Volatility Strategy

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Volatility Risk Premium (VRP)

A quick test of the results presented at:

• DDNs Volatility Risk Premium Strategy Revisited (2)
• Volatility Risk Premium Trading Volatility (Part I)
• Volatility Risk Premium: Sharpe 2+, Return to Drawdown 3+
• Chasing the Volatility Risk Premium
• Easy Volatility Investing by Tony Cooper at Double-Digit Numerics
• MarketSci VIX & Volatility
  • Free Historical VXX Data
    • Bloomberg
    • Reconstruct using CBOE futures
    • CBOE VIX Futures Historical Market Data
    • QMAO getSymbols.cfe() method that will do the adjustment for you by default (rescale=TRUE)
• How Does the CBOEs VIX Index Work?

• Volatility ETP

• Reconstructing VXX from CBOE futures data > Do you have equivalent code to recreate historical VXZ data? > Just read the VXZ brochure and adjust the calculation accordingly

How to calculate the VXX price and how does backwardation and contango influence it

Paid Data:

Products

Strtegies:

A Failed Attempt at Backtesting Structural Arbitrage Trading VXX with nearest neighbors prediction

Helmuth Vollmeier You can download a “long” XIV and “long” VXX back to the start of VIX futures 2004 from my Dropbox which is updated daily with CSI data via a small R-script. VXX: https://dl.dropboxusercontent.com/s/950x55x7jtm9x2q/VXXlong.TXT XIV: https://dl.dropboxusercontent.com/s/jk6der1s5lxtcfy/XIVlong.TXT

Ilya , Samuel update: ZIV & VXZ, reconstructed according to the method outlined in their prospectus VXZ: https://www.dropbox.com/s/y3cg6d3vwtkwtqx/VXZlong.TXT ZIV: https://www.dropbox.com/s/jk3ortdyru4sg4n/ZIVlong.TXT

Frank says:The rationale behind is very easy: Sell (volatiity) risk when there is fear in the market: (implied volatility overestimates realized volatility), and vice versa. Even the most simple strategy based on that concept: Go long XIV at todays close if the VIX index will close below the front month VIX futures contract, or go long VXX if it will close above. Hold until a change in position. came up with 50+% annualized returns over the course of the last 10 years (including the financial crisis and the most recent bull market). See http://volatilitymadesimple.com/backtest-comparing-the-vix-index-to-front-month-vix-futures/

Instruments:

• VIX - CBOE Volatility Index
• XIV (inverse volatility)(inverse 1-month VIX) - VelocityShares Daily Inverse VIX Short-Term ETN
• VXX (long volatility)(long 1-month VIX) - iPath S&P 500 VIX Short-Term Futures
• VXZ - iPath S&P 500 VIX Mid-Term Futures

Strategy rules:

• At the close, calculate the following:
  • the 5-day average of the [VIX index (10 day historical volatility of SPY * 100)]. Note that historical volatility is based on the natural log of each days % change.
• Go long XIV at the close when the result of the above formula is greater than 0, otherwise go long VXX. Hold until a change in position

Based on Reconstructing VXX from CBOE futures data , I was able to reconstruct VXX from CBOE futures. I made a helper function reconstruct.VXX.CBOE to show steps needed to reconstruct VXX. I also made another helper function, cboe.volatility.term.structure.SPX, to download an plot VIX Term Structure.

For comparison I used historical VXX data from Free Historical VXX Data >1) From the raw VX contract data, there are 33 Month-2 data values that have missing values, How did you fill those values? I filled them by using the same Month-2 to Month-1 ratio as the first subsequent non-missing value.

I just put NA’s

  #*****************************************************************
  # Load historical data
  #****************************************************************** 
  library(SIT)
  load.packages('quantmod')
  tickers = spl('VXX')    
                 
  data <- new.env()
getSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, set.symbolnames = F, auto.assign = T)

load.packages('gdata,data.table')

Next let’s load historical data

  # load data from [Free Historical VXX Data](https://marketsci.wordpress.com/2012/04/18/free-historical-vxx-data/)  
  temp = read.xls('data/VXX.20120416.xlsx', sheet=1, header=T,perl=perl)
  data$VXX.HIST = make.stock.xts(make.xts(temp$VXX, as.POSIXct(temp$DATE)))
    
  data$VXX.LONG = read.xts("data/VXXlong.TXT", format='%Y-%m-%d' )
data$VXX.LONG$Adjusted = data$VXX.LONG$Close
    
  # reconstruct VXX
  temp = reconstruct.VXX.CBOE()  
  data$VXX.CBOE = make.stock.xts(temp$VXX)

print(bt.start.dates(data))

    proxy.test(data)    

    proxy.test(data, spl('VXX.HIST,VXX.CBOE,VXX.LONG'))    

    proxy.test(data, spl('VXX,VXX.CBOE,VXX.LONG'))    

    proxy.test(data, spl('VXX,VXX.CBOE'))    

    plota(temp$VXX,type='l',lwd=2, main='VXX reconstructed from CBOE futures')

So we can use following VXX + VXX.LONG to extend VXX in getSymbols.extra

  tickers = spl('VXZ')    
                 
  data <- new.env()
getSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, set.symbolnames = F, auto.assign = T)
  
  # reconstruct VXZ
  temp = reconstruct.VXX.CBOE()  
  data$VXZ.CBOE = make.stock.xts(temp$VXZ)
  
  data$VXZ.LONG = read.xts("data/VXZlong.TXT", format='%Y-%m-%d' )
data$VXZ.LONG$Adjusted = data$VXZ.LONG$Close
  
  
print(bt.start.dates(data))

    proxy.test(data) 

    proxy.test(data, spl('VXZ.CBOE,VXZ.LONG'))       

plota(temp$VXZ,type='l',lwd=2, main='VXZ reconstructed from CBOE futures')  

So we can use following VXZ + VXZ.LONG to extend VXZ in getSymbols.extra

Finally let’s plot VIX Term Structure.

    print(cboe.volatility.term.structure.SPX())

(this report was produced on: 2014-12-07)