Interesting volatility measurement, part 2
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A few weeks ago I have mentioned about an interesting volatility prediction. It is based on two periods of historical volatility (standard deviation). The remaining question was – does it really works? I could not give the answer, because I didn’t have VIX futures data at that time. Later on, I was contacted by Brian G. Peterson, who provided necessary data to finish this test. By the way, I just found, that CBOE shares VIX futures data on its website.
Now I want you to show, what are returns of VIX futures for the next 3 days, then historical volatility ratio of 3 days vs 10 days is less than 0.25:
Sys.setenv(TZ="GMT")
require('xts')
require('quantmod')
require('blotter')
require('PerformanceAnalytics')
tmp<-as.matrix(read.table('tickers/various_day_close/VIXc1.csv',sep=',',header=TRUE))
vix<-as.xts(as.double(tmp[,9]),order.by=as.POSIXct(strptime(tmp[,2],'%d-%b-%Y'),tz='GMT'))
vix<-(vix[!is.na(vix)])
colnames(vix)<-c('Close')
tmp<-as.matrix(read.table('tickers/various_day_close/ESc1.csv',sep=',',header=TRUE))
es<-as.xts(as.double(tmp[,9]),order.by=as.POSIXct(strptime(tmp[,2],'%d-%b-%Y')))
es<-(es[!is.na(es)])
colnames(es)<-c('Close')
#-----------------data end-----------------
#-----------------signal-------------------
es.delta<-Delt(Cl(es))
delta<-Delt(Cl(vix))#Front contract
#Historical volatility during 3 and 10 days
short.vol<-as.xts(rollapply(es.delta,3,sd,align='right'))
long.vol<-as.xts(rollapply(es.delta,10,sd,align='right'))
past.vol<-short.vol/long.vol
future.vol<-lag(past.vol,-3)
future.delta<-lag(vix,-3)/vix-1
signal<-ifelse(past.vol<0.25,1,0)
#here we see, increase in historical volatility
summary(as.double(future.vol[index(signal[signal!=0])]))/summary(as.double(past.vol[index(signal[signal!=0])]))
#-----------------signal end-------------------
#--------------blotter code------------------
symbols<-c('vix')
initDate=time(get(symbols)[1])
initEq=50000
rm(list=ls(envir=.blotter),envir=.blotter)
ltportfolio='volatility'
ltaccount='volatility'
initPortf(ltportfolio,symbols, initDate=initDate)
initAcct(ltaccount,portfolios=c(ltportfolio), initDate=initDate,initEq=initEq)
currency("USD")
stock(symbols[1],currency="USD")
signal<-signal[index(vix)]
signal[is.na(signal)]<-0
counter<-0 #date counter - exit on 3th day
for(i in 2:length(signal))
{
currentDate= time(signal)[i]
equity = initEq #getEndEq(ltaccount, currentDate)
position = getPosQty(ltportfolio, Symbol=symbols[1], Date=currentDate)
print(position)
print(currentDate)
if(position==0 &counter==0)
{
#open a new position if signal is >0
if(signal[i]>0)
{
print('open position')
closePrice<-as.double(get(symbols[1])[currentDate])
print(closePrice)
unitSize = as.numeric(trunc((equity/closePrice)))
print(unitSize)
commssions=-unitSize*closePrice*0.0003
addTxn(ltportfolio, Symbol=symbols[1], TxnDate=currentDate, TxnPrice=closePrice, TxnQty = unitSize , TxnFees=commssions, verbose=T)
counter<-1
}
}
else
{
#position is open. If signal is 0 - close it.
if(position>0 & as.integer(signal[i])==0 &counter>=3)
{
position = getPosQty(ltportfolio, Symbol=symbols[1], Date=currentDate)
closePrice<-as.double(get(symbols[1])[currentDate])#as.double(get(symbols[1])[i+100])
commssions=-position*closePrice*0.0003
addTxn(ltportfolio, Symbol=symbols[1], TxnDate=currentDate, TxnPrice=closePrice, TxnQty = -position , TxnFees=commssions, verbose=T)
counter<-0
}
else
counter<-counter+1
}
print('>>>>>>>>>>>>')
updatePortf(ltportfolio, Dates = currentDate)
updateAcct(ltaccount, Dates = currentDate)
updateEndEq(ltaccount, Dates = currentDate)
}
rez1<-(getPortfolio(ltaccount))
#--------------blotter code end------------------
#----------------results------------------------
png('vix_front.png',width=650)
#net profit - commissions, slipage excluded
chart.TimeSeries(cumsum(rez1$symbols$vix$txn[,7]),main='VIX front contract')
dev.off()
#----------------results end------------------------ |
The graph shows, that this strategy is pure random or just follows VIX index. Now let’s see, what are returns of this strategy, if S&P500 futures are used instead of VIX.
signal<-ifelse(past.vol<0.25,1,0)
#signal<-signal[index(es)]
#------------------------blotter code-----------------------
symbols<-c('es')
initDate=time(get(symbols)[1])
initEq=15000
rm(list=ls(envir=.blotter),envir=.blotter)
ltportfolio='volatility'
ltaccount='volatility'
initPortf(ltportfolio,symbols, initDate=initDate)
initAcct(ltaccount,portfolios=c(ltportfolio), initDate=initDate,initEq=initEq)
currency("USD")
future(symbols[1],currency="USD",multiplier=50,1/4)
signal[is.na(signal)]<-0
counter<-0
for(i in 2:length(signal))
{
currentDate= time(signal)[i]
equity = initEq #getEndEq(ltaccount, currentDate)
position = getPosQty(ltportfolio, Symbol=symbols[1], Date=currentDate)
print(position)
print(currentDate)
if(position==0 &counter==0)
{
#open a new position if signal is >0
if(signal[i]>0)
{
print('open position')
closePrice<-as.double(get(symbols[1])[currentDate])
print(closePrice)
unitSize = 1#as.numeric(trunc((equity/closePrice)))
print(unitSize)
commssions=-2
addTxn(ltportfolio, Symbol=symbols[1], TxnDate=currentDate, TxnPrice=closePrice, TxnQty = unitSize , TxnFees=commssions, verbose=T)
counter<-1
}
}
else
{
#position is open. If signal is 0 - close it.
if(position>0 & as.integer(signal[i])==0 &counter>=3)
{
position = getPosQty(ltportfolio, Symbol=symbols[1], Date=currentDate)
closePrice<-as.double(get(symbols[1])[currentDate])#as.double(get(symbols[1])[i+100])
commssions=-2
addTxn(ltportfolio, Symbol=symbols[1], TxnDate=currentDate, TxnPrice=closePrice, TxnQty = -position , TxnFees=commssions, verbose=T)
counter<-0
}
else
counter<-counter+1
}
updatePortf(ltportfolio, Dates = currentDate)
updateAcct(ltaccount, Dates = currentDate)
updateEndEq(ltaccount, Dates = currentDate)
}
rez1<-(getPortfolio(ltaccount))
#-------------------------results---------------------
#net profit
png('vix.png',width=650)
chart.TimeSeries(cumsum(rez1$symbols$es$txn[,9]),main='ES future contract')
dev.off() |
Well, that is exact opposite of expectations – if we expect volatility increase, as it was described in the first post, then the returns of S&P index have to be negative in long run.
From the beginning I suspected, that it has more to do with standard deviation formula and less with forecast.
Now funny part – I generated 2500 random returns and got median 0.9930 and mean 1.6360 for all days. Then I took all days, when buy signal suppose to be generated and guess what mean did I get? Median was 4.3170 and mean 6.3450. Once again, significant difference but on random data.
Source code on github
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