#' VAR_fore_Par
#'
#' @description Forecast oneday ahead VAR and backtest
#'
#' @param Period xts object of log returns or other risk measures
#' @param foreLength length of forecast
#' @param windowLength length of window used for fitting
#' @param outDir Output dir
#' @param version Version of dataset (if run multipe times and want to save each data)
#' @param Name Name of asset (colname)
#' @param tickers Names of columns
#'
#' @return Write to files position each day and its cumulative return
#' @export
#'
VAR_fore_Par <- function(Period = Period, foreLength = foreLength, windowLength = windowLength,
outDir = "./output/", version = "v1",
Name = NULL, tickers = tickers) {
Index = zoo::index(Period[(nrow(Period)-foreLength):nrow(Period),])
no_cores <- parallel::detectCores()
cl <- parallel::makeCluster(no_cores)
parallel::clusterEvalQ(cl, library(dplyr))
forecastsVAR <- do.call(rbind, parallel::parLapply(cl, 0:foreLength, for_VAR_Par,
returnsVAR = Period, wL = windowLength)) %>%
dplyr::as_tibble()
stopCluster(cl)
readr::write_csv(forecastsVAR, path=paste0(outDir, "forecasts_VAR_", version,".csv"))
fore_VAR <- readr::read_csv(file=paste0(outDir, "forecasts_VAR_", version,".csv"))
colnames(fore_VAR) <- paste0(tickers,".pos")
#### Univariate returns ####
if(!is.null(Name)) {
for_VAR <- fore_VAR %>%
dplyr::transmute(Date = lubridate::as_date(Index),
pos = as.numeric(pull(fore_VAR, paste0(Name, ".pos"))))
for_VARxts <- xts::xts(for_VAR$pos[1:(nrow(for_VAR)-1)],
order.by = for_VAR$Date[1:(nrow(for_VAR)-1)])
# Create the ARIMA+GARCH returns
Intersect_VAR = merge(for_VARxts, Period[,Name], all = FALSE)
ArimaGarchReturns_VAR = Intersect_VAR[,1] * Intersect_VAR[,2]
# Create the backtests for ARIMA+GARCH and Buy & Hold
ArimaGarchCurve_VAR = exp( cumsum( ArimaGarchReturns_VAR )) - 1
BuyHoldCurve_VAR = exp( cumsum( Intersect_VAR[,2] )) - 1
CombinedCurve_VAR = merge( ArimaGarchCurve_VAR, BuyHoldCurve_VAR, all=F ) %>%
dplyr::as_tibble() %>%
dplyr::mutate(Date = zoo::index(BuyHoldCurve_VAR))
readr::write_csv(CombinedCurve_VAR, path=paste0(outDir, "forecasts_VAR_Uni_", version,".csv"))
}
#### Multivariate returns ####
for_VAR <- fore_VAR %>%
dplyr::mutate(Date = lubridate::as_date(Index))
for_VAR[1:(nrow(for_VAR)-1), 1:(ncol(for_VAR)-1)]
for_VARxts <- xts::xts(for_VAR[1:(nrow(for_VAR)-1), 1:(ncol(for_VAR)-1)],
order.by = for_VAR$Date[1:(nrow(for_VAR)-1)])
ArimaGarchReturns_VAR = xts::xts(rowSums(for_VARxts * Period[zoo::index(for_VARxts)] * (1/ncol(for_VARxts))),
order.by = zoo::index(for_VARxts))
BuyHoldReturns_VAR <- xts::xts(rowSums(Period[zoo::index(for_VARxts)] * (1/ncol(for_VARxts))),
order.by = zoo::index(for_VARxts))
# Create the backtests for ARIMA+GARCH and Buy & Hold
ArimaGarchCurve_VAR = exp( cumsum( ArimaGarchReturns_VAR )) - 1
BuyHoldCurve_VAR = exp( cumsum( BuyHoldReturns_VAR )) - 1
CombinedCurve_VAR = merge( ArimaGarchCurve_VAR, BuyHoldCurve_VAR, all=F ) %>%
dplyr::as_tibble() %>%
dplyr::mutate(Date = zoo::index(BuyHoldCurve_VAR))
readr::write_csv(CombinedCurve_VAR, path=paste0(outDir, "forecasts_VAR_Mul_", version,".csv"))
}
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