Original link:http://tecdat.cn/?p=23130
The goal of this paper is to create a synthetic VIX that 1) reflects the VIX as closely as possible when applied to the S&P 500; 2) Relying entirely on price as input, so it can be applied to any market index.
The solution is a synthetic volatility index. \> Mov(ATR(1)/C,20,S)
Now I’m going to try the code.
# * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # load historical data #***************************************************************** tickers = 'SP=^GSPC,VIX=^VIX' # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # drawing data #***************************************************************** layout(1:3) plot(SP) plot.legend('SP',SP)
matplot(scale(temp)# * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # test strategy #***************************************************************** vol= SMA( SP),1/ Cl), 20 ) high= vol > SMA(vol, 40) low= vol< SMA(vol, 40) plot(SP\[index\], type='l', plotX=F, x.highlight = highlight)
# * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # test strategy #***************************************************************** models = list() data$weight\[\] = NA run(data) # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # report #***************************************************************** #performance(models, T)
This estimate is similar to other volatility estimates provided by the TTR package.
print(cor(, use='complete.obs',method='pearson'))
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