdnsqcBfYuulk66R0Z+TFUi9jd4RM0uAs0oI4etsd1z01vstsIZzlnmiHpprQtVXDAGW+6P7wdfOzAXct/EM8muukk+DfK1U5AarcU0y+T2iWQJMF940imIZF+RHrpaQC9+c5IXPuzNxwZXrd9IjQgi340T5ayJq7qFO0aoe5SnCHpXQwuFuLgJtNttaScfPclUfxbAa+q2CClfnhKYraUuWeYkSEufd4W9eVVSZql5bKMDHt6fBvukuden1u/cI8fBKY5PLUFyuivm/whKqbeB/tn7qixF/zI5Za5bnIkJ7JKbuc6wzt1ZphUaqmpgOvcgWpILsaHdYRnIvxgXcX8ty0VSwfe1rYRJr563eHjH1wy5C0NGsu8OjaEzzmeGGWb9lLj9aNWLo5tKTAF8u2MnnhZn83IMdn/vp1x9KjdaBCbu+qNf/K6GOIxLQxJ9OkXgGn9G3Lm9cfx8BOieUQ1FbSPdH6p/P7+5dvH9Wb43vktjss2bRpUh3RFTxhnUzUcs8ATlne5+zuQ/siFBXzaqPWu20TWjdJbpXJSLjrcOSF8YGv2WHFujvlAcDqPrVlz0Eue3oGz3+92l9v3ZlQ7dOuaYhbqb5rcjVcYs5Rdh0Y943giM7NczpSJlu4qKQTF5ZUd1m6bmj3hBPz6hon927D/244npUPnR52wjoZqHJPE+679Q9b9wX07jxQVhm2pEBwT9B0cv6RHTm+R0vuObO6ANKpYRJVnGzRf39Z3cB64H0fMfgPU/zrb8y2arZ/vMhS7l7FmwryfLRrWo+x54ZvYPznCwbw0i+PjuM/UZKF3j+Tw8BOzVJujKhyTxPT7xzhXw6OFjnmoSn85aOl6RYpKs0aFPLiL4/xV2KE8Ekhf/eo+R38RPL71+Yx0dU6L9zJ/fUdw7n06PBp7F1aNuS4JLX3U2Jn9ImHeM7DKNmJKvcs4fFPozdEAKsue7qJdTb/drtUbyTGvBF7ApeSXdx5+mFJKRCnpAdV7mnkjz+NnJ79ZYRO9ucf2ZEvbhvGWRHK2aaKSI+Pd51e3Yj6lZmp6ROrZJbzoiStKdmJKvc0ctFRnSMWMrr/3cCiYG4//J8vGECnFg1SJlssDPcII2vZSC25TPJ/ZyW3WJwXj1w0kFXjzkj55yjJRUMh00ykwlZLN+/FGOO3lCtS3B09HhY/MCqk0xTg7zTl5opjuzDBVZfGi8ZFeuolg+CSyIrikEibvV4iMtf1t0dEbhGRFiIyWUSW2a/Z378sjWyMUoLAnRBUUZk9yr1eQZ5ngpFXaGTPGJKJ3rj+uKTIVdf49+UlfHbrUP+6hn8q4aixcjfGLDHGDDTGDASOBPYDbwJjgCnGmJ7AFHtdsbn11F7+5Q9vOZGF95/K9UOrS90ud3W1carFDQjTKiwbOMqj9+h7QW3ZwPpfn76ium5Mo3pqudeEEX3a0LVVQ84/smOmRVGynGT53IcDPxhjVmM1zZ5gj08AzknSZ+QEDW03xmn92tKrbWMaFOZzy4hD/dvdddMdyz1aFcZM0swjU3b6yh3+7NLebRvzx58eTq+2jRnuKhUcnIKtxMfD5/dXP7gSkWRdYRcDL9vLbYwxGwGMMRtFxLOYg4iMBkYDdO7cOUliZD8iwuy7R9LIpdzc9VrGf/YDt9mxxI7PPdt7fK4adwZz1uzk3PHTAPjwliH0aN2YlQ+dHtZt0LBQlXsiqDtGiUbCWkNECoGfAP+N5zhjzJPGmBJjTElxcepScLORFg0LYyr85bhlMlHSNV7ck62ONR9JAWVr7W9FyRWSYRKeBsw2xmy21zeLSDsA+3VL2CMVP3PvGRky5rhlaoNyP9Q1iVrfoz2bw6Rfn+Cv4qgoSupIhnK/hGqXDMDbwBX28hXAW0n4jJynWYNCju7WIkCRV7tlsl+5F+b7+PauEfzz54Mi+tP7tm/KKX3bplEyRambJKTcRaQBMBJ4wzU8DhgpIsvsbeMS+Yy6xGHtmgTEjVf63TLZ7XN3KG5cxKh+7TIthqIoJDihaozZD7QMGtuOFT2jxEmeT/xlcaG6wFhlFiUzKYpSO6gdJmEdIc8nAZ2X1uzYD0CT+hpZoihKfKhyzyKCLXfHHdM5wzVlFEWpfahyzyLyJFC5O8up6o6uKEruoso9i8jzCVWmuhqk02xaY8IVRYkXVe5ZhKPEHYt9wYY9gFruiqLEjyr3LMJR6pVB/VRrQ5y7oijZhSr3LOKvU5YBMGv1Ttbt3O8f11BIRVHiRWPsspCfPTU9YL1Ds/oZkkRRlNqKWu5ZxCl92oSMtWxYqBUAFUWJG1XuWcTPj+kSMnbDsB4ZkERRlNqOKvcs4oQerULGCmIoDawoihKMao4swucRz15aXpkBSRRFqe2ocs9yZq3emWkRFEWphahyz3LOHpi9/VMVRcleEq3n3kxEXhORxSKySESOFZEWIjJZRJbZr82TJWxdZPhhni1oFUVRIpKo5f5X4ANjTG9gALAIGANMMcb0BKbY60oNKcjy5tiKomQnNdYcItIEOBF4GsAYU2aM2QWcDUywd5sAnJOokHWV1687LtMiKIpSS0nELDwE2Ao8KyJzROTfItIQaGOM2Qhgv3r6FURktIjMFJGZW7duTUCM3OLqE7oBcOOwHhzZRT1aiqLUjESUez4wCPiHMeYIYB9xuGCMMU8aY0qMMSXFxcUJiJFbOHVkWjQszLAkiqLUZhJR7uuAdcYYpxDKa1jKfrOItAOwX7ckJmLd4ojOzQCrWbaiKEpNqXHhMGPMJhFZKyK9jDFLsJpiL7T/rgDG2a9vJUXSOsLZAztQ0rWFFgtTFCUhEq0KeRPwoogUAiuAK7GeBl4VkauBNcAFCX5GnUMVu6IoiZKQcjfGzAVKPDYNT+R9FUVRlMTQIGpFUZQcRJW7oihKDqLKXVEUJQdR5a4oipKDqHJXFEXJQcQYk2kZEJGtwOpMy+GiFbAt00JEQOVLDJUvMVS+xEimfF2MMZ4p/lmh3LMNEZlpjPEK8cwKVL7EUPkSQ+VLjHTJp24ZRVGUHESVu6IoSg6iyt2bJzMtQBRUvsRQ+RJD5UuMtMinPndFUZQcRC13RVGUHESVu6IoSg6iyl2JGxGRTMugpA79fRMjW76/Oq3cs+VH8EJEEq21n0oKMi1AJESklf2al2lZvBCRpq7lbDwHs1oviEiJiHj2Zs4SsuL6yOofMRWIyGEiciyAycLZZBE5VkSeAo7KtCzB2LL9F/iziPTJJuUpFg1E5GXs7l/GmMoMixWAiBwtIm8B/xaRq0SkKJvOQREZLCIvAA+JyOEiklX6QUT6isg04F6gWablCSbbro+s+vFSiYg0tZXmROABERkrIj0yLZcbEbkGK0xqNjAn0yeHG9tSehx4Dyt1+mbgKntbxq1PY7HfXm0lItcBZIuCEpH+wBNYvYb/C5wMZMX5JyI+EbkX+DfwPlYTnxuAARkVLJSbgTeNMWcZY5ZCdpx7kJ3XR1ac+Gni91ihnwOAXwEtga4ZlSiUzsBdxph/GGMOZpnlOQBYaox5FvgL8AZwtogcaowxmb7IRCTfbsi+GbgauE5EmhljqrJEwQ8Glhtj/gNMBuphtaEEMqukjDFVWLWdfmGMeREYC3QBssK4EJE8EWkBGCwFioicKyIdgfr2eqaVfNZdH9lw0qcMEekmIk5D0qeAewCMMT9gPdYdninZwC9fkb3cAugHzBCRk0XkQxG5U0TOs7en9eQQkUtE5D4R+Yk9NAcoEZHuxph9wLfATKwbZdpdXC75zrI/v8IYsxHoBqwCPgfG2PJWpVO2IPnOtofeAc4VkbHA90BH4G8icrstf7q/v5NE5GjX0ERgru0q2g78CLRLp0xu3PLZRs5+4ETgZNt19CvgQeAxe590f3/n2NfnGfbQXLLo+oAcVe4i0lVE3sd6zHxBRHoZY1YbYzaI1cwb4ADwQxbI95KIHGaM2QFsB14EzgHGAxuBe0RkQLpODtt3fS1wG5aSfFhEfgnsBZ7HetwE2AV8DDSwLea04CHfn0XkShFpKCJdgJXGmHVY1vH1wH9FpEhE0jLJ5SHfn0RktDFmM9Aba7LtTmPMMcBzwAlizwGlSb7GIvIG8CbwK9uoACg1xlQZY0rt76ojsCRdckWQrzmAMeYg8CyWa+tDY8wo4C6gn4iclkb5ikXkf8BvgR3AsyJyvjFmK/A6cJO9a0auDzc5o9yDLNtbgenGmOHAp1g+9r72NsfV0QFYax+b8u8hgnyfAA+KSDesiaLDgQ3GmLfsR7z3gLND3jBF2DeRY4Fx9uffAAzFanr+HtBDREbY1vB2rO9xd4blGwEMAXYC3UTkHeBhLOt9tTGm1BhTnkH5ThKR04wxK7H87Ovs3WcBW4DSdMhmU4Z1zv0c2ACc75Lb4TBgszFmqa1sB2dQvgtc28ZjuWGKAYwx64EvgXQ+mXUHvjLGnGiM+SfwO+A39raXgd4iMjxT14ebnFHuWD5MdwjhAgBjzONY/s6fiUhrY0ylWBOpO4wxc8SaeLtbRFI9+x5OvieAI4HRwFYsa/5813GtgWmpFExELrcfgx0rbhHQQUTyjTEfA/OBY2z5XgIes7/D4YAAhV7vm0b55gEnAIcC64EVwJHGmLOATiJyZBbIN1SsSbcPgXvtm/3FQF8sJZAO+ZoZY0qxzrGPgaVYroRD7f2cc7MFsF9EfoF17h2eSrdgrPIZY/ZiWcZXiMhA+9odgfWElDJs+YaKSAOsG/Lz9ngesND+A8vdNhH4azqvj3Bkcyx1TIjISKxH4CUiMtUY86qI7ACOEJGl9m7zsSaIWmJZSocAR4nIp8BB4BZjzK4MyrcAazK1szHmThHpLSLjsCzmDfb2ZMslQFssZV2F5aJqaF8wa7GeIHoAi7FO2EeBlsaYF0SkEzAGy81wTSq+uzjlexVrEuu/WL9lmeuthhtjkm45xSnfK1jfX3tjzL9E5CSqo1KuMsYkvVFNGPlGi8jNxpht9j5fY90QLwQeNMZU2IefClyC9URxqTFmXjbIB2BfP2KP9QUuM8Yk3X0URr5rgJuNMZtFJM82FA8DmtqyVQHP2TfxlF4fMWGMqbV/WBfPdCy3xRFYj0XXA42Bu4F3sR7bSrB+pF/bx12K5S8bkWXy/cY+rgnWiXFKiuTKs18PBV6wl/OxHnsnYPmFnwEuA5ra258DxrreozCF31tN5JsA3G8vC+DLQvkesJcLgLYZkO/vwOtB+55ry90DaGCPHQdclIXyNQQKnN84Q/K9EbTP88CF9nJb13uk7PqI9a/WWe6Of9xYd8mjgVnGmLfsbR9jW3DGmAdE5BBjzAp721dU+zYnGivkK1vl+9EYsxjL6kumbPnA/UCeiLyHdROptOWtEJEbsSZx+2DdbM7Bmlh7CMt68buHTKB1nA3yVWLdSDHW1ZX0CegkyPeNvW85sCkD8v0a2CAiJxljPrfH37Stzw+ARiIyzBiTEjdgMuQDhgGL7N844/JhBRqsFJH7gfNEZJQxZl0qro94qVU+dxG5Emsy6gF76HvgEhHpaq/nYz0+PWqvr7SPG40V+zwbUpe5mET5UnHinoTlL2wOLLdlLAeGORNm9g3pfuCPxvIVP4kVzTHdPu6zZMul8qVVPmPL93+u4y7Aijr5bK/SZgAAA5lJREFUFOhvjFmk8sUmn+1zvworMa0JMMxYkVrZQaYfHWL9w7pr/w8rFG820NsefwzL3fEV8AKWr3MS0MbefgtWzOlRdVy+IVj+SWd9PHAd8AuspwuwbvZtsXzXXe2xZkCHNPy+Kl/65HsV6OY6bojKF7d8XbAiZx4DBqVavhr9T5kWIM4foLP9Og54xV7Ow5rdP8Fe74TlHy6y1xuofNbnAEVU+wovBR6yl+cCN9nLJcDLGfhtVT6Vr7bINzHd8tXkr1a5ZYwxTrr2Y1jxzKcay8Wy2xjzpb3tWqxstgr7mP2h71T35DPG7DdWvLfjkhqJFdoIcCVwmIi8i/WUMTsdMql8Kl8tlW8WZEXJg8hk+u6SwJ32V8DnrvXBWNUA3yOFkQi1XT6sJwkfViheD3usB5b74ATS4EJQ+VQ+lS/1f7Wyh6qI+IxVEOo1rOiEUqykh2XGqhuTUbJZPtvaKMRKFHkTa0JoO9Zj555MygYqX6KofImR7fLFRabvLgncYRsAU7HKa/460/LUJvmwsk2rsGLsr860PCqfypdNf9kuX6x/tdJyBxCRW7FiiG83VspyVpHN8olVKvUy4JFskw1UvkRR+RIj2+WLldqs3H0mA6VcYyXb5VMUJbeptcpdURRFCU+tCoVUFEVRYkOVu6IoSg6iyl1RFCUHUeWuKIqSg6hyV+okIlIpInNFZIGIfCciv5Uo7RbF6n37s3TJqCiJoMpdqascMMYMNMb0xaojcjpWD9tIdAVUuSu1Ag2FVOokIrLXGNPItX4IVunlVljlXP+D1fkH4EZjzDQR+QarefRKrM5Kf8OqADoUq6LgE8aYf6Xtn1CUCKhyV+okwcrdHtuJ1d7wR6DKGHNQRHpilaAtEZGhwK3GmDPt/UcDrY0xD4pIEVbN/guMMSvT+s8oige1rs2eoqQQp4RrAfC4iAzEarN2aJj9TwH6i8j59npToCd2hy1FySSq3BUFv1umEtiC5XvfDAzAmpc6GO4wrGqBH6ZFSEWJA51QVeo8IlIM/BN43Fh+yqbARrs20GVYNb7Bctc0dh36IXCdiBTY73OoiDREUbIAtdyVukp9EZmL5YKpwJpAfcTeNh543W7O/Cmwzx6fB1SIyHdYrRL/ihVBM9uuA74VOCdd/4CiREInVBVFUXIQdcsoiqLkIKrcFUVRchBV7oqiKDmIKndFUZQcRJW7oihKDqLKXVEUJQdR5a4oipKD/H9+qb4UIZ76PQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot just the \"Price\" column from the dataframe:\n",
    "cad_jpy_df.Price.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** Do you see any patterns, long-term and/or short? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** The chart shows that over the past 30 years the CAD/JPY exchange rate has experienced many small trends when the exchage rate would move up or down consistently over several years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decomposition Using a Hodrick-Prescott Filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Using a Hodrick-Prescott Filter, decompose the exchange rate price into trend and noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Apply the Hodrick-Prescott Filter by decomposing the exchange rate price into two separate series:\n",
    "cadjpy_noise, cadjpy_trend = sm.tsa.filters.hpfilter(cad_jpy_df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a2c23168e0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cadjpy_trend.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a2c23e9bb0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cadjpy_noise.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Trend</th>\n",
       "      <th>Noise</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>126.37</td>\n",
       "      <td>125.850905</td>\n",
       "      <td>0.519095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>125.30</td>\n",
       "      <td>125.679684</td>\n",
       "      <td>-0.379684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>123.46</td>\n",
       "      <td>125.508788</td>\n",
       "      <td>-2.048788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>124.54</td>\n",
       "      <td>125.338304</td>\n",
       "      <td>-0.798304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-08</th>\n",
       "      <td>124.27</td>\n",
       "      <td>125.167037</td>\n",
       "      <td>-0.897037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-29</th>\n",
       "      <td>78.29</td>\n",
       "      <td>78.793961</td>\n",
       "      <td>-0.503961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>79.27</td>\n",
       "      <td>79.051061</td>\n",
       "      <td>0.218939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-02</th>\n",
       "      <td>80.40</td>\n",
       "      <td>79.311883</td>\n",
       "      <td>1.088117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-03</th>\n",
       "      <td>80.70</td>\n",
       "      <td>79.574499</td>\n",
       "      <td>1.125501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-04</th>\n",
       "      <td>80.71</td>\n",
       "      <td>79.837661</td>\n",
       "      <td>0.872339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7929 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price       Trend     Noise\n",
       "Date                                    \n",
       "1990-01-02  126.37  125.850905  0.519095\n",
       "1990-01-03  125.30  125.679684 -0.379684\n",
       "1990-01-04  123.46  125.508788 -2.048788\n",
       "1990-01-05  124.54  125.338304 -0.798304\n",
       "1990-01-08  124.27  125.167037 -0.897037\n",
       "...            ...         ...       ...\n",
       "2020-05-29   78.29   78.793961 -0.503961\n",
       "2020-06-01   79.27   79.051061  0.218939\n",
       "2020-06-02   80.40   79.311883  1.088117\n",
       "2020-06-03   80.70   79.574499  1.125501\n",
       "2020-06-04   80.71   79.837661  0.872339\n",
       "\n",
       "[7929 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe of just the exchange rate price, and add columns for \"noise\" and \"trend\" series from above:\n",
    "\n",
    "cadjpy_hp = cad_jpy_df.drop(['Open','High','Low'], axis=1)\n",
    "cadjpy_hp['Trend']=cadjpy_trend\n",
    "cadjpy_hp['Noise']=cadjpy_noise\n",
    "cadjpy_hp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a2c24a0ca0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Exchange Rate vs. the Trend for 2015 to the present\n",
    "cadjpy_hp_2015=cadjpy_hp.loc[\"2015-01-01\":, :]\n",
    "cadjpy_hp_2015.Price.plot()\n",
    "cadjpy_hp_2015.Trend.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** Do you see any patterns, long-term and/or short?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** The long-term trend in the CADJPY from Jan-2015 has been down. At the same time, there appears to exist a seasonal pattern where the JPY tends to strengthen vs CAD during the first half of each year. This is likely to do with the end of fiscal year which in Japan is at the end of March. The end of fiscal year tends to propmpt profit repatriation back to Japan and result in the strong deman for JPY against other currencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a2c25a8bb0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Settle Noise\n",
    "cadjpy_hp_2015.Noise.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting Returns using an ARMA Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using exchange rate *Returns*, estimate an ARMA model\n",
    "\n",
    "1. ARMA: Create an ARMA model and fit it to the returns data. Note: Set the AR and MA (\"p\" and \"q\") parameters to p=2 and q=1: order=(2, 1).\n",
    "2. Output the ARMA summary table and take note of the p-values of the lags. Based on the p-values, is the model a good fit (p < 0.05)?\n",
    "3. Plot the 5-day forecast of the forecasted returns (the results forecast from ARMA model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-29</th>\n",
       "      <td>0.076697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>1.251756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-02</th>\n",
       "      <td>1.425508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-03</th>\n",
       "      <td>0.373134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-04</th>\n",
       "      <td>0.012392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Price\n",
       "Date                \n",
       "2020-05-29  0.076697\n",
       "2020-06-01  1.251756\n",
       "2020-06-02  1.425508\n",
       "2020-06-03  0.373134\n",
       "2020-06-04  0.012392"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a series using \"Price\" percentage returns, drop any nan\"s, and check the results:\n",
    "# (Make sure to multiply the pct_change() results by 100)\n",
    "# In this case, you may have to replace inf, -inf values with np.nan\"s\n",
    "returns = (cad_jpy_df[[\"Price\"]].pct_change() * 100)\n",
    "returns = returns.replace(-np.inf, np.nan).dropna()\n",
    "returns.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a2c2627e20>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NBr61MOwyHUsY2lebzh5O2GNUsfHFoVWTMTM24YEJqwzTBN/y2VJc8Lo9L41QRDO6zY6oFQLwGYCNzPyWdZHMEyo73/HcgqD7w+WFqRvw+7YjWK6T3vXEGS8fZJAKeTKvAL9uDe1a+lCNg/3qj114fdZmfPpr5AOMo6dvAqD0MNo8NxM9R8+NygIDOafzMWt9oJLWLNHiCK+pvQAAb9eK+Vbf+cVf8PePlRm0sRwEO5lX4PPSjMW1Q92VySuzcOm7v+LD+dtw7IzSPoxirr/4fZepesLMePuXLdh5OPqRI54JQCbRXJon8gLHCABg5V/h+/uzT57FB/O2OmqRDjss8vMB3AJgABGtVv8us6HcoJgZcOn04mzP5+Nn7FPqeo/vhk9K8qX4JsDy5cEJq3DLZ6Hj2n9UJ3zkqtZwKN/yjLX7DbvuWkjaiImrAQBHcwuQ/uR0AErX0izefksjpXz31ytwLNfXMtcGJj9W45L1Tt137AyO5xaAmbFh3wm8NnOTp6Fc9t6vnuP2HlUaZqTGGwM4dDIP/1m43ach5hUU2TqOsScnFx2en43Wz860rUwzhHq/vTB1AzbuP4HXZ23GMS9D5+K3F+r68A+cCD3usSfnDN6duxU3fuKfMyhMTOjF1s/OxCPfrQ5dFCu94dkblLqfm19o2xjFY5PW4I3ZW7DKhA7aG6OxMTuiVn5jZmLmjszcWf2bbodwRsxaf8An4uOvI7kBA21H/Hzn4SySMGfDQZwIooyDvYlX7M4J6t/batIfq1mcWrsM5Ze/d/xK/P3jxcgrKArL8m03apbpY72tmq/+2G14XL5BfpYN+xXLWk+6XmPmodOLs3H1x4vx948X46MF23UjU4zcN2YpLCrG3V+vwOgZm7Bxf8nM3AcmrEKvMfM8z3ZPTq4li0uL0fYmmnHEGgQK6+WsseXgKd2xAm285/iZAqzfp98D1hTrfhOD3ZFw/EwBhn2xDM/+tA6AOVfQlDV78c6cEjfaI9+F50I7cDwPufn691HbXuBXP4uKGUt3+Lpw/OeXzNbpsdqB62Z25hcWByjlC16fjx6jfWPHn1EferhkHc3FP77KxAiTK7H4s3qPsbvnf2vM+yI1BV4SAWPuvNbPzsQ935ibgXnXV5mm5QmHULLO23QIgDLI6u9mWvXXsYAJKnoMHRtZsrFdR3KxSu1OF3oNAv+yoSR2edOBE+jz2nxL7iw9zPTErPLUj2vDejmHggh4+5ct6PTCbAx+77eA/bn5hciM0iDugDcW4Mips3j55w2YvzkbXy8xNh78ySsINAKyjpq3jnuMnosbP9EfvPcMEvttf3/eVlw3bkmAMvdm+NcropIa2nWKvOUzM/CJiQbmP6nFrCLUul+7dRIZecoy2D597X5MXmkcX/3DCvOx11sPKeszGlUaO/BWXpFy6myhj+UD+N5ro3GKomLG/d8qbiYj32owN4GR1R8Oy3fpK6A9OUqDX7pTXSkp6zimrw1ceu3giTxs3B8QoAUA+Pw33zq6XcdCdwvv6qw0tfvIaWw6cAJtn9N/aTAzcvMLscVCPqIdh0+j55h5mBRGuzHLur3HcX6ISLLVe4755Dc/eCIPhUXFnsgg/56v1ts+dLLEG6BXhb8PoiMixXWK3Ap2pWQ1eincN34l1u/Tb9gAsHBLdlhWwf/W7MPrOrNDj+cW2BBna52pa/ah/xsLAiylHqPnesL8Lnpbvzt7zlMl3rf+byyIinz3fL0CaSOnGe7POR0YTcQMr4aq/L/ig99088z0HD0Xl777K6au2YeH/+vrt/Vf/OHCN813609F4Bqxg3DCDfu+vgCD3vnVcH/6k9Mx8M2FuPjtRSgsKkZxMePrJbuD+qn1XE/RSgvx3tytPv7rTQdOoPsrcwLCmTUX7rHcfJz36ly8PG2jx91kpAdCvbz2hqEDzFJmFHlObj5aPD0Dn/0WyprXfBnBjjFvH1vJAXHKyyf98YLtnpwnA99eGHacbTCFFikPTFjlmWnpzyvqtP5DBvvNEKoXpY2LZO7KwaPfrQEzY8TEVZ7kUzMj9EeSl8V11M8nf83Hi/HW7M3o9/p8j6J/YMIqz+C0nm89nCgLABg9fWPog1zAPvX5MICf1+7Hsz+t07Xu44G/wh63aAcOnTyL+ZsO4aMFJVE86/edwO4jpz1++V82HESCqjUD6qf6/f15wTMvRqN3XWYUudbF/3KxryJ//n/rkTZyGtJGTsPi7cZT9s/kF+E3dUp/pkGX3G78JxBMWKZMpNGUZ9rIadjh0C77gRN5GPTOoqheQ7P8rxn7B35YmYWzhcWYsnof3pu71VTv68P5+tn9NIurqJjR5aVfPNuzjuYic/dRvDdvG3bpTCK59fNl6PPa/AD//QtT15v+TWkjp2F8kNQO0WTwe78FvJgjDRn1psXTMzzrjB49nY/rx/0RkC4ZUFwpZjEzi1gPzQr3Dzs8eKLkpfPazM1++85i1P+UZ1hYXOxJwhfs3miT3mK1/m2ZUeRGg4ZfLt7l+fzJoh2G5y/0ykb3aUirPjpMWPZXgGU9e8PBgHA/p7DpQPTztT/gNSjtHaY44M0FEZX3zdLdGPblcgCB7pFQvSAtY6H/4F+wCB+ncdOnviGE/krNKsXMWLIjxzPgrPHRgm34z0Lj9udPj9Fz8ZuJtUv9MepBejKk6ujmiV75irztg2CvuCd++NNwXzRUe5lZs1NT4KFekHr7zx8zD4nhzH2OIbPWH8CYGZviLUZUCNcY3OHlxtIGLMPluSnmrefSyBa/fCA/ei1gYQfe4bdr9hxDx0bVQEQRvTDMTKzT49xXjGeD79EZe/JexMPbJRPg7/dTEV/9sQvNddYPjYaR7iqLfG1W5KlLtYEUAgXkK9cgogCfKKB0x/SW43IC/pZNaWLVnti4sITY4V1fr/zwd0zTiQYyS24ECdgOHD9jaJUDof3b3tz99QoMHbsYy3flYOvBkwFpAJ6bsl43TXE0llJ0lUV+xQeBcaxm8Z5IEqyLfI3q3zQzo02ILjd+shQbXxwUbzGEKLIzxgtCFBTZO9S4fNfRoHMa9NdjtVUEAC6zyK2g+d+IjGOQtYkqQGRve8F+2jwX2ynuQmxZk3XcZ/wpHCJRiDkWZwbbgfjIbWB3jFJWCoIQmjkbD4a1PJ03kShELfoknohFLgiCoPJ/LooG8iYaIYmiyAVBEFyOKHJBEIQYIq4VQRAElxON8EO7lnobRESbiWgbEY20o0xBEITSiCMtciJKBPAhgEsBtAVwAxG1tVquIAhCaSQaK8TZYZF3B7CNmXcwcz6AiQCutKFcQRAEwQR2KPKGAPZ4fc9StwmCIAgxwA5FrufxCeg8ENFwIsokoszs7MhmcgmCILid5nUCE2lZxQ5FngWgsdf3RgACFqdk5nHMnMHMGampqTZcVhAEwX30aFbT9jLtUOTLAbQgonQiKg/gegD/s6FcQRCEUkeVikm2l2k51wozFxLR/QBmAUgE8Dkzxz+hgSAIQhnBlqRZzDwdwPSQBwqCIAi2IzM7BUEQXI4ockEQBJcjilwQhLjRv5VEsNmBqxR5eu2UeIsgCIKN1EypEG8RSgWuUuQ7D8d2fT+NLk2qx+W64dCpsfNltJumtZLjLYJgkScva41aKeVxTbdG8RYlKjSuWSkm13GVIo8XKeWdvyJeQjQWAnQ4/VvViVrZt/dKs3T+dRklc+Su6NTAojSll9qVK2DFsxehXtWKhsfUqeJOq33XmMGY92i/mFxLFLkJLmpbN94ihCQhGrkxTbBrzOC4XBcAKiYlGu67oGVkvteruyhpglIjVB4/3Nsz4J48OKA5urqgVxdPrju3seG+rk1qxFAS8wy/oFnIY2LVLkWRq3jf76u6+Ob8urVn0xhLEz6JLjfJ372+MxY93j+sc85LN57q3Kd57aDnvndDl4Btg9rVQ4Uka02icgVl1h57pRtqUbcK6leLTRc7XtRIDj1b0f95vX5NR8/nxjWN3WQVLT6TeBKrVuneOxQhtxkoZe835+OXtPLZZ3ax1CcGtQp9kE2kVqmARy5q6fnerakzrRazNKheCU38fN4/P9A76DnBHksw3+SdvdMxpFMDfHprhs/2BK/WEKkh1apeFQDAAwNaRFaAi9B6L2YsUwCoX83XfTI0w9cKb1W3ClrVreKz7f7+zfH8kHYWpDSmTwvlZf/G0E4B+8Zc3QHlQhhHbCKxeKw6ymVOkTesUdLAtYpVPTnJ580ZSd73XWMG475+za0JFwbLnroQA1orPuI29auGrHSxxN+t0ahGaGtUT/r2DasFPSdYtzVYG9N6XAP9XGZGL+yXrmyH7+7uGVQWf/wtTI6oVsWf85vX0t2+6aVBePPaTtg1ZjCeuqyNKWPnlp5pns96YYezHr4A//ay0gHgsUtaoXpy+fCEDoJ33Xz3+i7499874JpujbBrzGA8eWlrz77ruzfBnEf64uObuuLXJ/R7imYWiPC/L9EyuFyryFvXqxL6IC8a+FkDHRtVw9OD2wAAep1Ty+fNaeZNGy/uOD8d393d06eCEIByCcEf5c09mkRZshKG+kUgeCvcNaMujqjMb+86D5nPDPTZFsydREQei8vbP73j1ct8XhDe+xKIdBvnLT3T0K5BVd3rVExKwMLH++G7u3ti1kMXmPotbqFcAmH8P3ro7quYlGi6p6pRoVxJHX3xyva6x0TbHBl7c1fP55op5XHduSXtwv/npNVOwaUd6qNxzeSA3mFarWQklTNuc9Uq6buaru4anaUaXKvIP70tI/RBXjSqoVhI2sKnzCVvVCLyUTYO1uN47oq26K7jG04ubzzwBwDPXR5Z9zTc3MmNa1YKGIT01rdGFbxNfUVR3t1Xv5terVJSQCPv1rSGj8W4a8xgXOyxshkD29RVP3nJ4qf8J993Pt65rjMAXyVidoHc4mKgaa0UdE+v6XGrOJlwBoG1Hp8ZkhKN75e/0XVh6zpBfeJ289FNXdFWrV9JicYqL9gz9375T7qnJ+Y/1g/392+O4Rc0w+T7eqFl3ZJ2MqhdPcwY0Ue3nBu7R8egcq0iDxeta+tjeav/CeH5si7rUM82uUIx7cHehqGFNVKULmeXJtVj5osLRremNbDwscBuqJmXQUoFJcTzyUvb6O4nUIBzomJSoqHFCCi9LkDpcQVDqxt697lqxXLqMfpEI7QwmgPX4YTyGSm9r+7orrPtPMNyNMudSHnZfnb7uUGONSfbAwPMuzEv61AfPz/QG5teGhRckZu89rlpNUFESKlQDk9d1gZdm9TA7If7evb3bZWKBtX13Ynh9mLM4ipF/sO9ip/yzt7pYZ+rWdxaWFnflqk+LhRvi7xOVfviVt+/oYtnUHL2wxfgt3+Zj8xoWisZ7RpUw47R+iF+DatXwowRfTDqitDWdiwUfSJRgMULAANa1/UoxGjj/Tu7NKmBNaMuxq1evlk9BrSqizb1q+L+AS28emnmrvfIxS1DHxQm/7m5m+1latxxfjoqVzD3LLR70L6hr1tJz6o/J9WeWddme0J/7xreBKKEBAoarmoHY9Xn1teg1zPln+dH7drOn+niRbemNT0xuntyck2f16Z+Vbx1XWd8+usOXN6xAc5Lr4U6VSpg6p/KQkZEhESvlluhnH0PXLPYHrwwdBTDRW3r4pcNB8MqX3NJOAntBTmwTR0MOz8dvc6phSGdG+D02cKwy2pdrwo2HTgJAD7PKLQMyv9qlZJQrVISlj89ELVS9AfNqiUnGXaFjaiVUh5HTuejuoGryKm0bVAV6164BGkjp4U8VjNuOjSshnV7T0R8zXDGnMw+4kY1KqF25fI4fCo/QqnCZ/5j/VAc5LcMal8v6LyKaBpTrrLII2HUFW3x3d090LB6JYy6oh0SEwj1qlVEQkLJwFYw10ozHUsjmNXQ0KBLZYZPbg3P7+9NqC6b914jP3Wo8yLh/Oa1QUSoXKEc6hrM3pv7aF/d7QB8LOQaKeVxV5/gvTGjZ5NapYJub8EsmjJKKZ+IXWMGI/OZgdj+6mUel5DbeFMn5M4f7XaZqdPB6p/mWjPbEzBDucQEfHabsYsGUBTvkicvNF1mqDaUXjsF56Tav96mHVhS5ET0OhFtIqI/iehHInLU9LU29ati2M5OcswAABj3SURBVPnphksrefvNy6sj0P7RLVVMVL73dSaXxJpwuraRRo6EJEI9Gaxx3K/6QrXBsWszjGcAehPpeLWnThjsL/H3Uli+7LYO6Dl51+W/+0UWfXNnoI9b+6339D0HA9sEH/gMdideu6Yjvr6zO5rWim3Su/TaKahXzXjqvz+dGwcPd3UyVi3yXwC0Z+aOALYAeNK6SPbRNMTIuLdFrkUcfHWn70CObuXzq7XeA15muk9GoWxG8pmhX6s6eP6Ktob7ozXI4kOAvMGv+fntGSGTJV3RqQF2jRkcYM0Zvbi0n2k18sj/dlkNZDIzx+DKzko9isYq60DwsZ/eLQJnwmr1tFxiAj4NYf0GI7l8OfRpYS5axqiazhjRx/Zn4k+3pjXxzZ3n4b5+59hcsoJZ/38kWFLkzDybmTXH5xIAMUth5t1Qv73L15rQ3CE1DHyi/mUQET66sRvG3dINzesoCl2LL77FwvT8LwxG54NZckbd2InDe+Dd6zsHvV5rB1h9gDIlHQAuahvcihvQuq7urLpwyXxmoKcLrcUqBwlOCModvdNRPTnJMPQu0qZoxq0ztFtjbHppENJinK7Zf4YroEQghRNUYJedYFT/29SvijmP+LrgvH3vdl2/d4vaeGJQ69AHOgw7feR3AJhhY3lB8Z4p5/+mC/eZEpQBr4vbBYYVhl2W1wnlDSYMjL66g2eyij/e53T2Sk3bo1ktXNk5+GSCFlGy5H5+oLe5xGHqb0+vnYINL15i2g0SCd49jNqVK3i60KOuaKe6AiJLdNa6XlWsfu5iNKyu9ObMWpJ2wOCoRlYYWbCaNT7Iq/4P7lA/oBd3Z+90wzh/PepWrYB1L1wSlozVk8ubTsTmPdZzT9/oWNFuIaQiJ6I5RLRO5+9Kr2OeBlAIYHyQcoYTUSYRZWZnZ9sjfUiCd76C7Q3mivDeE0nYVbsG1fC1jk/Su+x3r++M1/ymK4eiVuUKhtOJ/X/N3X2bmS6/fcNquKF7aKXs/eJJLl8uKu6cUN3pGinlMfLS1igXqUmuUql8IhY81g9vXqv0GJw8Scwsberp99i0xzT2lm5B0/c+e3lb4zh/nWddLiHB1gFOf5qlVsake3pi00uDwjK4Fj3eHwse6xctseJCyLvMzAOD7Sei2wBcDuBCDhJnxMzjAIwDgIyMjKg2C7MKhL2d5P5laMdYKD8i1KLbNagWVevsyUvbIK+gCE98/6dtZT5xSeyShsVi/pOuiyOKF47my6JapSS8PjQ8w8AqdjcTveLOTdPPgGnU4wUQkJwtVjg2/JCIBgH4F4AhzGw+sNsGglX6NJMPqmRmZ+AdrqROefffc0P3xnju8pJBxWBKPdKc1r7ShXmWzmlT7+8dViX6+s7AmXvaPTKKxQZg2Qo2Q7PaKbi0fT28fV3w8QLbsVHJjr66Q7QvEcC5aTWRbGKBlK5qUqfW9Z2fbsAb//pt1OMtrVhteR8AqALgFyJaTURjbZDJFMEqfX+zOSKCzOJ789pO6oIAvtnKRl/dEalVKnhmafl3QjSFN/bmbmhZN/zG4OkJWGzV3oNGHRqFF1YVzC8cKiNhtCmXmICPb+4WNznsMKou71jfhlKCE5hx0lyFGtKpARaPHIBe5wTP524GuxdVCGY0lQbXlxWsRq00Z+bGzNxZ/bvHLsHCwfj5Bq9IwWKG61SpiEcubmUYbZBSIbjbI9zsjBpW3TaevCE2GcdDMwIDkabeHzxPeGkkpUIiKiYl4JnLjUM8zWI4ryFMbTT66g6YfF8vU8eGU7RRnpBwiYVrRVBw7cxOO1LNhpNX46bzzGUts6vyWv11AZE8EQp2V59mWoEewrXwSwPlEhOw6aVLoxqJUyPMvNuVkhJ9eozhzNiNBdFa5kyvWCckjYsnrlXkdmImUP+VqzqEtT5lpIrYan0M9X4b3CG8br3/C0Ar/vZeaWW+8dhFB9VNFO79bOo3FuRt3Lzgt6pO0AitKNm6VkptlpqCpy7zjefW7o+ZhUqcSDTbizsTRaCkYqb7RRZ8Mexc7Dt2Jqwy7CTeus0zgKsjSOYzA1HVq1sfTsUq8d0rV3h+SLuoLcFV1ohk9aB5j/ZFsyCpDYzy2sQUC41Bb/X5YC+cPi1S8eH87ZFf0OW41iKvoqZF7dqkhufxdk+rif6tvAc6Q8SRq7sj8SdrlqpRfmOrrh/LU8x1ttWuXEF3klK5BAqZZjYmU/xLGUmJhH+FmCU40mt5sXAsYz0lHqzKxGLVqx2vXub53LVJdYy5Onbhjj2a1fK5flnDtYq8TpWKmP5gH7xyVcmSUSWDl+YaRHV15e/UKuFbL81qp+D+/s0DMhaGo/D0ptx7coVEHH4Y/nlEwCKDiUQa2qxRo9mlTkynG2+2vnIZ7g2Rt8OOGYnaeq1VDQZRAfgsaRYtvAMDJt93vu5KVnZgVMWtZLd0O651rQBKbmVAz49rTpkp8cidcHnH8Fd5ISI8FmQCTOQ+cuW3WE/6FF6lDrXAbYPqlQzHCBY+3g81Q+S18eahgS2w6/Bpw/1zHukbdOmw0obVZ31f/+Z4b+5WDOncAB8vCHQvvPS39hjUPnarWkULuxKixYtoJs1ytSLXqJmiWCKtA6Ygh8jRTYSrutib58vqo7LqwYhHHQ83PelDA4OvqhOt7H/x5pnBbYKms/V+9gkEFJt8mOXVl56RQXpJiDw54jVzP6VCkTevUwU/3Nsz7pNVrOC/DmikVkeQrAMBBLMQZj98Ac4WFEcmhKDLP/qYTziVXL4cTplcUUlLJZBeW/8FWMcJA59CVCkVihxQcgm7lSGdGuA9dXEKzc9peQFei6dHMitViAyrroLBHeqj/r2V0LVJdTw2aQ2A0mlll7hWXOpbiSKlRpE7DTN1Te+YD27sgh9X7UXLupG6F8xX8kgHVIX48do1HZF11De8lojQTc2Rsujx/tidYzz+EG3+Nag1/sw6FpWy3R45JXHkbiKCh+X9gOtUrYi7LUQylE9UUgekVq6AHdnxa9CCNYyszq5NqgedXdqkVjKa1ErGur3HQ16DyP6Bw1BROnYg5kcgrg0/dD6hq5tmRV3d1b4B1ya1kvHaNR3x0U1dQx4bzVF0wTxabH+sDU63PX23yetPNCOxxCK3mXAeVeOayWFN+zeLZrG9ObQTft1qvIiHuFacwYc3dcW3S3f7RLTE0o3gdgXpFoItMm4VUeSlmL93axSwWroeYpnHl4bVK+HxS3xngMZiQI9U34rbXuduHeuM5stZXCtRwk2VzX1NWbADu3Lfxwqrs55LM6LIbcbtI+uCM9EWPTab6tZMNXSbYtTW/7xEZ5H0so4tipyIHiMiJiLry4qUEtzRNBTEteJ8nrikNZY/PRC1KltZPtCX23qmAVAWSXYDVSomIfOZgT5LLQoKln3kRNQYwEUA/rIujiAIeiQmkMU1YAN5enAbjLy0tfXJZzGkto0vstKEHa/itwE8AXcZoQJKLPHKIVLYCvZzXnpNPDO4jeH+O3qnR10GIorJgtlC9LHUgoloCIC9zLwmlG+YiIYDGA4ATZpEP6VmvHCPbaPELz9/RVv0bWVysWrBNv57d8+g+x+9uBXen7ctRtIIbifk65iI5hDROp2/KwE8DeA5Mxdi5nHMnMHMGampxqu028HANnVRuUI53NqzaVSvEwy3RALcfn56wCpLgiC4i5AWOTMP1NtORB0ApAPQrPFGAFYSUXdmPmCrlGFSt2pFrHvhkrhcW4JWBEGINRG7Vph5LQBPn5yIdgHIYObDNsjletwS0iWUTiQSqWwhIx02Iw1IcAJiSJQtbAtXYOY0u8oqDbjFRy4IgvsRi9xmxEcuCEKsEUUeJcQiFwQhVogiFwRBcDmiyG2m1zlKupmaKeaSGwmCIFhF5mbbzFOXtcZtvZqiXjVZuVyIH1r0VOt6soh2vPli2LlYmxV66T0riCK3mXKJCWhaS2ZKCoKg0L9VHfSPchoMca0IgiC4HFHkgiAILkcUuSA4lE6Nq8dbBMEliCIXBKeiTka4ukvDOAsiOB1R5ILgULQ5Zbf2Sgv73KREJWqlasUk+wQSHItErQg+yFJaziOSrA/N61TGM4PbYEjnBrbLIzgPUeSChx/u7YXGNSvFWwxBxUqaByLCP/o0s08YwdGIIhc8dGtaI94iCDpIIjbzDO5QH1lHc+MtRswRRS4IDkVyiofPhzd1jbcIcUEGOwXB4chiJUIoLCtyInqAiDYT0Xoies0OoQRBkFTIgnksuVaIqD+AKwF0ZOazRBTdhAKCUIbQFLn4yIVQWLXI7wUwhpnPAgAzH7IukiAIghAOVhV5SwB9iGgpES0konONDiSi4USUSUSZ2dnZFi8rCKWffq1SAUhsvxAa4hCOOCKaA6Cezq6nAbwCYB6AEQDOBfBfAM04RKEZGRmcmZkZkcCCUFYoKmZknzwrue0FD0S0gpkz/LeH9JEz88Aghd4LYLKquJcRUTGA2gDE5BYEiyQmkChxwRRWXSs/ARgAAETUEkB5AIetCiUIgiCYx+qEoM8BfE5E6wDkA7gtlFtFEARBsBdLipyZ8wHcbJMsgiAIQgTIzE5BEASXI4pcEATB5YgiFwRBcDkh48ijclGibAC7Y35hfWrD2ZE2Ip81RD5riHzWsFu+psyc6r8xLorcSRBRpl6AvVMQ+awh8llD5LNGrOQT14ogCILLEUUuCILgckSRA+PiLUAIRD5riHzWEPmsERP5yryPXBAEwe2IRS4IguByRJELgiC4HFHkQkiIZLGx0ow8X2s44f6VGUXuhJttBBFZzUIZbZLiLUAwiKi2+j8x3rLoQUTVvD47sR46Wg8QUYbD1wOOe/tw9AO0ChG1IaKeAODE9LpE1JOIPoGyupLjUOWbBOANImrrJEVJCslENAHAFABg5qI4i+UDEZ1HRFMAfEpEdxBRBSfVQyLqTkTfABhNRB2IyFH6gIjaEdFiAKMAVI+3PP44qX046sHZBRFVUxXkRAAvEdErRNQ83nJ5Q0R3QQlNWglglZOUJACoFtAHAKZDmWI8AsAd6r64W5WskKt+ra2uVgWnKCMi6gjgQwDfA5gEZQEWR9RBIkogolEAPgUwA0o6638C6BRXwQIZAeBHZr6CmbcAzqh7gPPahyMqfRR4HEpoZScAdwOoBSAtrhIF0gTA08z8MTPnOc2ahNKotzDzFwDeBDAZwJVE1JKZOd4NiojKEVF9AAcB3AngXiKqzszFDlHm3QFsY+avAfwCoCKAv7Sd8bx/zFwMJdfR7cw8Hsrau00BOMKYIKJEIqoJgKEoSxDRVUTUCEAl9Xu8Fbqj2ocTKrwtEFE6EVVSv34C4DkAYObtULplHeIlG+CRr4L6uSaA9lDWOR1ARLOI6CkiulrdH/NKSkQ3ENELRDRE3bQKQAYRncPMpwEsB5AJ5cUYc1eVl3xXqNcvZOb9ANIB7AKwEMBIVd7iWMrmJ9+V6qapAK4iolcArAXQCMB7RPQvVf5Y37++RHSe16aJAFar7p4jAE4CqB9Lmbzxlk81anIBXABggOr+uRvAywDeUY+J9f37m9pGB6ubVsNB7cP1ipyI0ohoBpRu4jdE1IqZdzPzPiIqrx52BsB2B8j3LRG1YeYcAEcAjAfwNwAfAdgP4Dki6hTLSqD6mu8B8AQUhfg6Ef0DwCkAX0HpMgLAMQBzACSrlnC85HuDiIYRUQoRNQWwk5mzoFi99wGYREQViCgmA1A68r1GRMOZ+SCA1lAGwp5i5h4AvgTQm9RxmxjJV4WIJgP4EcDdqhEBAGeZuZiZz6r3qhGAzbGSK4h8NQCAmfMAfAHFPTWLmQcBeBpAeyK6NIbypRLRTwAeAZAD4AsiuoaZswH8AOAB9dC4tA8NVypyP4v1MQBLmflCAPOh+MTbqfs0d0VDAHvUc6P+m4PINw/Ay0SUDmUApwOAfcw8Re2iTQdwZUCBUUR9afQEMEaV4Z8A+gG4UJWnORENVK3cI1Du5fE4yzcQQB8ARwGkE9FUAK9Dscp3M/NZZi6Io3x9iehSZt4JxS+epR6+AsAhAGdjIZtKPpR6dzOAfQCu8ZJbow2Ag8y8RVWs3eMo31CvfR9BcaWkAgAz7wXwG4BY9rjOAfA7M1/AzGMBPArgYXXfBACtiejCeLUPDVcqcij+Ru+wvfUAwMwfQPFN3khEdZi5iJRBzhxmXkXKgNizRBTtEXAj+T4E0A3AcADZUKz0a7zOqwNgcZRlAxHdqnZlNetsI4CGRFSOmecAWAeghyrjtwDeUe/jhQAIQHm9cmMo358AegNoCWAvgB0AujHzFQAaE1E3B8jXj5QBsVkARqkv9+sBtIPS4GMhX3VmPgulns0BsAWKO6ClepxWP2sCyCWi26HUvw7RdO+ZlY+ZT0GxeG8jos5q+x0IpecTNVT5+hFRMpSX71fq9kQAG9Q/QHGZTQTwbizbhx5Oj1/2gYgugtKF3UxEi5j5OyLKAdCFiLaoh62DMnBTC4r10wzAuUQ0H0AegIeY+Vgc5VsPZaCzCTM/RUStiWgMFCt4n7o/GrIRgHpQFHMxFFdTito49kDpHTQHsAlK5XwbQC1m/oaIGgMYCcVVcFc07l+Y8n0HZYBpEpTnme9V1IXMbLtFFKZ8/4Vy/xow83+IqC9KokPuYGbbF1UxkG84EY1g5sPqMX9AefldC+BlZi5UT78EwA1Qego3MfOfTpAPANQ2ROq2dgBuYWbbXUAG8t0FYAQzHySiRNUwbAOgmipbMYAv1Rd2VNtHSJjZFX9QGslSKK6HLlC6NfcBqALgWQA/Q+l2ZUB5GA+q590Exbc10GHyPayeVxVKBbg4irIlqv9bAvhG/VwOStf1/6D4cT8HcAuAaur+LwG84lVGeYfJ938AXlQ/E4AEB8r3kvo5CUC9OMj3PoAf/I69SpW7OYBkdVsvANc5UL4UAEnaM46TfJP9jvkKwLXq53peZUStfZj5c7RFrvmzWXnznQdgBTNPUffNgWqVMfNLRNSMmXeo+35HiR9yIishVk6V7yQzb4JiydktXzkALwJIJKLpUF4aRarMhUR0P5RB1rZQXi5/gzLoNRqKVeJx87Cv1esE+YqgvDjBSkuyfYDYBvmWqMcWADgQB/keBLCPiPoy80J1+4+qVTkTQGUi6s/MUXHn2SEfgP4ANqrPOO7yQQkC2ElELwK4mogGMXNWNNpHODjWR05Ew6AMEr2kbloL4AYiSlO/l4PS/Xlb/b5TPW84lLjilUD0ZvvZKF9UIlTU7vwKADUAbFPlLADQXxvMUl9ALwL4Nyu+3XFQoiqWquctiIZsIl/M5GNVvue9zhsKJfpjPoCOzLxR5DMnn+ojvwPKJK+qAPqzEjEVf+LZHQjS1akM4CcooW8rAbRWt78DxWXxO4BvoPglpwGoq+5/CEo857llWT71Wn2g+BO17x8BuBfA7VB6DoDyIq8Hxdecpm6rDqChyFeq5PsOQLrXeX1EvrDlawolguUdAF2jLV/YvyfeAgS50U3U/2MA/Ff9nAhlhL23+r0xFF9uBfV7ssjnkS8ZQAWU+PZuAjBa/bwawAPq5wwAE+LwfEU+kc8t8k2MtXzh/jnWtcLM2nTmd6DECl/CipvkODP/pu67B8oMsEL1nNzAksqsfLmsxFNrrqWLoIQTAsAwAG2I6GcoPYiVsZJL5BP5XCjfCsARaQGMifebxOTb824AC72+d4eS8W46ohgNUBrkg9JLSIAS/tZc3dYcigugN2LgBhD5RD6RL7p/jl+zk4gSWEmE9D2UCIGzUCYPbGUlj0pccYF82gSFT6FMg74DyoSUB5j5RDxlA0Q+q4h81nC6fKaJ95vE5FszGcAiKOkiH4y3PC6UrweUcMLfANwZb3lEPpHPSX9Ol8/Mn+MtcgAgosegxOf+i5UpvY7CBfI1gjJZ5S2RL3xEPmuIfNHHLYo8geOQmtQsTpdPEITSjSsUuSAIgmCMY8MPBUEQBHOIIhcEQXA5osgFQRBcjihyodRDREVEtJqI1hPRGiJ6hEKsFEXKEn03xkpGQbCCKHKhLHCGmTszczsoU7Evg7LUXjDSAIgiF1yBRK0IpR4iOsXMlb2+N4OShbI2lKx2X0NZxAAA7mfmxUS0BMpaljuhLBLxHpQEaf2gJFv6kJn/E7MfIQhBEEUulHr8Fbm67SiUlZlOAihm5jwiagElE18GEfUD8BgzX64ePxxAHWZ+mYgqQElVPJSVBZYFIa44eoUgQYgiWia7JAAfEFFnKKvDtDQ4/mIAHYlIWyy7GoAWUBcMEYR4IopcKHOorpUiKItzjwJwEEAnKGNGeUanQUmkNCsmQgpCGMhgp1CmIKJUAGMBfMCKX7EagP1qioVboKQ1BRSXSxWvU2cBuJeIktRyWhJRCgTBAYhFLpQFKhHRaihulEIog5tvqfs+AvCDulbkfACn1e1/AigkojVQVnl6F0oky0o19Wk2lMWWBSHuyGCnIAiCyxHXiiAIgssRRS4IguByRJELgiC4HFHkgiAILkcUuSAIgssRRS4IguByRJELgiC4HFHkgiAILuf/AYZEE0n85SnXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "returns.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autocorrelation and Partial Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "plot_acf(returns, lags=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pacf(returns, zero=False, lags=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.arima_model import ARMA\n",
    "# Estimate and ARMA model using statsmodels (use order=(2, 1))\n",
    "model = ARMA(returns.values, order=(2, 1))\n",
    "\n",
    "# Fit the model and assign it to a variable called results\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>y</td>        <th>  No. Observations:  </th>   <td>7928</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARMA(2, 1)</td>    <th>  Log Likelihood     </th> <td>-9751.978</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>0.828</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Sun, 29 Nov 2020</td> <th>  AIC                </th> <td>19513.955</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>23:10:01</td>     <th>  BIC                </th> <td>19548.846</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>0</td>        <th>  HQIC               </th> <td>19525.903</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>   <td>   -0.0022</td> <td>    0.009</td> <td>   -0.245</td> <td> 0.807</td> <td>   -0.020</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.y</th> <td>   -0.8547</td> <td>    0.164</td> <td>   -5.200</td> <td> 0.000</td> <td>   -1.177</td> <td>   -0.533</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.y</th> <td>   -0.0197</td> <td>    0.013</td> <td>   -1.475</td> <td> 0.140</td> <td>   -0.046</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.y</th> <td>    0.8246</td> <td>    0.164</td> <td>    5.030</td> <td> 0.000</td> <td>    0.503</td> <td>    1.146</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>          -1.2033</td> <td>          +0.0000j</td> <td>           1.2033</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>         -42.1702</td> <td>          +0.0000j</td> <td>          42.1702</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>          -1.2127</td> <td>          +0.0000j</td> <td>           1.2127</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                 7928\n",
       "Model:                     ARMA(2, 1)   Log Likelihood               -9751.978\n",
       "Method:                       css-mle   S.D. of innovations              0.828\n",
       "Date:                Sun, 29 Nov 2020   AIC                          19513.955\n",
       "Time:                        23:10:01   BIC                          19548.846\n",
       "Sample:                             0   HQIC                         19525.903\n",
       "                                                                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0022      0.009     -0.245      0.807      -0.020       0.016\n",
       "ar.L1.y       -0.8547      0.164     -5.200      0.000      -1.177      -0.533\n",
       "ar.L2.y       -0.0197      0.013     -1.475      0.140      -0.046       0.006\n",
       "ma.L1.y        0.8246      0.164      5.030      0.000       0.503       1.146\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1           -1.2033           +0.0000j            1.2033            0.5000\n",
       "AR.2          -42.1702           +0.0000j           42.1702            0.5000\n",
       "MA.1           -1.2127           +0.0000j            1.2127            0.5000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output model summary results:\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a2c3f7fe80>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 5 Day Returns Forecast\n",
    "pd.DataFrame(results.forecast(steps=5)[0]).plot(title=\"CAD/JPY 5-Day Daily Return Forecast with ARMA 2.1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** Based on the p-value, is the model a good fit?\n",
    "\n",
    "**Answer:** Out the two auto-regressive lags used in the model, only the first lag has the p value < 0.005 and therefore is statistically significant. It would therefore be appropriate to re-run the model as AR(1) process (ARMA (1,1)) which may produce a better fit.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting the Exchange Rate Price using an ARIMA Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 1. Using the *raw* CAD/JPY exchange rate price, estimate an ARIMA model.\n",
    "     1. Set P=5, D=1, and Q=1 in the model (e.g., ARIMA(df, order=(5,1,1))\n",
    "     2. P= # of Auto-Regressive Lags, D= # of Differences (this is usually =1), Q= # of Moving Average Lags\n",
    " 2. Output the ARIMA summary table and take note of the p-values of the lags. Based on the p-values, is the model a good fit (p < 0.05)?\n",
    " 3. Plot a 5 day forecast for the Exchange Rate Price. What does the model forecast predict will happen to the Japanese Yen in the near term?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autocorrelation and Partial Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s4Z8PbI2IbRFRAW4HLm+cISL+OSL25YP3AqsKWK+Zmc1AEYF/BrC9YXhHPq6da4Bvt5so6VpJGyVtHB4eLqB5ZmYGxQS+WoyLljNKv0QW+De0+7CIWB8RQxExtHLlygKaZ2ZmAIsK+IwdwOqG4VXAzuaZJL0G+DxwaUTsLWC9ZmZ9IwLGaglpCkkESXr0VUlSCNix7wgRkEaQ5n9HqgkEPLLrAGkEEfDKU5Zz3OLBwttYRODfB5wt6SzgGeAK4B2NM0g6E/gmcFVEPFbAOs3MOhIR1NKJwRzAc4fGSNMgiaCWBGke3iPVhAjYsvMF0hRqaZpPgwMjVQDuf2p/y3WNVhIAtj8/csy0ai0FYP+RakPbCv6yuY4DPyJqkq4H7gQGgVsiYouk6/LpNwMfBV4K/IUkgFpEDHW6bjMrnyAL65FKkoVuHr5JBJVaSgBP7T18NMgb9rYPjdWIgB9s20vaFKqHx2oAPL77UMv11oP5wEhtDr/d3CpiD5+I2ABsaBp3c8P79wLvLWJdZjY/penEPeoA9h4aGw/k+t50YxdHfd5aenRP+2C+N715+7F706PVbE965/7Rtm0Ajgn7sigk8M1sYWsM6/pedTXJ9nh37h+ZEMxJGhyu1CDgR0/vGx/f2E1R35t+rMXedKsuDiuGA9+sBOpBPR7KSfa3sQtkPNCTqbs/AEbyfumn9h45dn1JtsBoNZ3Lr2Uz5MA3myeqycTAruYBPlbLrgDZNnyIWkNg19KUg6M1guCHTzzf8jMn6wIpe/fHQuTAN+uiWpJmoTy+l50Nj9VSIoKfDh8a38uuTzs4WiMi2PjkvpafOZaH9u4DY8dMi7m63MPmJQe+2Sw0Bnf9fb175MnnDo+HdS3vOqnvad83RWjvcWjbHHLgW6lFBNW8C6SaptSSIAie2T9yNNSToJqk433a927b2/I66Xr3yK4Xju0ecWhbP3Dg24JSS9LxXyvuO1yhmqZUk2wvvJoERyoJEcGPnt43HuaNjlSyq0eebnEist6n7ey2+cqBb31tvLskScf3tOv93Y/vPpiFeUOopwGHRrPQ/smzB1t+HvjqESsnB751Vb0LpX4vkeGDY1TzMK8k6fj7yfq86/3dzx2qdLv5ZvOaA98KkeQnLeuhXamlRMDWPYeo5uOqebdKNOyFb93T+mfs7vM2K54D3yYVkf3Aph7mlSSlWsvC+/BYjQB++MTzJE0Xa9dPYA4fPPaqEzPrDQd+iVVq2Q93IuDZF0bzUE+o1LLulfr1363uWQKMh3xz2JtZf3LgL1C1/ORm/YZUT+89QiVJGK0e3UtP4+g9TZ547vAxn+FuFbOFxYE/T1WTlNFqwljt6FUrP3n2AGPVbLi+112/zPCZ/cfeh9vMysWB36fS/Of2o9Ws/zxtE+hw9KqVfYd9d0Eza8+B30MRwWg1ZSTfU08j2LLzBUar2UnReo/KqAPdzArgwO+CWn6XwzSCp/YeZqSaMFJJ8q6YbJ76Xvp8fpqOmfU3B37BkjTYc3CUkUrCkfxVqaXjJ0fbPYnHzGyuOfBnqf5Un8NjNQ6PJRweq3FgtAoBP91z7BUvZma95sCfppFKQiXvmtm8fT+j+RPsJ/BVjGbWxxz4bRweq3FwNNtrPzhapVILRvNHutUf7WZmNp848Mmuljk0VuPAaI0jlYQkDR7c8UKvm2VmVqhSBn4A+49UxvfgD43Wxp/bWb99rpnZQlOKwK8lKS+MVDk4mp1kTSJ4ZNex90o3M1vIBor4EEmXSHpU0lZJN7aYLkmfzqc/KOl1Rax3uvYcHOOx3YfY9cJo9gtVn1w1sxLqOPAlDQKfBS4F1gJXSlrbNNulwNn561rgc52u18zMZqaIPfzzga0RsS0iKsDtwOVN81wO3BaZe4EVkk4rYN1mZjZN6vQWuJJ+E7gkIt6bD18FXBAR1zfM8y3gkxHx/Xz4LuCGiNg42Wef9PJz4+IP3TLjNm1+YDMA685bB2T3fR+tZZdSPv7wQwCcvfbVLZedbLqX7c6y/douL+tlu7Xs8qWLGJBaftZUvn7dv9kUEUOtphUR+P8OeEtT4J8fEe9vmOd/An/cFPi/GxGbWnzetWTdPiw/7RU/d9nHvtRR+2Bi4JuZ9bu5CvwirtLZAaxuGF4F7JzFPABExHpgPcDQ0FB87T++vuMG7tw/wlN7j3T8OWZm3bBu9QqOXzI4q2W/fl37aUX04d8HnC3pLElLgCuAO5rmuQN4V361zs8DL0TErgLWbWZm09TxHn5E1CRdD9wJDAK3RMQWSdfl028GNgCXAVuBI8B7Ol3vTJx0whIADo7WODhapZr4ukwzK59CfngVERvIQr1x3M0N7wN4XxHrmo3jFg9y+orjx4dHKgkHR6scyAvAaNW/rjWzha8Uv7RtdvySQY5fMsgpJ2bDY7Uk3/vPCsCRSos7YZqZzXOlDPxmSxcNsnT5ICcvXwpkt2I4eqfMGofGai4AZjbvOfBbWDQ4wEtOWMJL8r7/JA0O5uF/YLTK4bFkwkPEzczmAwf+NAwOiBXLlrBi2ZLxcSOVZPyJV4fGstsq13wy2Mz6mAN/lurnAerdQACj1exRh0cqCSPV7Hm2LZ+MZWbWAw78Ah23eJDjFg/y0oZxaRqM1rLwH8kLwWg1e+9eITPrJgf+HBsYEMuWLGLZkmM39Wge/qPVdLwQjFYTxmqpjwrMrHAO/B6qHxE0iwjGaul4MRirTfzrE8ZmNhsO/D4kqW0xAKgmKWO1lLH8aCB7JYxVUypJ6pPHZtaSA38eWjw4wOLBAZYvbf2fr5YXhEpDMai/ryTZeHcZmZWPA38BWjQ4wKLBAU5Y2np6RFBNYrwQVJKUai2oJMl4oajUUp9UNltgHPglJIkli8SSRZPfLLWWHD0iqCQp1SSo1FKqjeNcGMzmDQe+tVU/Umj4vVlL1SQrAtlRQnp0OEmp1KJh2JXBrJcc+Nax+jkFpigMETF+pFCtpVTThvf18XlxqKXh8wxmBXPgW9dIym5Utwhoc36hrn6eoZZmRw5ZcciuQKpfiVQvDFVfmWQ2LQ5860vj5xmY+sgBWheIxqJQy48gavlRRS3xuQcrHwe+LQgzLRCQ3QW1VUGoJdGySCRp+EdvNq858K20BgfE4MDMHhSdptnRQ1YssqKQpEE1LxBZoTi2cLhOWD9w4JvNwMCAWDrDIgHHFoqkoUDUjzSSNCYUjPqwT15bURz4Zl0w20IBTCgMzcWguVgc/ZsdYfjIwho58M36XPZ7iNktm9aPGtK8aCSNhWFioUhcMBY8B77ZAjYwIJYM5CezZyFtLAhRLxjHFormItI43l1S/cOBb2Zt1QtGJ9KmgpCmHC0MEfmRRLsjDV8dVSQHvpnNqYlFY3Z9UxENRxlpi1dT4UibC0YcHVfmIw4Hvpn1PUksGlQhgVUP/jSOHkmk7YpJTCweadM88+3Ao6PtJ+kk4GvAGuBJ4O0Rsa9pntXAbcDLgBRYHxF/3sl6zcxmq4huqrqI9sUgKxaMn/tIo/7i6Ps0ex+N4wJUTPOO0WnBvBG4KyI+KenGfPiGpnlqwO9ExP2SXgRskvS9iHi4w3WbmfWUJBYPijYPp+s7szt1f9TlwK35+1uBX2+eISJ2RcT9+fuDwCPAGR2u18zMZqjTwD81InZBFuzAKZPNLGkN8FrgB5PMc62kjZI2Dg8Pd9g8MzOrm7JLR9I/kfW/N/vwTFYkaTnwDeCDEXGg3XwRsR5YDzA0NDTPTomYmfWvKQM/In6l3TRJuyWdFhG7JJ0G7Gkz32KysP+biPjmrFtrZmaz1mmXzh3A1fn7q4F/aJ5BkoAvAI9ExJ91uD4zM5ulTgP/k8DFkh4HLs6HkXS6pA35PG8ArgJ+WdLm/HVZh+s1M7MZ6uiyzIjYC1zUYvxO4LL8/feBObqq1MzMpqvTPXwzM5snHPhmZiWh6OM7CUkaBp6a5eInA88V2Jwi9GObwO2aiX5sE/Rnu/qxTdCf7SqyTS+PiJWtJvR14HdC0saIGOp1Oxr1Y5vA7ZqJfmwT9Ge7+rFN0J/t6lab3KVjZlYSDnwzs5JYyIG/vtcNaKEf2wRu10z0Y5ugP9vVj22C/mxXV9q0YPvwzcxsooW8h29mZg0c+GZmJbHgAl/SJZIelbQ1fwpXX5D0pKQf5/cS2tjDdtwiaY+khxrGnSTpe5Iez/++pA/a9HFJz/Tq/kuSVkv635IekbRF0gfy8b3eVu3a1evtdZykH0p6IG/XJ/LxPdtek7Spp9sqb8OgpB9J+lY+3JXttKD68CUNAo+R3chtB3AfcGU/PE5R0pPAUET09Acfkn4BOATcFhGvzsf9F+D5hkdVviQimh9V2e02fRw4FBH/tVvtaGrTacBpjY/mJHui27vp7bZq166309vtJeCEiDiU3w79+8AHgLfRo+01SZsuoYfbKm/bfwKGgBMj4q3d+je40Pbwzwe2RsS2iKgAt5M9htFyEXEP8HzT6CkfVdmDNvXUJI/m7PW26stHhkbmUD64OH8FPdxek7SppyStAn4V+HzD6K5sp4UW+GcA2xuGd9AH/xhyAXxX0iZJ1/a6MU1m9KjKLrpe0oN5l09Xu04aNT2as2+2lY59ZGhPt1feTbGZ7EFI34uInm+vNm2C3m6rTwG/C6QN47qynRZa4Le6DXPPK3ruDRHxOuBS4H15N4a19zngFcA6YBdwUy8aoWk+mrPbWrSr59srIpKIWAesAs6X9Oput2GaberZtpL0VmBPRGzq1jobLbTA3wGsbhheBezsUVsmyJ8RQETsAf4HWfdTv9id9w3X+4hbPqqymyJid/6PNQX+ih5sL7V+NGfPt1WrdvXD9qqLiP3A3WR95T3fXs1t6vG2egPwa/k5vdvJHgz1Zbq0nRZa4N8HnC3pLElLgCvIHsPYU5JOyE+wIekE4M3AQ5Mv1VVTPqqy2+r/8+d+gy5vr/yEX6tHc/Z0W7VrVx9sr5WSVuTvjwd+BfgJPdxe7drUy20VEb8XEasiYg1ZPv2viPj3dGs7RcSCepE9aesx4KfAh3vdnrxNPwM8kL+29LJdwFfJDmOrZEdE1wAvBe4CHs//ntQHbfoS8GPgwfwfw2ldbtMbyboDHwQ256/L+mBbtWtXr7fXa4Af5et/CPhoPr5n22uSNvV0WzW070LgW93cTgvqskwzM2tvoXXpmJlZGw58M7OScOCbmZWEA9/MrCQc+GZmJeHANzMrCQe+mVlJ/H9h0duhCyTx9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(cad_jpy_df.Price, lags=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JWyR1SLqxzPLfk/RMdvtnSec3vlQzM6smN9AltQJ3AkuBBcDlkhaUrPYL4IMR8W7gz4FVjS7UzMyqq+UK/UKgIyK2RkQ38CCwrHiFiPjniHg9m/wpMKuxZZqZWZ5aAn0msKNoujObV8m1wCPlFkhaLmm9pPVdXV21V2lmZrlqCXSVmRdlV5R+g0Kg31BueUSsioj2iGifOnVq7VWamVmuthrW6QRmF03PAnaWriTp3cA3gKURsbcx5ZmZWa1quUJfB8yVdKakscBlwOriFSS9E3gIuCIiXmh8mWZmlif3Cj0ijkpaATwGtAJ3R8QmSddny1cCNwOTga9JAjgaEe1DV7aZmZWqZciFiFgDrCmZt7Lo/nXAdY0tzczMBsLfFDUzS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLR1uwChktPb7B2y2427dzPuTNOZvG8abS2qNllmZk1zNsi0Ht6gyvu+hkbd+zjUHcP48e2snD2JO67dpFD3UYsX4TYQL0tAn3tlt1s3LGPg909ABzs7mHjjn2s3bKbi+ZPb2ptQ/mkTS0QUjsfqHxOvgixwXhbBPqmnfs5lIV5n0PdPTy/c39TA30on7T17nukhWeKAVftnEbyRUizjLQ+ORIlEej/7+d7j93ff+jIcfNaJca2tXD4aO+xeWPbWmiRjq3X2xts3LGPbXvfZM7kk1g4exItQ9xZntz+Ohu2v36sroPdPWzY/jor1/6cC844tWn77u0NvvTIZjp2H6D7aC9j21o4Z9oEblo6v+42GWw719tWzXh881Q7p2173yx7EfLoc69y4tjR+7Qd7OMwlH2yGd579uQh2W9NPUPSEuCrQCvwjYj4cslyZcsvAQ4CV0fEkw2uddAWzp7EOdMmsOmlPdDaxrgxbZwzbQILZ08Chr6zVOrE2/a+SXfRiwxA99Fetu19s+5Ar2ffG3fso2P3gWNBc/hoLx27D7Bxx7666qqnnes5n5EaBtXOac7kk8pehMyZfNKQ11XPi1+1bet5HIaqTw614b6QyA10Sa3AncDFQCewTtLqiHi+aLWlwNzstgj4evZzRGhpETctnc/vf+Zz9EyYzorrl/dr2LzOUm8Hr9SJh/JJW8++632hqdRetTwpK21by/nUc9x65IVYpWXVzinvIqSemvK2G2zo5m1bz+MwlBc/Q6VaewyVWq7QLwQ6ImIrgKQHgWVAcaAvA+6NiAB+KmmSpNMj4pWGVzxILS1i7N4O2NvBBWfc0G9Ztc6ycPak3A5e7clTrRPX8qQd7BOznkCo58WgWifOe1JW27ae37KG6gUq77hA1b5T7ZzyLkKq1VVLKA/Fi27etvU8DvVe/Az2Rbce1drjfXOn1L3/clTI4CorSB8HlkTEddn0FcCiiFhRtM7DwJcj4ifZ9OPADRGxvtJ+Tztjflx8090DLnjj0xsBWHj+wmPz9r915Nj9F59/DoC5C847bttKy9546ygv7ztEcVNIMHPSeICKyyae0EZE8NJrhzh0pIeIwrLxY1p552njkUTXG4fZc6D7uFqmThjLlInjiAhe6NgKrWOZMeN0JoxrpTCCRe6+81Tbd952L712iIOHjwBCLep33IjgwOEe3jrSwwljWvvtt562rLZtX1tXOp96jtt3zuXOKe8xaMRxqz1GlfpstboOHO4ZdJ/dc6A7t78Odtu8x7eaevpktZqB3OdYtX1XW17tuX/W1AlVz7ea713/axsior3cslqu0MslQOmrQC3rIGk5sBxgwuln13Do4xUHeTnlgjxv2YRxrYwf03rcgzphXCt7DnT364AAEXD4SA8TT2jjwOGeY9v1LTt0pIcDhwvLTxjTisRxnXjcmNbsvpg3t3xb5O07Lwyq7Rsqh4WkLBjGcvhID+PKBFylJ9ZbRfWWttfkCWMrtjNQdduJJ7RVPZ96jlvtnPIeg2rHDah6Pn1tXe0xqtRnq9WV147Vts3rr/VsW+151vc4VOrP9fTJajUDuc+xvBeSSsvz2mMo1BLoncDsoulZwM5BrENErAJWAbS3t8d3f/+9Ayq2kuJPtAxWpV+7ntz+Orf/+MV+v+qNa2vh6l87kwvOOJWHnuzk+xs6++8s4L1nTeZjF8yqa0yy2r4vXTiTLz2ymd4TJkFrG11vHOaU8QN7o+9T9/8JADf/8eqa1geOtQcq/NWIiMLHyT767plccMapue1V7dfbvG1rqaue45Y7p21736z6+FY7LjDo88lTrW/MmXzSoPtsX7+q1F/r2Rbyh4kG05/z+mS1moGaHt+8/l5uebXh2nqGXL53feVltQT6OmCupDOBl4HLgE+WrLMaWJGNry8CfjmSxs9r0dIiLjjj1OOeaH3jnKUPSt/Ybd7YXt9Y6GDG6Krtu298jraxwMDf6OvtDbonn0PPhOk8uf31mmvKGwfNa69K7Qz5bV1NPcet59Mmeccd7PnkqeUN1cH02bz+Ws+2UPlxqKc/5/XJvMew2rK8fectH+xzf7ByAz0ijkpaATxG4WOLd0fEJknXZ8tXAmsofGSxg8LHFq8ZsoqHWV4nrSWEqoVJNdX2/bcbX677Y3wHFlwKrW3c/uMXa/6tYShfwJq1bT3hmHfcoXpCV6ur3j5bz4vuYPv6UL5hWs+Lbt6+a3k+DKY9Biv3TdGh0t7eHuvXV3zPdFTp+wbb8zv3s2CIvr5fuu/HN+/i0w88deybhAAnjm3lry9/T+43CevZ9u32jc2+r+EP1eNbb92DratZ21Yy1H2yWs15y/L6xnA/HyRVfFPUgT5K1dORbn/8RW790Qv93rUW8EcXv4tPXzS3pmOPxICrR4rnNJo06k9VDOdF1XAcuxwHeqIG25HquRoyGyp+Ua1NtUAfvX8UwmhtERfNnz7gEF48bxoLZ0867mpo8bxpQ1SpWb7B9mf7Nw70t6HWFh37i36+GjJLhwP9bcpXQ2bp8f8UNTNLhAPdzCwRDnQzs0Q40M3MEuFANzNLRNO+WCSpC9g+yM2nAHsaWE6juK7ajcSaYGTWNRJrgpFZ10isCRpb1xkRMbXcgqYFej0kra/0Talmcl21G4k1wcisayTWBCOzrpFYEwxfXR5yMTNLhAPdzCwRozXQVzW7gApcV+1GYk0wMusaiTXByKxrJNYEw1TXqBxDNzOz443WK3QzMyvhQDczS8SoC3RJSyRtkdQh6cZm19NH0jZJz0raKKkp/7lD0t2Sdkt6rmjeaZJ+JOnF7Ofw/HPD/LpukfRy1l4bJV0yzDXNlvSPkjZL2iTpM9n8prZXlbqa1l6STpD0L5Kezmr6r9n8ZrdVpbqa2reyGlolPSXp4Wx6WNpqVI2hS2oFXgAuBjqBdcDlEfF8UwujEOhAe0Q07UsNkn4dOADcGxHnZfP+EngtIr6cvQCeGhE3jIC6bgEORMT/GM5aimo6HTg9Ip6UNBHYAFwKXE0T26tKXZ+gSe0lScBJEXFA0hjgJ8BngI/R3LaqVNcSmti3str+CGgHTo6I3xqu5+Fou0K/EOiIiK0R0Q08CCxrck0jRkQ8AbxWMnsZcE92/x4K4TCsKtTVVBHxSkQ8md1/A9gMzKTJ7VWlrqaJggPZ5JjsFjS/rSrV1VSSZgEfAb5RNHtY2mq0BfpMYEfRdCdN7uxFAvihpA2Slje7mCLTI+IVKIQFMJL+z9wKSc9kQzLDPhTUR9Ic4D3AzxhB7VVSFzSxvbIhhI3AbuBHETEi2qpCXdDcvnUb8MdAb9G8YWmr0Rbo5f5HWtNfkTPvi4gLgKXAp7JhBqvs68DZwELgFeArzShC0gTgB8AfRsT+ZtRQTpm6mtpeEdETEQuBWcCFks4bzuNXUqGuprWVpN8CdkfEhuE6ZrHRFuidwOyi6VnAzibV0k9E7Mx+7gb+N4XhoZFgVzYu2zc+u7vJ9QAQEbuyJ2Mv8L9oQntl464/AL4TEQ9ls5veXuXqGgntldWxD1hLYZy66W1Vrq4mt9X7gN/O3lN7EPhNSd9mmNpqtAX6OmCupDMljQUuA1Y3uSYknZS9gYWkk4APAc9V32rYrAauyu5fBfxdE2s5pq9zZ36HYW6v7A21u4DNEfE/ixY1tb0q1dXM9pI0VdKk7P544D8A/0rz26psXc1sq4j4k4iYFRFzKOTTjyPiPzJcbRURo+oGXELhky4/Bz7f7Hqyms4Cns5um5pVF/AAhV8xj1D4beZaYDLwOPBi9vO0EVLXfcCzwDNZZz99mGt6P4XhumeAjdntkma3V5W6mtZewLuBp7JjPwfcnM1vdltVqqupfauovsXAw8PZVqPqY4tmZlbZaBtyMTOzChzoZmaJcKCbmSXCgW5mlggHuplZIhzoZmaJcKCbmSXi/wNykl3JXDJqjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pacf(cad_jpy_df.Price, lags=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dlevs\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:216: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  warnings.warn('A date index has been provided, but it has no'\n",
      "C:\\Users\\dlevs\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:216: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  warnings.warn('A date index has been provided, but it has no'\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "# Estimate and ARIMA Model:\n",
    "# Hint: ARIMA(df, order=(p, d, q))\n",
    "model = ARIMA (cad_jpy_df.Price, order=(5,1,1))\n",
    "\n",
    "# Fit the model\n",
    "results =model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARIMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>D.Price</td>     <th>  No. Observations:  </th>   <td>7928</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>          <td>ARIMA(5, 1, 1)</td>  <th>  Log Likelihood     </th> <td>-8745.622</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>0.729</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Sun, 29 Nov 2020</td> <th>  AIC                </th> <td>17507.244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>23:10:05</td>     <th>  BIC                </th> <td>17563.070</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>1</td>        <th>  HQIC               </th> <td>17526.361</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>         <td>   -0.0058</td> <td>    0.008</td> <td>   -0.743</td> <td> 0.458</td> <td>   -0.021</td> <td>    0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.D.Price</th> <td>    0.4287</td> <td>    0.246</td> <td>    1.740</td> <td> 0.082</td> <td>   -0.054</td> <td>    0.912</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.D.Price</th> <td>    0.0177</td> <td>    0.014</td> <td>    1.264</td> <td> 0.206</td> <td>   -0.010</td> <td>    0.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3.D.Price</th> <td>   -0.0118</td> <td>    0.012</td> <td>   -0.959</td> <td> 0.337</td> <td>   -0.036</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L4.D.Price</th> <td>    0.0110</td> <td>    0.012</td> <td>    0.879</td> <td> 0.379</td> <td>   -0.013</td> <td>    0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L5.D.Price</th> <td>   -0.0191</td> <td>    0.011</td> <td>   -1.702</td> <td> 0.089</td> <td>   -0.041</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.Price</th> <td>   -0.4568</td> <td>    0.246</td> <td>   -1.856</td> <td> 0.064</td> <td>   -0.939</td> <td>    0.026</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>           1.7660</td> <td>          -0.7702j</td> <td>           1.9266</td> <td>          -0.0655</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>           1.7660</td> <td>          +0.7702j</td> <td>           1.9266</td> <td>           0.0655</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.3</th> <td>          -2.3649</td> <td>          -0.0000j</td> <td>           2.3649</td> <td>          -0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.4</th> <td>          -0.2974</td> <td>          -2.4212j</td> <td>           2.4394</td> <td>          -0.2695</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.5</th> <td>          -0.2974</td> <td>          +2.4212j</td> <td>           2.4394</td> <td>           0.2695</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>           2.1892</td> <td>          +0.0000j</td> <td>           2.1892</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             ARIMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                D.Price   No. Observations:                 7928\n",
       "Model:                 ARIMA(5, 1, 1)   Log Likelihood               -8745.622\n",
       "Method:                       css-mle   S.D. of innovations              0.729\n",
       "Date:                Sun, 29 Nov 2020   AIC                          17507.244\n",
       "Time:                        23:10:05   BIC                          17563.070\n",
       "Sample:                             1   HQIC                         17526.361\n",
       "                                                                              \n",
       "=================================================================================\n",
       "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "const            -0.0058      0.008     -0.743      0.458      -0.021       0.009\n",
       "ar.L1.D.Price     0.4287      0.246      1.740      0.082      -0.054       0.912\n",
       "ar.L2.D.Price     0.0177      0.014      1.264      0.206      -0.010       0.045\n",
       "ar.L3.D.Price    -0.0118      0.012     -0.959      0.337      -0.036       0.012\n",
       "ar.L4.D.Price     0.0110      0.012      0.879      0.379      -0.013       0.035\n",
       "ar.L5.D.Price    -0.0191      0.011     -1.702      0.089      -0.041       0.003\n",
       "ma.L1.D.Price    -0.4568      0.246     -1.856      0.064      -0.939       0.026\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            1.7660           -0.7702j            1.9266           -0.0655\n",
       "AR.2            1.7660           +0.7702j            1.9266            0.0655\n",
       "AR.3           -2.3649           -0.0000j            2.3649           -0.5000\n",
       "AR.4           -0.2974           -2.4212j            2.4394           -0.2695\n",
       "AR.5           -0.2974           +2.4212j            2.4394            0.2695\n",
       "MA.1            2.1892           +0.0000j            2.1892            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output model summary results:\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "None of the p values are below 0.05 and therefore the model is not a good fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a2c411ff70>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 5 Day Price Forecast\n",
    "pd.DataFrame(results.forecast(steps=5)[0]).plot(title=\"5-Day CAD/JPY Exchange Rate Forecast with ARIMA 5,1,1 \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** What does the model forecast will happen to the Japanese Yen in the near term?\n",
    "\n",
    "**Answer:** The models is forecasting that the Japanese Yen will strengthen vs. Canadian Dollar over the next 5 days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Volatility Forecasting with GARCH\n",
    "\n",
    "Rather than predicting returns, let's forecast near-term **volatility** of Japanese Yen exchange rate returns. Being able to accurately predict volatility will be extremely useful if we want to trade in derivatives or quantify our maximum loss.\n",
    " \n",
    "Using exchange rate *Returns*, estimate a GARCH model. **Hint:** You can reuse the `returns` variable from the ARMA model section.\n",
    "\n",
    "1. GARCH: Create an GARCH model and fit it to the returns data. Note: Set the parameters to p=2 and q=1: order=(2, 1).\n",
    "2. Output the GARCH summary table and take note of the p-values of the lags. Based on the p-values, is the model a good fit (p < 0.05)?\n",
    "3. Plot the 5-day forecast of the volatility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import arch as arch\n",
    "from arch import arch_model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate a GARCH model:\n",
    "model = arch_model(returns, mean='Zero', vol='GARCH', p=2, q=1)\n",
    "\n",
    "# Fit the model\n",
    "results = model.fit(disp='off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Zero Mean - GARCH Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Price</td>       <th>  R-squared:         </th>  <td>   0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mean Model:</th>         <td>Zero Mean</td>     <th>  Adj. R-squared:    </th>  <td>   0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vol Model:</th>            <td>GARCH</td>       <th>  Log-Likelihood:    </th> <td>  -8911.02</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>   17830.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>   17858.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>          <th>  No. Observations:  </th>    <td>7928</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Sun, Nov 29 2020</td>  <th>  Df Residuals:      </th>    <td>7924</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>23:10:07</td>      <th>  Df Model:          </th>      <td>4</td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Volatility Model</caption>\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>      <th>95.0% Conf. Int.</th>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>omega</th>    <td>9.0733e-03</td> <td>2.545e-03</td> <td>    3.566</td> <td>3.628e-04</td>  <td>[4.086e-03,1.406e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[1]</th> <td>    0.0624</td> <td>1.835e-02</td> <td>    3.402</td> <td>6.682e-04</td>  <td>[2.647e-02,9.841e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[2]</th>   <td>0.0000</td>   <td>2.010e-02</td>   <td>0.000</td>   <td>    1.000</td> <td>[-3.940e-02,3.940e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>beta[1]</th>  <td>    0.9243</td> <td>1.229e-02</td> <td>   75.205</td>   <td>0.000</td>      <td>[  0.900,  0.948]</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Covariance estimator: robust"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                       Zero Mean - GARCH Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                  Price   R-squared:                       0.000\n",
       "Mean Model:                 Zero Mean   Adj. R-squared:                  0.000\n",
       "Vol Model:                      GARCH   Log-Likelihood:               -8911.02\n",
       "Distribution:                  Normal   AIC:                           17830.0\n",
       "Method:            Maximum Likelihood   BIC:                           17858.0\n",
       "                                        No. Observations:                 7928\n",
       "Date:                Sun, Nov 29 2020   Df Residuals:                     7924\n",
       "Time:                        23:10:07   Df Model:                            4\n",
       "                               Volatility Model                              \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
       "omega      9.0733e-03  2.545e-03      3.566  3.628e-04  [4.086e-03,1.406e-02]\n",
       "alpha[1]       0.0624  1.835e-02      3.402  6.682e-04  [2.647e-02,9.841e-02]\n",
       "alpha[2]       0.0000  2.010e-02      0.000      1.000 [-3.940e-02,3.940e-02]\n",
       "beta[1]        0.9243  1.229e-02     75.205      0.000      [  0.900,  0.948]\n",
       "=============================================================================\n",
       "\n",
       "Covariance estimator: robust\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarize the model results\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Our p-values for GARCH and volatility forecasts tend to be much lower than our ARMA/ARIMA return and price forecasts. In particular, here we have all p-values of less than 0.05, except for alpha(2), indicating overall a much better model performance. In practice, in financial markets, it's easier to forecast volatility than it is to forecast returns or prices. (After all, if we could very easily predict returns, we'd all be rich!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = results.plot(annualize='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-06-04'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the last day of the dataset\n",
    "last_day = returns.index.max().strftime('%Y-%m-%d')\n",
    "last_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a 5 day forecast of volatility\n",
    "forecast_horizon = 5\n",
    "\n",
    "# Start the forecast using the last_day calculated above\n",
    "forecasts=results.forecast(start=last_day, horizon=forecast_horizon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>h.1</th>\n",
       "      <th>h.2</th>\n",
       "      <th>h.3</th>\n",
       "      <th>h.4</th>\n",
       "      <th>h.5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-06-04</th>\n",
       "      <td>12.566035</td>\n",
       "      <td>12.573725</td>\n",
       "      <td>12.581308</td>\n",
       "      <td>12.588786</td>\n",
       "      <td>12.596161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  h.1        h.2        h.3        h.4        h.5\n",
       "Date                                                             \n",
       "2020-06-04  12.566035  12.573725  12.581308  12.588786  12.596161"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Annualize the forecast\n",
    "intermediate = np.sqrt(forecasts.variance.dropna() * 252)\n",
    "intermediate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Date</th>\n",
       "      <th>2020-06-04</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>h.1</th>\n",
       "      <td>12.566035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.2</th>\n",
       "      <td>12.573725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.3</th>\n",
       "      <td>12.581308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.4</th>\n",
       "      <td>12.588786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.5</th>\n",
       "      <td>12.596161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Date  2020-06-04\n",
       "h.1    12.566035\n",
       "h.2    12.573725\n",
       "h.3    12.581308\n",
       "h.4    12.588786\n",
       "h.5    12.596161"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose the forecast so that it is easier to plot\n",
    "final = intermediate.dropna().T\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a2c254d6d0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Yv5KdV8CM77fyxo/baFQviL9cF8m11j213CwcjDF+4+ft6UxZsJ7t+44wqm97Hr+iJy2se2qFlHriTUTeFpE0EdlQaN5LIpIgInEiskBESnzwiIgkich6EVkrIrGF5keKyApn2eci0qTQsikislVENovIZZU9QGOM/8s8lseU+XHcMHMlufku/n3nQP56Q5QFQyWU5arMu0DRkehjgN6qGgFsAaacYvuhqhpV5Jnhs4DJqtoHWABMABCRXsCNQLjzmjNEpHZZDsQYE3hUla/W7+WSV37ko9W7uee8Tiz50/lcENbK26XVeKWeVlLVpSLSsci8JYV+XQmMKefrdgeWOtMxwNfAE8DVwH9UNQfYISJbgYHAinLu3xjj51IOurunfrPJ3T31beueWqWq4prDncBHHpYpsEREFHhLVWc68zcAVwGfAtcBHZz57XGHzXHJzrxiRGQcMA4gNDS0MvUbY2qQApfywcqdTF+cQIEqj47owZ1DrHtqVatUOIjIY0A+8KGHVYaoaoqItAZiRCRBVZfiDpS/i8iTwGdA7vFdlrCPEh+y7wTNTHAPE1qJwzDG1BCbfzvE5Plx/LrrIOd1a8lz1/QhtEUDb5fllyocDiJyGzASuFg9jJKiqinOzzQRWYD7FNFSVU0Ahjn7CQOucDZJ5n+tCIAQIKWiNRpj/EN2XgGvf7eVN3/cRuPgIF65PpJRfa176ulUoXAQkeHAJOACVT3qYZ2GQC1VPeRMDwOecZa1dgKjFvA48Kaz2WfAbBF5BWgHdANWVaRGY4x/WLk9nUfnr2f7/iNc27c9j4/sRfOGdb1dlt8rNRxEZA5wIdBSRJKBp3D3TqqH+1QRwEpVHS8i7YBZqjoCaAMscJYHAbNVdbGz27Eicp8zPR94B0BV40XkY2Aj7tNV96lqQZUcqTGmRsk8mscLizbxn9W76dC8Pu/dOZDzrRdStRF/GDc3OjpaY2NjS1/RGOPz3N1T3U9PPXA0l7vP7cSDl3Szp6eeBiKypshtBifYu22M8RkpB4/xxMINfJuQRu/2TXj3jgH0bm/dU73BwsEY43XHn5764qIEXAqPjejJHUM6WvdUL7JwMMZ41da0w0yeF0fszgOc160lz4/qQ4fm1j3V2ywcjDFekVfg4q0ft/H3b7dSv25tXr4uktH29FSfYeFgjKl2cckHmTg3joTfDnFFn7Y8fVU4rRrbQ/J8iYWDMabaHMst4JWYzfzrpx20alyPmbf0Z1j4md4uy5TAwsEYUy3+u3U/U+avZ1fGUcYODGXKiB40Ca7j7bKMBxYOxpjTKvNoHs99tZGPY5Pp2KIB/xk3mMGdW3i7LFMKCwdjzGmzaP1envwsnowjuYy/oAv/d0k3guvYEC01gYWDMabKpWVl88SnG/g63j3Wwju3281sNY2FgzGmyqgqH63ezXNfbSI338Wk4T24+7xO1LGb2WocCwdjTJVI2n+EKfPXs2J7OoM6NWfa6Ag6tWzo7bJMBVk4GGMqJb/Axdv/3cErMVuoU6sWz4/qw40DOlCrlt3MVpNZOBhjKmxjShaT5sWxfk8ml/Rsw5+v6c2ZTYO9XZapAhYOxphyy84r4LXvEnnrx+00a1CH12/qyxV92tqjL/yIhYMxplxW7chg8vw4tu87wuh+ITx+RU/OsJHZ/I6FgzGmTA5l5/Hi4gQ+WLmLkDNsZDZ/Z+FgjCnVt5tSeXzhBn7LyubOIZ14eFgYDevZ14c/s/+7xhiP9h/OYernG/l8XQphbRox4+Zz6Bt6hrfLMtXAwsEYU4yqsuDXPTz7xUYO5+Tzp0vCuPfCLtQNspvZAoWFgzHmJMkHjvLYgg38uGUffUOb8eLoCMLaNPZ2WaaaWTgYYwD3OM7vrUhi+tebAXjqyl7cenZHatvNbAHJwsEYQ2LqISbNi+OXXQc5P6wVz13T28ZxDnClhoOIvA2MBNJUtbcz7yXgSiAX2AbcoaoHS9g2CTgEFAD5qhrtzI8C3gSCgXzgD6q6SkQ6ApuAzc4uVqrq+EocnzHmFHLzXbz54zZe/24rDerV5pXrIxnV18ZxNmVrObwLvA68V2heDDBFVfNF5EVgCjDJw/ZDVXV/kXnTgamqukhERji/X+gs26aqUWWs3xhTQWt3H2TS3Dg2px5iZIR7HOeWjWwcZ+NWajio6lLnL/rC85YU+nUlMKacr6tAE2e6KZBSzu2NMRV0NDefvyzZwjv/3UHrxsHMujWaS3q18XZZxsdUxTWHO4GPPCxTYImIKPCWqs505v8f8LWIvAzUAs4ptE0nEfkVyAIeV9VlJe1YRMYB4wBCQ0MrfxTGBICfEvczZUEcuzOOcfOgUCZdbuM4m5JVKhxE5DHc1ww+9LDKEFVNEZHWQIyIJKjqUuBe4E+qOk9Ergf+BVwC7AVCVTVdRPoDC0UkXFWziu7YCZqZANHR0VqZ4zDG3x08msufv9zE3DXJdG7ZkI/GDWaQjeNsTqHC4SAit+G+UH2xqpb45ayqKc7PNBFZAAwElgK3AQ86q30CzHLWywFynOk1IrINCANiK1qnMYFMVflq/W889Vk8B47m8ocLu/DAxTaOsyldhcJBRIbjvgB9gaoe9bBOQ6CWqh5ypocBzziLU4ALgB+Ai4BEZ5tWQIaqFohIZ6AbsL0iNRoT6FKzsnl84QZiNqbSu30T/n3nAMLb2TjOpmzK0pV1Du6eRC1FJBl4CnfvpHq4TxWB0+VURNoBs1R1BNAGWOAsDwJmq+piZ7f3AK+KSBCQjXPtADgfeEZE8nF3fx2vqhlVcqTGBAiXS/nP6t288NUmcgtcTLm8B3ed24kgG8fZlIN4OCNUo0RHR2tsrJ15MiZp/xEmz49j5fYMBnduzrRrI+ho4zgbD0RkzfH7z4qyO6SN8QP5BS5m/bSDv8ZsoW5QLV641j2Os93MZirKwsGYGi4+JZNJ8+LYsCeLYb3a8Ow1vWnTxMZxNpVj4WBMDZWdV8Cr3yYyc+l2zmhQlxk39+Py3mdaa8FUCQsHY2qgn7enM3n+enbsP8J1/UN47IqeNGtg4zibqmPhYEwNkpWdx7RFCcz+eRcdmtfng7sGcW63lt4uy/ghCwdjaoiYjak8sXADaYeyufvcTjw0LIwGde2fsDk97JNljI/bdyiHpz+P58u4vfQ4szFv3tKfqA7NvF2W8XMWDsb4KFVl/i97ePbLjRzNKeDhS8P4/QU2jrOpHhYOxvig3RlHeXTBepYl7qf/WWfw4ug+dG1t4zib6mPhYIwPKXAp/16exMtLNiPA1KvCuWXwWdSycZxNNbNwMMZHbHHGcf5110Eu7N6K50b1oX2z+t4uywQoCwdjvCw338WMH7byj++30qheEH+7IYqro9rZzWzGqywcjPGiX3YdYPK8OLakHubqqHY8ObIXLWwcZ+MDLByM8YIjOfm8vGQz7y5Pom2TYN6+PZqLetg4zsZ3WDgYU82WbtnHlPnr2XPwGLeefRYTh/egUT37p2h8i30ijakmB47k8uyXG5n/yx66tGrI3PFnE92xubfLMqZEFg7GnGaqyhdxe5n6eTwHj+Zx/9Cu3H9RVxvH2fg0CwdjTqO9mcd4YuEGvtmURkRIU96/axA92zbxdlnGlMrCwZjTQNU9jvPzX24iz+XisRE9uWNIRxvH2dQYFg7GVLFd6UeZPD+O5dvSObtzC6aN7sNZLWwcZ1OzWDgYU0WOP/ripa83U7uW8PyoPowdaOM4m5rJwsGYKrA17TAT567jl10HGeo8+qKdPfrC1GAWDsZUQl6Bi5lLt/Pqt4k0qFubv94QyTVR7a21YGo8CwdjKig+JZOJc+OIT8liRJ8zmXpVb1o1tkdfGP9QatcJEXlbRNJEZEOheS+JSIKIxInIAhEpcVgqEUkSkfUislZEYgvNjxKRlcfni8jAQsumiMhWEdksIpdV9gCNqWo5+QX8Zclmrn79v6Rm5fDGzf2YcXN/CwbjV8rSr+5dYHiReTFAb1WNALYAU06x/VBVjVLV6ELzpgNTVTUKeNL5HRHpBdwIhDuvOUNE7E4h4zN+3XWAkX//ide+28pVUe345qHzubxPW2+XZUyVK/W0kqouFZGOReYtKfTrSmBMOV9XgeN3AjUFUpzpq4H/qGoOsENEtgIDgRXl3L8xVepYrru18PZ/d9CmSTDv3DGAod1be7ssY06bqrjmcCfwkYdlCiwREQXeUtWZzvz/A74WkZdxt17Ocea3xx02xyU784oRkXHAOIDQ0NBKHYAxp7JyezqT5sWxM/0oNw8KZfLlPWgcXMfbZRlzWlUqHETkMSAf+NDDKkNUNUVEWgMxIpKgqkuBe4E/qeo8Ebke+BdwCVBSFw8tacdO0MwEiI6OLnEdYyrjUHYeLy5O4IOVuwht3oA59wzm7C4tvF2WMdWiwuEgIrcBI4GLVdXTF3iK8zNNRBbgPkW0FLgNeNBZ7RNgljOdDHQotIsQ/nfKyZhq88PmNB6dv569WdncdW4nHh4WRoO61rnPBI4KPehFRIYDk4CrVPWoh3Uaikjj49PAMOB4j6cU4AJn+iIg0Zn+DLhRROqJSCegG7CqIjUaUxEHj+by8MfruP2d1TSoF8S8e8/hiZG9LBhMwCn1Ey8ic4ALgZYikgw8hbt3Uj3cp4oAVqrqeBFpB8xS1RFAG2CBszwImK2qi53d3gO8KiJBQDbOtQNVjReRj4GNuE9X3aeqBVV1sMacyuINv/H4wg0cOJrL/UO78seLu1IvyDrLmcAkHs4I1SjR0dEaGxtb+orGlGDfoRye/iyeL9fvpVfbJkwfE0Hv9k29XZYxp52IrClym8EJ1lY2AUtV+XRtCk9/Hs/RnAImXNadced3po49VtsYCwcTmPZmHuOxBRv4LiGNvqHNeGlMBF1bN/Z2Wcb4DAsHE1CKDsLzxMhe3H5OR2rXsgflGVOYhYMJGDYIjzFlZ+Fg/F5Jg/DcOKADtay1YIxHFg7Gr21NO8ykeXGs2XnABuExphwsHIxfyi9w8VahQXheuT6SUX1tEB5jysrCwfidjSlZTJy3jg17bBAeYyrKwsH4jZz8Av7x3VZm/LCNZg3q8sbN/WysBWMqyMLB+IVfdx1g4tw4EtMOc22/9jw5shfNGtT1dlnG1FgWDqZGO5ZbwCsxm/nXT84gPLcPYGgPG4THmMqycDA11srt6UyeF0dS+lFuGhTKFBuEx5gqY+FgapzDOflMW7TpxCA8s+8ZxDldWnq7LGP8ioWDqVFsEB5jqof9qzI1wsGjuTz7xSbm/ZJM19aNmDv+HPqfdYa3yzLGb1k4GJ+3eMNvPPHpBjKO2CA8xlQXCwfjs/YfzuGpT/83CM87tw+wQXiMqSYWDsbnHB+EZ+rn8RyxQXiM8QoLB+NTfsvM5rEF6/nWGYRn+ugIurWxQXiMqW4WDsYn2CA8xvgWCwfjdbsz3IPw/HerDcJjjK+wcDBeY4PwGOO7LByMV9ggPMb4NgsHU63yC1zMXLadv32TSP06NgiPMb6q1HAQkbeBkUCaqvZ25r0EXAnkAtuAO1T1YAnbJgGHgAIgX1WjnfkfAd2d1ZoBB1U1SkQ6ApuAzc6ylao6vqIHZ3xL4UF4Lu99JlOvDqd142Bvl2WMKUFZWg7vAq8D7xWaFwNMUdV8EXkRmAJM8rD9UFXdX3iGqt5wfFpE/gJkFlq8TVWjylCXqSFOHoSnjg3CY0wNUGo4qOpS5y/6wvOWFPp1JTCmIi8u7nMJ1wMXVWR74/tOGoSnb3ueGNmLMxraIDzG+LqquOZwJ/CRh2UKLBERBd5S1ZlFlp8HpKpqYqF5nUTkVyALeFxVl5W0YxEZB4wDCA0NrUz95jSwQXiMqdkqFQ4i8hiQD3zoYZUhqpoiIq2BGBFJUNWlhZaPBeYU+n0vEKqq6SLSH1goIuGqmlV0x07QzASIjo7WyhyHqVo2CI8xNV+Fw0FEbsN9ofpiVS3xy1lVU5yfaSKyABgILHW2DwKuBfoXWj8HyHGm14jINiAMiK1onab62CA8xviPCoWDiAzHfQH6AlU96mGdhkAtVT3kTA8Dnim0yiVAgqomF9qmFZChqgUi0hnoBmyvSI2mev24ZR+Pzl9PSuYxG4THGD9Qlq6sc4ALgZYikinBomQAAA1fSURBVAw8hbt3Uj3cp4rA6XIqIu2AWao6AmgDLHCWBwGzVXVxoV3fyMmnlADOB54RkXzc3V/Hq2pGJY7PnGY2CI8x/kk8nBGqUaKjozU21s48VbfCg/CMv6Azf7yoG8F1bBAeY2oKEVlz/P6zoqzdb8pt/+Ecnvosni/jbBAeY/yVhYMpM1Xls3UpPP2ZexCeR4aF8fsLutggPMb4IQsHUyZpWdk8umAD32xKJapDM14aY4PwGOPPLBzMKakqC37dw9OfxZOT7+LxK3pyx5BONgiPMX7OwsF49FtmNo8uWM93CWlEn3UG08dE0LlVI2+XZYypBhYOphhV5ZM1yTz7xUbyCmzITmMCkYWDOUnKwWNMmb+eH7fsY2DH5kwfE0HHljZkpzGBxsLBAO7Wwsexu/nzF5vIdylTrwrnlsFn2ZCdxgQoCwfDnoPHmDwvjmWJ+xncuTnTR0cS2qKBt8syxniRhUMAU1XmrNrN819twqXKs1eHc/Mgay0YYywcAtbujKNMmb+en7bu55wuLXhxdAQdmltrwRjjZuEQYFwu5cNVu5j21SYAnhvVm5sGhuI8INEYYwALh4CyO+MoE+fGsWJ7Oud2bcm00X0IOcNaC8aY4iwcAoDLpXzw806mLUqglgjTru3DDQM6WGvBGOORhYOf25l+hIlz4/h5Rwbnh7XihWv70L5ZfW+XZYzxcRYOfsrlUv69IonpizcTVEuYPjqC66JDrLVgjCkTCwc/tGP/ESbNjWNVUgZDu7fi+Wv70LaptRaMMWVn4eBHClzKO//dwctLNlOndi1evi6S0f3aW2vBGFNuFg5+Ytu+w0ycG8eanQe4uEdrnhvVhzObBnu7LGNMDWXhUMMVuJS3f3K3FoLr1OavN0RyTZS1FowxlWPhUINtTTvMhLnr+HXXQS7p2YbnR/WmdRNrLRhjKs/CoQbKL3Ax66cdvBKzhQZ1a/PqjVFcFdnOWgvGmCpj4VDDJKYe4pG5cazbfZDLwtvw7DW9ad3YWgvGmKpl4VBD5Be4eGvpdl79JpGG9Wrz2ti+jIxoa60FY8xpUWo4iMjbwEggTVV7O/NeAq4EcoFtwB2qerCEbZOAQ0ABkK+q0c78j4DuzmrNgIOqGuUsmwLc5WzzgKp+XZkD9AebfzvEhLnriEvO5Io+bZl6dTgtG9XzdlnGGD9WlpbDu8DrwHuF5sUAU1Q1X0ReBKYAkzxsP1RV9xeeoao3HJ8Wkb8Amc50L+BGIBxoB3wjImGqWlC2w/EveQUu3vpxG69+m0iT4Dr846Z+XBHR1ttlGWMCQKnhoKpLRaRjkXlLCv26EhhTkRcX9zmR64GLnFlXA/9R1Rxgh4hsBQYCKyqy/5ps094sHvlkHfEpWVwZ2Y6nr+xFC2stGGOqSVVcc7gT+MjDMgWWiIgCb6nqzCLLzwNSVTXR+b097rA5LtmZV4yIjAPGAYSGhlawdN+TV+BixvfbeP37RJrWr8Obv+vH8N7WWjDGVK9KhYOIPAbkAx96WGWIqqaISGsgRkQSVHVpoeVjgTmFd1nCPrSkHTtBMxMgOjq6xHVqmviUTB75JI5Ne7O4OqodT18ZzhkN63q7LGNMAKpwOIjIbbgvVF+sqp6+wFOcn2kisgD3KaKlzvZBwLVA/0KbJAMdCv0eAqRUtMaaIjffxevfb2XG91s5o2FdZt7Sn2HhZ3q7LGNMAKtQOIjIcNwXoC9Q1aMe1mkI1FLVQ870MOCZQqtcAiSoanKheZ8Bs0XkFdwXpLsBqypSY02xYU8mj3yyjoTfDnFt3/Y8eWUvmjWw1oIxxrvK0pV1DnAh0FJEkoGncPdOqof7VBHASlUdLyLtgFmqOgJoAyxwlgcBs1V1caFd38jJp5RQ1XgR+RjYiPt01X3+2lMpJ7+A177dyhs/bqNFw7rMujWaS3q18XZZxhgDgHg4I1SjREdHa2xsrLfLKLO45IM88sk6tqQeZkz/EJ64ohdNG9TxdlnGmAAjImuO339WlN0hXY2y8wp49dtEZi7dTqtG9Xjn9gEM7dHa22UZY0wxFg7V5NddB5gwN46taYe5PjqEx67oRdP61lowxvgmC4fTLDuvgL9+s4V/Lt1OmybBvHvHAC7sbq0FY4xvs3A4jdbsPMDEuevYtu8IYwd2YMqInjQJttaCMcb3WTicBtl5BfxlyWZm/bSDdk3r896dAzk/rJW3yzLGmDKzcKhisUkZTJwbx/b9R7h5UCiTL+9BY2stGGNqGAuHKnIst4CXvt7MO8vdrYUP7x7EkK4tvV2WMcZUiIVDFVi1I4OJc9eRlH6UWwafxaTLe9Conr21xpiay77BKuFobj7TF2/m3yuSCDmjPrPvGcQ5Xay1YIyp+SwcKmjl9nQmzo1jV8ZRbj+nIxMu605Day0YY/yEfZuV05GcfF5cnMB7K3ZyVosGfDRuMIM6t/B2WcYYU6UsHMph+db9TJwXx56Dx7hzSCcmXNad+nVre7ssY4ypchYOZXA4J59pizbxwcpddGrZkE9+fzbRHZt7uyxjjDltLBxK8VPifibNiyMl8xj3nNeJhy611oIxxv9ZOHhwKDuP579KYM6qXXRu1ZC548+m/1nWWjDGBAYLhxIs3bKPyfPi+C0rm9+f35k/XRpGcB1rLRhjAoeFQyFZ2Xk898UmPordTdfWjZh37zn0DT3D22UZY0y1s3BwfL85jUfnryc1K5t7L+zCgxd3s9aCMSZgBXw4ZB7N49kvNzJ3TTJhbRrx5u+GENmhmbfLMsYYrwrocIhLPsg978Wy/3Au9w/tyh8v7kq9IGstGGNMQIdDaPMGhLVpzKxbe9AnpKm3yzHGGJ8R0OHQrEFd3r9rkLfLMMYYn1PL2wUYY4zxPRYOxhhjiik1HETkbRFJE5ENhea9JCIJIhInIgtEpMTuPSKSJCLrRWStiMQWWfZHEdksIvEiMt2Z11FEjjnrrxWRNyt7gMYYY8qvLC2Hd4HhRebFAL1VNQLYAkw5xfZDVTVKVaOPzxCRocDVQISqhgMvF1p/m7N+lKqOL8tBGGOMqVqlhoOqLgUyisxboqr5zq8rgZByvu69wDRVzXH2l1bO7Y0xxpxGVXHN4U5gkYdlCiwRkTUiMq7Q/DDgPBH5WUR+FJEBhZZ1EpFfnfnneXpRERknIrEiErtv377KH4UxxpgTKtWVVUQeA/KBDz2sMkRVU0SkNRAjIglOSyQIOAMYDAwAPhaRzsBeIFRV00WkP7BQRMJVNavojlV1JjATIDo6WitzHMYYY05W4ZaDiNwGjARuVtUSv5xVNcX5mQYsAAY6i5KB+eq2CnABLVU1R1XTnW3WANtwtzKMMcZUowq1HERkODAJuEBVj3pYpyFQS1UPOdPDgGecxQuBi4AfRCQMqAvsF5FWQIaqFjgtiW7A9tLqWbNmzX4R2VmRY3G0BPZXYvvTxeoqH6urfKyu8vHHus7ytKDUcBCROcCFQEsRSQaewt07qR7uU0UAK1V1vIi0A2ap6gigDbDAWR4EzFbVxc5u3wbedrrH5gK3qaqKyPnAMyKSDxQA41X1pIvhJVHVVqWtU8oxxhbuTeUrrK7ysbrKx+oqn0Crq9RwUNWxJcz+l4d1U4ARzvR2INLDernA70qYPw+YV1pNxhhjTi+7Q9oYY0wxFg5uM71dgAdWV/lYXeVjdZVPQNUlHjoaGWOMCWDWcjDGGFOMhYMxxphiAiIcnKe9bihlnR4iskJEckTkkeqqzVeV8T272Xkyb5yILBeREnunBYIyvl9XO+/VWufRL+dWV32+pizvV6F1B4hIgYiMOd11+aoyfr4uFJHMQk+1frIyrxnQI8EVkQE8AFzj7UJqkB24b4Q8ICKX474wZkPrefYt8JlzT08E8DHQw8s1+TQRqQ28CHzt7VpqiGWqOrIqdhQQLQdHbRH5pzN+xBIRqV94oaqmqepqIM9L9fmi0t6z5ap6wPm1Ik/n9TelvV+HCz1qpiHuB1MGslO+X44/4r73yZ7cXLb3q8oEUjh0A/7hjB9xEBjt5XpqgvK8Z3fh+em8gaLU90tERolIAvAl7icaB7JTvl8i0h4YBdigX25l+fd4toisE5FFIhJemRcLpHDYoaprnek1QEcv1lJTlOk9cwZvugv387YCWanvl6ouUNUeuE9fPluNtfmi0t6vvwGTVLWgWqvyXaW9X78AZ6lqJPAa7mfYVVgghUNOoekC7HpLWZT6njnnzmcBVx9/om4AK/NnzHl0fRcRaXnaq/Jdpb1f0cB/RCQJGAPMEJFAviZ4yvdLVbNU9bAz/RVQpzKfL/uCNBUmIqHAfOAWVd3i7Xp8nYh0xT0MropIP9xPIw70QPVIVTsdnxaRd4EvVLVSfw37MxE5E0h1Pl8Dcf/xX+HPV0CHg4iMB1DVN503NhZoArhE5P+AXiUNNBTICr9nwJNAC9x/0QHk++JTK72pyPs1GrhVRPKAY8ANnsZCCVRF3i9TiiLv1xjgXuep1seAGyvz+bLHZxhjjCkmkK45GGOMKSMLB2OMMcVYOBhjjCnGwsEYY0wxFg7GGGOKsXAwxhhTjIWDMcaYYv4fVP8u4CrOrxUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the final forecast\n",
    "final.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** What does the model forecast will happen to volatility in the near term?\n",
    "\n",
    "**Answer:** The models predicts that the CADJPY volatility will increase over the next 5 days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Based on your time series analysis, would you buy the yen now?\n",
    "\n",
    "    * The ARMA model predicts that the daily CADJPY returns will be negative over thenext 5 days. Negative returs for this currency pair mean weaker Canadian Dollar and stronger Japanese Yen.\n",
    "    * The ARIMA model too predicts lower CADJPY over the next 5 days, which means weaker CAD and stronger JPY.\n",
    "    * Therefore, both model support the case for buying the Japanese Yen against the Canadian Dollar now with the time-frame of 5 days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Is the risk of the yen expected to increase or decrease?\n",
    "    * The volatility (and therefore the risk) of the CADJPY currency pair is expected to increase over the next 5 days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Based on the model evaluation, would you feel confident in using these models for trading?\n",
    "    * The models look promising, however, I would like to use these time series models in combination with technical analysis and observe the perfomance for some time before deciding to implement them for trading.\n",
    "    * I would also want to experiment with model parmeters and perhaps find models with better fit. My analysis above suggested that in its current form the AIRMA model is not a good fit.\n",
    "    "
   ]
  }
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