Merge pull request #35 from EchoRLiu/23-data-with-dates-issue

resolve importing, data dates, and other issues

NAMESPACE

@@ -3,7 +3,12 @@
 S3method(plot,robustGARCH)
 S3method(print,robustGARCH)
 S3method(summary,robustGARCH)
-export(.aValue)
-export(.expFisherI)
-export(aef)
 export(robGarch)
+importFrom(graphics,legend)
+importFrom(graphics,par)
+importFrom(stats,density)
+importFrom(stats,integrate)
+importFrom(stats,median)
+importFrom(stats,nlminb)
+importFrom(stats,pnorm)
+importFrom(xts,xts)

R/data.R

@@ -16,4 +16,5 @@
 #'
 #' @keywords datasets
 #' @source \url{https://finance.yahoo.com/quote/\%5EGSPC/history?period1=949363200&period2=1025222400&interval=1d&filter=history&frequency=1d}
+#' @importFrom xts xts
 "gspc"

R/robGarch.R

@@ -1,3 +1,5 @@
+#' @importFrom stats median pnorm density nlminb
+#' @importFrom graphics par
 #' @title Robust Estimates for GARCH(1,1) Model
 #'
 #' @name robGarch
@@ -59,9 +61,13 @@
 robGarch <- function(data, methods = c("BM", "M", "QML", "MLE"), fixed_pars = c(0.8, 3.0), 
                      optimizer = c("Rsolnp", "nloptr", "nlminb"), optimizer_x0 = FALSE, 
                      optimizer_control = list(trace=0), stdErr_method = c("numDeriv", "optim", "sandwich")){
-  # TODO: xts data takes a long time
-  if(!is.numeric(data) || length(data)==0)
-    stop("Data must be a numeric vector of non-zero length")
+
+  # if(!is.numeric(data) || length(data)==0)
+  #   stop("Data must be a numeric vector of non-zero length")
+
+  # the calculation will use data_, which only has the return data and without dates
+  # but we will keep data to retain the original dates
+  data_ = zoo::coredata(data) 
 
   methods = match.arg(methods)
   optimizer = match.arg(optimizer)
@@ -99,7 +105,7 @@ robGarch <- function(data, methods = c("BM", "M", "QML", "MLE"), fixed_pars = c(
     stop("use Rsolnp optimizer for now")
   }
 
-  std_errors <- sqrt(diag(abs(solve(H)/length(data))))
+  std_errors <- sqrt(diag(abs(solve(H)/length(data_))))
   if(methods == "MLE"){
     standard_error <- c(std_errors[2:4], std_errors[6])
     fit$observed_I <- -H[c(2,3,4,6), c(2,3,4,6)]
@@ -189,7 +195,7 @@ robGarchDistribution <- function(param = c(8.76e-04, 0.135, 0.686), methods = c(
     fixed <- param
     names(fixed) <- c("gamma", "alpha", "beta", "shape")
     fspec <- spec
-    setfixed(fspec) <- fixed
+    rugarch::setfixed(fspec) <- fixed
     y <- rugarch::ugarchpath(fspec, n.sim = n, m.sim = m, rseed = 42)
     y. <- y@path$seriesSim
 
@@ -221,7 +227,7 @@ robGarchDistribution <- function(param = c(8.76e-04, 0.135, 0.686), methods = c(
     fixed <- param
     names(fixed) <- c("gamma", "alpha", "beta")
     fspec <- spec
-    setfixed(fspec) <- fixed
+    rugarch::setfixed(fspec) <- fixed
     y <- rugarch::ugarchpath(fspec, n.sim = n, m.sim = m, rseed = 42)
     y. <- y@path$seriesSim
 
@@ -251,6 +257,10 @@ rgFit_local <- function(data, optimizer, optimizer_x0, optimizer_control, shared
   # unpack shared_vars
   methods <- shared_vars$methods
 
+  # for minimum code change, data_ and data naming are exchanged here
+  data_ = data # retain the dates and structure
+  data = zoo::coredata(data) # for calculation
+
   start_time <- Sys.time()
   # optimizer/optimizer_control
 
@@ -315,7 +325,7 @@ rgFit_local <- function(data, optimizer, optimizer_x0, optimizer_control, shared
 
   time_elapsed <- Sys.time() - start_time
 
-  list(data=data,
+  list(data=data_,
        methods = methods,
        optimizer=optimizer,
        optimizer_x0=res$x0,

R/robustGARCH-infomat.R

@@ -1,4 +1,5 @@
-#' @export
+# import methods from other packages
+#' @importFrom stats integrate
 .aValue <- function(fit, top = FALSE, v=5){
   if(top){
     # Calculate a(Psi_0, g), the top value of AEF, where Psi_0 = rho_0'.
@@ -158,11 +159,13 @@
 
   g
 }
-#' @export
 .expFisherI <- function(fit, true_pars){
 
   # Big difference between expected and observed Fisher Information matrix.
-  T <- length(fit$data)
+
+  data_ = zoo::coredata(fit$data)
+
+  T <- length(data_)
 
   if(fit$methods == 'MLE'){
     # to be added.
@@ -175,17 +178,17 @@
     # it seems like k is used here as an index
     # could any k was supposed to be the global one implemented before?
     for(k in 1:(T-1)){
-      FS[2,1] <- FS[2,1] + true_pars[3]^(k-1) * (fit$data[T-k])^2
+      FS[2,1] <- FS[2,1] + true_pars[3]^(k-1) * (data_[T-k])^2
     }
 
     FS[3,1] <- true_pars[1]/(1-true_pars[3])^2
     for(k in 1:(T-2)){
-      FS[3,1] <- FS[3,1] + true_pars[2]*k*true_pars[3]^(k-1)*(fit$data[T-k-1])^2
+      FS[3,1] <- FS[3,1] + true_pars[2]*k*true_pars[3]^(k-1)*(data_[T-k-1])^2
     }
 
     ht <- true_pars[1]/(1-true_pars[3])
     for(k in 1:(T-1)){
-      ht <- ht + true_pars[2] * true_pars[3]^(k-1) * (fit$data[T-k])^2
+      ht <- ht + true_pars[2] * true_pars[3]^(k-1) * (data_[T-k])^2
     }
   }
 

R/robustGARCH-plots.R

@@ -1,8 +1,10 @@
+#' @importFrom graphics legend par
+#' @importFrom stats density integrate median nlminb pnorm
 .plot.garchsim <- function(fit, digits = 3, estimation_pos = "topleft", line_name_pos = "topright", 
                            par_ = par(no.readonly = TRUE), pctReturn_ = TRUE, abs_ = TRUE, original_ = FALSE, main_name = "Conditional Volatility (vs |pctReturns(%)|)")
 {
 
-  n <- length(fit$data) + 100
+  n <- length(zoo::coredata(fit$data)) + 100
   a0 <- fit$fitted_pars["gamma"]
   a1 <- fit$fitted_pars["alpha"]
   b1 <- fit$fitted_pars["beta"]
@@ -21,11 +23,11 @@
   #}
   #sigma2 <- sigma2[101:n]
 
-  returns <- xts::xts(fit$data)
-  sigma2 <- xts::xts(sqrt(fit$sigma), order.by = zoo::index(returns))
+  returns <- zoo::coredata(fit$data)
+  sigma2 <- xts::xts(sqrt(fit$sigma), order.by = zoo::index(fit$data))
 
   if(original_){
-    zoo::plot.zoo(returns, type = "l", xlab = "", ylab = "Return", main = "Returns")
+    zoo::plot.zoo(fit$data, type = "l", xlab = "", ylab = "Return", main = "Returns")
   }
 
   if(pctReturn_){
@@ -34,14 +36,17 @@
   }
 
   if(abs_){
-    retAndVol <- cbind(abs(returns),sigma2)
-  } else{
-    retAndVol <- cbind(returns,sigma2)
+    retAndVol <- cbind(abs(returns), sigma2)
+  } else {
+    retAndVol <- cbind(returns, sigma2)
   }
 
+  # Ensure retAndVol retains the dates from fit$data
+  retAndVol <- xts::xts(retAndVol, order.by = zoo::index(fit$data))
+
   par_
-  zoo::plot.zoo(retAndVol, screens = "single", type = "l", xlab = "",ylab = "",main = main_name,
-                lty = c("dotted","solid"),col = c("blue","black"), lwd = c(.8,1.5))
+  zoo::plot.zoo(retAndVol, screens = "single", type = "l", xlab = "", ylab = "", main = main_name,
+                lty = c("dotted", "solid"), col = c("blue", "black"), lwd = c(.8, 1.5))
   exprss <- c(bquote(alpha[0]~'='~.(signif(a0, digits))),
               bquote(alpha[1]~'='~.(signif(a1, digits))),
               bquote(beta[1]~'='~.(signif(b1, digits))))

R/robustGARCH-summary.R

@@ -14,8 +14,7 @@
 #' @param pctReturn_ a logical argument. IF TRUE, the plot function will plot the returns in percentage instead of original. Default is TRUE.
 #' @param abs_ a logical argument, when TRUE, the plot function will plot abs(returns) with conditional standard deviation instead of returns, default to TRUE.
 #' @param ... # to be written
-#' @param nu # to be written
-#' @param v degrees of freedom in a Student's t-distribution.
+#' @param nu degrees of freedom in a Student's t-distribution.
 #'
 #' @name robustGARCH-summary
 #' @aliases summary.robustGARCH
@@ -31,7 +30,6 @@
 #' summary(fit)
 #' print(fit)
 #' plot(fit)
-#' aef(fit)
 #' 
 #' @export
 summary.robustGARCH <- function(object, digits = 3, ...){
@@ -45,6 +43,8 @@ summary.robustGARCH <- function(object, digits = 3, ...){
 #    The \usage entries must correspond to syntactically valid R code.
 #    See chapter ‘Writing R documentation files’ in the ‘Writing R
 #    Extensions’ manual.
+  fit <- object
+
   res <- rbind(round(fit$fitted_pars, digits), round(fit$standard_error, digits), round(fit$t_value, digits), round(fit$p_value, digits))
   colnames(res) <- names(fit$fitted_pars)
   rownames(res) <- c("Estimates", "Std. Errors", "t-statistic", "p-value")
@@ -75,6 +75,8 @@ summary.robustGARCH <- function(object, digits = 3, ...){
 #' @export
 print.robustGARCH <- function(x, digits = 3, ...){
 
+  fit <- x
+
   res <- rbind(round(fit$fitted_pars, digits))
   colnames(res) <- names(fit$fitted_pars)
   rownames(res) <- c("Estimates (Std. Errors)")
@@ -96,12 +98,12 @@ print.robustGARCH <- function(x, digits = 3, ...){
 #' @export
 plot.robustGARCH <- function(x, digits = 3, estimation_pos = "topleft", line_name_pos = "topright", par_ = par(no.readonly = TRUE), pctReturn_ = TRUE, abs_ = TRUE, original_ = FALSE, main_name = "Conditional Volatility (vs |pctReturns(%)|)", ...){
 
+  fit <- x
   .plot.garchsim(fit, digits, estimation_pos, line_name_pos, par_, pctReturn_, abs_, original_, main_name)
 
 }
 
 #' @rdname robustGARCH-summary
-#' @export
 aef <- function(fit, nu=5){
 
   aTop <- .aValue(fit, TRUE, nu)