VaR_validation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/olesyamba/Risk_analysis/blob/main/VaR_validation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TUAAt--KgbkB"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.stats as ss\n",
        "!pip install yfinance\n",
        "import yfinance as yf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YAJ2RJeShdMX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f638c6e-09ff-498f-bf02-f078167e6ab9"
      },
      "source": [
        "quotes = yf.download(\"AAPL\", \"2016-01-01\", \"2020-12-31\")\n",
        "returns = quotes['Adj Close'].pct_change().dropna()\n",
        "returns.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2016-01-05   -0.025059\n",
              "2016-01-06   -0.019570\n",
              "2016-01-07   -0.042205\n",
              "2016-01-08    0.005288\n",
              "2016-01-11    0.016192\n",
              "Name: Adj Close, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRqsFBo1hwAP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "367d0fb2-dfbb-4abc-e680-2809d90db7dc"
      },
      "source": [
        "# Эмпирическая оценка VaR\n",
        "\n",
        "level = 0.99\n",
        "VaRs = returns.rolling(252, closed=\"left\").apply(lambda x:-x.quantile(1 - level)).dropna()\n",
        "display(VaRs)\n",
        "#дроп нэ надо чтобы первые нулевые удалились, нампай и пандас, реализуя методы по отношению к массиву, не меняют их размерность, просто заполняют нэ\n",
        "#парметрическая оценка вар: х это ретернс, а именно кусочек в 252 дня, это написано для норм распр, можно менять на любое\n",
        "\n",
        "VaRs_param = returns.rolling(252, closed=\"left\").apply(lambda x:- ss.norm.ppf(1 - level, *ss.norm.fit(x))).dropna()\n",
        "display(VaRs_param)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Date\n",
              "2017-01-04    0.036269\n",
              "2017-01-05    0.036269\n",
              "2017-01-06    0.036269\n",
              "2017-01-09    0.029306\n",
              "2017-01-10    0.029306\n",
              "                ...   \n",
              "2020-12-23    0.079567\n",
              "2020-12-24    0.079567\n",
              "2020-12-28    0.079567\n",
              "2020-12-29    0.079567\n",
              "2020-12-30    0.079567\n",
              "Name: Adj Close, Length: 1005, dtype: float64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Date\n",
              "2017-01-04    0.033552\n",
              "2017-01-05    0.033250\n",
              "2017-01-06    0.033028\n",
              "2017-01-09    0.032255\n",
              "2017-01-10    0.032255\n",
              "                ...   \n",
              "2020-12-23    0.065223\n",
              "2020-12-24    0.065269\n",
              "2020-12-28    0.065276\n",
              "2020-12-29    0.065301\n",
              "2020-12-30    0.065417\n",
              "Name: Adj Close, Length: 1005, dtype: float64"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYHLWiy5jQoR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "ced656c7-0439-478e-fe3a-723bfb1c6408"
      },
      "source": [
        "plt.figure(figsize=(16,5))\n",
        "returns.plot()\n",
        "(-VaRs).plot(color='y')\n",
        "(-VaRs_param).plot(color='r')\n",
        "\n",
        "plt.title(\"Daily returns and VaR line\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_srYshYicRB"
      },
      "source": [
        "VaRs.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hg4zJF8Ci-5p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14850bd6-93cd-4439-a772-5e4a4e4b7d92"
      },
      "source": [
        "#оцениваем пробития\n",
        "hits = returns[VaRs.index] < -VaRs\n",
        "hits\n",
        "#false это когда нет пробития"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2017-01-04    False\n",
              "2017-01-05    False\n",
              "2017-01-06    False\n",
              "2017-01-09    False\n",
              "2017-01-10    False\n",
              "              ...  \n",
              "2020-12-23    False\n",
              "2020-12-24    False\n",
              "2020-12-28    False\n",
              "2020-12-29    False\n",
              "2020-12-30    False\n",
              "Name: Adj Close, Length: 1005, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_OOePttYjZn4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9b49294-c178-4d85-f1b3-f0be3c4b5565"
      },
      "source": [
        "hit_times = hits.index[hits]\n",
        "hit_times"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2017-05-17', '2017-06-09', '2017-08-10', '2018-02-02',\n",
              "               '2018-04-20', '2018-10-10', '2018-11-02', '2018-11-12',\n",
              "               '2018-11-20', '2019-01-03', '2019-05-13', '2020-01-31',\n",
              "               '2020-02-24', '2020-02-27', '2020-03-09', '2020-03-12',\n",
              "               '2020-03-16', '2020-09-03'],\n",
              "              dtype='datetime64[ns]', name='Date', freq=None)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xw_UuboKjSI-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "9247112a-a78a-48de-b248-159a75ab4414"
      },
      "source": [
        "plt.figure(figsize=(16,5))\n",
        "returns.plot()\n",
        "(-VaRs).plot(color='y')\n",
        "plt.plot(hit_times, returns[hit_times], 'r*')\n",
        "\n",
        "plt.title(\"Daily returns and VaR curve\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-pq3jpKejvQn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44fa7884-e31f-4043-ec85-95090b7db687"
      },
      "source": [
        "N = hits.size\n",
        "n = hits.sum()\n",
        "N, n # N - количество наблюдений, n - количество пробоев VaR"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1005, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CmwuPwhnjvOI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b924a278-4bb0-4196-df39-5ec63ff0a5a0"
      },
      "source": [
        "# Hit fraction (expected 0.01). Вероятность пробоя = 1% (1 - level)\n",
        "n / N * 100\n",
        "# по сути 1,79 больше 1 уровня значимости, но так сравнивать нельзя, надо делать\n",
        "# бэктест, потому что вар 99 это теор модель, а мы считаем на эмпирических данных"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.791044776119403"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KQJGvEkyHabZ"
      },
      "source": [
        "#Бэкстестиг\n",
        "\n",
        "Используем биномиальный тест на покрытие\n",
        "\n",
        "H0:  𝑛∼𝐵(𝑁,𝑞),𝑞=1−𝑙𝑒𝑣𝑒𝑙"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "or6zKHbDkMIJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2867d2cc-93aa-41dc-a72f-c766b23a2916"
      },
      "source": [
        "# А действительно ли вероятность пробоя = 1%? двусторонний биномиальный тест\n",
        "p_val = ss.binom_test(n, N, 1-level, alternative=\"two-sided\")\n",
        "p_val * 100\n",
        "# идея тесте: сравниваем со средним ожидаемым количеством пробоев (вар), считаем количество пробоев вправо и влево от среднего"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.4177619417213485"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0NjewHxMG55y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0cc2738c-9292-4003-8bf6-ca9c8711d498"
      },
      "source": [
        "# А действительно ли вероятность пробоя <= 1%? односторонний биномиальный тест\n",
        "p_val = ss.binom_test(n, N, 1-level, alternative=\"greater\")\n",
        "p_val * 100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.4469466935299535"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVP-hQ7THZsF"
      },
      "source": [
        "Как можно интерпретировать полученные результаты на основе p-value?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9-RSMJN8k0JO"
      },
      "source": [
        "# Правило сфетофора, trafic light room, если пивэлью будет больше 5 процентов,\n",
        "# при уровне значимости 95 процентов, мы не можем отвергнуть гипотезу нулевую,\n",
        "# модель признается качественной  - зеленый\n",
        "\n",
        "# если пивэлью меньше 5 процентов но не сильно, пробоев больше чем критическое\n",
        "# значение, но не сильно, модель можно применять, она ошибочно оенена как\n",
        "# плохая, возможно вар слишком маленький, цвет желтый\n",
        "\n",
        "#если пивэлью супермаленькое - в топку эту модель - цвет красный\n",
        "def zone(p):\n",
        "  if p > 0.05:\n",
        "    return \"green\"\n",
        "  elif p > 0.0001:\n",
        "    return \"yellow\"\n",
        "  else:\n",
        "    return \"red\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqRonKHVk_NV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "df2227b1-a567-46ae-8956-604a4cdbc83a"
      },
      "source": [
        "zone(p_val)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'yellow'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AV6gMsE0Hqm4"
      },
      "source": [
        "### Какой тест использовать? Односторонным или двустороннонним?\n",
        "\n",
        "Регулирование? = односторонный тест с пороговым значением \"не хуже, чем 99..\"\n",
        "\n",
        "Внутренняя разработка или тест купленной системы? = односторонний тест\n",
        "\n",
        "Если не независимые пробои - распределение может быть небиномиальным, с тяжелыми хвостами и тесты будут работать некорректно. Поэтому необходимо тестировать независимость.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xL3yVXbaHryG"
      },
      "source": [
        "Тест на независимость: $P(Hit_{t+1}\\,|\\,Hit_t) \\overset{?}{=} P(Hit_{t+1}\\,|\\, \\overline{Hit_t})$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_vWMZpozm0xw",
        "outputId": "b38deecd-b564-4a6c-c3ee-13b749c78bcc"
      },
      "source": [
        "hits_yeasterday = hits.shift(1).dropna().astype(bool)\n",
        "hits_today = hits[hits_yeasterday.index]\n",
        "n_hits_today_given_no_hit_yeasterday = (hits_today & ~(hits_yeasterday)).sum()\n",
        "n_hits_today_given_hit_yeasterday = (hits_today & hits_yeasterday).sum()\n",
        "print(n_hits_today_given_no_hit_yeasterday, n_hits_today_given_hit_yeasterday)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Пробои следуют за спокойными днями, Не повезло! Нет последовательность из пробоев!"
      ],
      "metadata": {
        "id": "pR0wPH_gimIh"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_hTNRzdOIv6Q"
      },
      "source": [
        "Альтернативный тест: исследование времени между пробитиями VaR - если нулевая гипотеза верна, распределение должны быть экспоненциальным (здесь нестрогий тест;  `Christoffersen, Pelltier (2004)` - строгий тест)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VJsqEYWoqWP"
      },
      "source": [
        "hit_deltas = np.diff(np.hstack([VaRs.index[[0]].values, hit_times.values])) / np.timedelta64(1, 'D')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9g4a5Usnp77l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "ae6c648e-33e1-4f03-908a-df5a8b1f4b36"
      },
      "source": [
        "params = ss.expon.fit(hit_deltas)\n",
        "ss.probplot(hit_deltas, dist=ss.expon(*params), plot=plt);\n",
        "# если все хорошо, мы должны увидеть синие точки на красной линии, если есть волны и отклонения, скорее всего наблюдения не независимые"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dD3--f37JrS1"
      },
      "source": [
        "Супер важный технический момент при реализации бэктеста.\n",
        "\n",
        "Пропуски данных (P&L или VaR)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNG6xoSDsdmH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "38170db8-176d-4125-fd48-29472c6a34fd"
      },
      "source": [
        "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
        "losses = [10, 11, 12, 11, 10, 9, 11, 13, 11, 9, 8, 9]\n",
        "VaRs_3m_ahead = [9.5, 9, 8.5, 9, 9.5, 10.5, 9, 10, 11, 12, 11, 10]\n",
        "\n",
        "df = pd.DataFrame({\"Month\": months, \"Losses\": losses, \"VaR\": VaRs_3m_ahead})\n",
        "df = df.set_index(\"Month\")\n",
        "#print(df.to_string(index=False))\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Losses   VaR\n",
              "Month              \n",
              "Jan        10   9.5\n",
              "Feb        11   9.0\n",
              "Mar        12   8.5\n",
              "Apr        11   9.0\n",
              "May        10   9.5\n",
              "Jun         9  10.5\n",
              "Jul        11   9.0\n",
              "Aug        13  10.0\n",
              "Sep        11  11.0\n",
              "Oct         9  12.0\n",
              "Nov         8  11.0\n",
              "Dec         9  10.0"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Losses</th>\n",
              "      <th>VaR</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>10</td>\n",
              "      <td>9.5</td>\n",
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              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>11</td>\n",
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              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>12</td>\n",
              "      <td>8.5</td>\n",
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              "    <tr>\n",
              "      <th>Apr</th>\n",
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              "      <td>9</td>\n",
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              "      <th>Nov</th>\n",
              "      <td>8</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dec</th>\n",
              "      <td>9</td>\n",
              "      <td>10.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b56f5e1d-137d-4b8c-9041-2715057c73e9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b56f5e1d-137d-4b8c-9041-2715057c73e9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b56f5e1d-137d-4b8c-9041-2715057c73e9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODJWnAJTufux",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "e9981a3a-c20b-4c7f-8e59-1aae53df8a1b"
      },
      "source": [
        "pd.concat([df['Losses'], df['VaR'].shift(1)], axis=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Losses   VaR\n",
              "Month              \n",
              "Jan        10   NaN\n",
              "Feb        11   9.5\n",
              "Mar        12   9.0\n",
              "Apr        11   8.5\n",
              "May        10   9.0\n",
              "Jun         9   9.5\n",
              "Jul        11  10.5\n",
              "Aug        13   9.0\n",
              "Sep        11  10.0\n",
              "Oct         9  11.0\n",
              "Nov         8  12.0\n",
              "Dec         9  11.0"
            ],
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Losses</th>\n",
              "      <th>VaR</th>\n",
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              "    <tr>\n",
              "      <th>Month</th>\n",
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              "  <tbody>\n",
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              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>11</td>\n",
              "      <td>9.5</td>\n",
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              "      <th>Mar</th>\n",
              "      <td>12</td>\n",
              "      <td>9.0</td>\n",
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              "      <td>8.5</td>\n",
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              "      <th>May</th>\n",
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              "      <td>9</td>\n",
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              "      <td>11</td>\n",
              "      <td>10.5</td>\n",
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              "    <tr>\n",
              "      <th>Aug</th>\n",
              "      <td>13</td>\n",
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              "      <td>11</td>\n",
              "      <td>10.0</td>\n",
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              "    <tr>\n",
              "      <th>Oct</th>\n",
              "      <td>9</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nov</th>\n",
              "      <td>8</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dec</th>\n",
              "      <td>9</td>\n",
              "      <td>11.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c9088b98-0041-40b6-949a-08e2b032bd19 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c9088b98-0041-40b6-949a-08e2b032bd19');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0czfaz2tv4G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "535ad1c2-ee17-47e1-eb92-4ddf5d0a8097"
      },
      "source": [
        "hits = df['Losses'] > df['VaR'].shift(1)\n",
        "# this is bad... We don't have an observation for Jan!!!\n",
        "hits"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month\n",
              "Jan    False\n",
              "Feb     True\n",
              "Mar     True\n",
              "Apr     True\n",
              "May     True\n",
              "Jun    False\n",
              "Jul     True\n",
              "Aug     True\n",
              "Sep     True\n",
              "Oct    False\n",
              "Nov    False\n",
              "Dec    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VW1SzrqYtv01",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "588ad149-3b85-4eeb-caac-0894443e157e"
      },
      "source": [
        "n = hits.sum()\n",
        "N = hits.size\n",
        "N, n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7mgv4actvr9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4f476cc-1998-423e-f1d7-1beced417ea8"
      },
      "source": [
        "df['VaR'] = df['VaR'].shift(1)\n",
        "df.dropna(inplace=True)\n",
        "hits = df['Losses'] > df['VaR']\n",
        "n = hits.sum()\n",
        "N = hits.size\n",
        "N, n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    }
  ]
}