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I have a time series of S&P500 prices, for which I have calculated log-returns and roll-volatility. My goal is to forecast daily realized volatility and test a straddle strategy based on it (I have the full option chain time series on the same future underlying).

For the purpose, I am pondering 4 different R libraries. Being new to R, for all of them I would like to have the whole series of fitted values in a list, same for forecast ones.

These are the specs:

  1. Stochvol::
    res <- svsample(op$ret, priormu = c(-10, 1), priorphi = c(20, 1.1), priorsigma = 0.1) 
    pred <- predict(res, 2)
  1. rugarch::
spec <- ugarchspec(mean.model = list(armaOrder = c(1, 1)), variance.model = list(model = 'eGARCH', garchOrder = c(2, 1)), distribution = 'nig')
forecast <- ugarchforecast(spec, data = op$ret)
  1. fGarch::
fit2=garchFit(~ garch(1,1), data = op$ret, include.mean=FALSE, trace=F)
  1. tseries::
fit1=garch(op$ret, order = c(1, 1), control = garch.control(trace = F))
predict(fit1, n.ahead=1, doplot=F)

Do you guys can help? Many thanks

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  • $\begingroup$ What means "roll volatilities"? Is same as "rolling volatility" (historical vol calculated over overlapping intervals of time (of length T days each) ? $\endgroup$ – Alex C Mar 27 at 2:17
  • $\begingroup$ Yes, Alex, rolling volatility where T=5 in my case $\endgroup$ – VitoC Mar 27 at 7:26

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