R/HedgeRatio.R

In GabauerDavid/ConnectednessApproach: Connectedness Approach

#' @title Kroner and Sultan (1993) hedge ratios
#' @description This function calculates the hedge ratios of Kroner and Sultan (1993)
#' @param x zoo return matrix (in percentage)
#' @param H Residual variance-covariance, correlation or pairwise connectedness matrix
#' @param method Cumulative sum or cumulative product
#' @param statistics Hedging effectiveness statistic
#' @param metric Risk measure of Sharpe Ratio (StdDev, VaR, or CVaR)
#' @param digit Number of decimal places
#' @return Get hedge ratios
#' @importFrom zoo zoo
#' @importFrom zoo index
#' @importFrom PerformanceAnalytics SharpeRatio
#' @importFrom PerformanceAnalytics Return.annualized
#' @importFrom PerformanceAnalytics StdDev.annualized
#' @examples
#' \donttest{
#' data("g2020")
#' fit = VAR(g2020, configuration=list(nlag=1))
#' hr = HedgeRatio(g2020/100, fit$Q)
#' hr$TABLE
#' }
#' @references
#' Kroner, K. F., & Sultan, J. (1993). Time-varying distributions and dynamic hedging with foreign currency futures. Journal of Financial and Quantitative Analysis, 28(4), 535-551.
#' 
#' Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157-170.
#' 
#' Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2020). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762.
#' @author David Gabauer
#' @export
HedgeRatio = function (x, H, method = c("cumsum", "cumprod"), statistics = c("Fisher", "Bartlett", "Fligner-Killeen", "Levene", "Brown-Forsythe"), metric="StdDev", digit = 2) {
  method = match.arg(method)
  statistics = match.arg(statistics)
  if (!is(x, "zoo")) {
    stop("Data needs to be of type 'zoo'")
  }
  k = ncol(x)
  t = nrow(x)
  date = as.character(rownames(x))
  NAMES = colnames(x)
  if (length(dim(H)) == 2) {
    H = array(H, c(k, k, 1))
  }
  if (dim(H)[[3]] == 1) {
    H = array(H, c(k, k, t), dimnames = list(NAMES, NAMES, date))
  }
  HR = array(NA, c(k, k, t), dimnames = list(NAMES, NAMES, date))
  for (i in 1:k) {
    for (j in 1:k) {
      HR[i, j, ] = H[i, j, ]/H[j, j, ]
    }
  }
  summary = NULL
  for (i in 1:k) {
    for (j in 1:k) {
      x_ = as.matrix(HR[i, j, ])
      summary_ = matrix(NA, nrow = ncol(x_), ncol = 4)
      for (ij in 1:ncol(x_)) {
        summary_[ij, ] = matrix(c(mean(x_[, ij]), 
                                  stats::sd(x_[,  ij]), 
                                  stats::quantile(x_[, ij], 0.05), 
                                  stats::quantile(x_[, ij], 0.95)), nrow = 1)
      }
      colnames(summary_) = c("Mean", "Std.Dev.", "5%","95%")
      rownames(summary_) = paste0(NAMES[i], "/", NAMES[j])
      summary = rbind(summary, summary_)
    }
  }
  
  ret = risk = SR = HE = pvalue = array(NA, c(k, k), dimnames = list(NAMES, NAMES))
  portfolio_return = cumulative_portfolio_return = array(NA, c(k, k, t), dimnames = list(NAMES, NAMES, date))
  for (i in 1:k) {
    for (j in 1:k) {
      portfolio_return[i, j, ] = as.numeric(x[, i] - HR[i, j, ] * x[, j])
      HE[j, i] = 1 - var(portfolio_return[i, j, ])/var(x[, i])
      df = rbind(data.frame(val = x[,i], group = "A"), data.frame(val = portfolio_return[i, j, ], group = "B"))
      z = zoo(portfolio_return[i, j, ], order.by=index(x))
      SR[j, i] = SharpeRatio(z, FUN=(metric), annualize=TRUE)
      ret[j, i] = Return.annualized(z)
      risk[j,i] = StdDev.annualized(z)
      pvalue[i, ] = VarianceTest(val ~ as.character(group), data = df, method=statistics)$p.value
      if (method == "cumsum") {
        cumulative_portfolio_return[i, j, ] = cumsum(portfolio_return[i, j, ])
      } else if (method == "cumprod") {
        cumulative_portfolio_return[i, j, ] = cumprod(1 + portfolio_return[i, j, ]) - 1
      }
    }
  }
  TABLE = cbind(summary, c(HE), c(pvalue), c(ret), c(risk), c(SR))
  TABLE = TABLE[-which(TABLE[, 1] == 1), ]
  colnames(TABLE) = c("Mean", "Std.Dev.", "5%", "95%", "HE", "p-value", "Return", "Std.Dev", "SR")
  return = list(TABLE = format(round(TABLE, digit), nsmall = digit), 
                hedge_ratio = HR, portfolio_return = portfolio_return, 
                cumulative_portfolio_return = cumulative_portfolio_return)
}