Forecasting Implied Volatility Alpha Asset Management Roger Kramer Brian Storey Matt Whalley Kristen Zolla

Objective and Methodology  Objective Develop a model that forecasts the CBOE Volatility Index (VIX)  Methodology Sampling frequency – weekly Extensive variable development Sample size – 313 observations Two 50-week holdout samples

Regression Model  Predictive Variables VIX Level, Lag 1  Intuition: Mean reversion  Negative correlation Change in 10-Yr U.S. Treasury Yield, Lag 2  Intuition: “Flight to quality” precedes equity market volatility  Negative correlation Change in S&P 100 (if positive), Lag 1  Intuition: Volatility affected by momentum effect of equity market  Positive correlation

Final Model: Out-of-Sample Performance  First out-of-sample 213-week sample; 50-week holdout for validation Correct direction forecast: 64%  Second out-of-sample 263-week sample; 50-week holdout for validation Correct direction forecast: 60%

Regression Analysis Summary Statistics

Final Model Results  Trading Strategy #1 Continuous trading – long or short every week Correct direction forecast: 57.5%  VIX % Change > 0: 72.1%  VIX % Change < 0: 43.4% Mean return (weekly): 3.89%  Mean return (winning weeks): 11.26%  Mean return (losing weeks): -6.14% Standard deviation (weekly): 11.82% Cumulative return (10/95 – 1/03): 2,300,000%

Final Model Results  Trading Strategy #2 Trade if absolute forecasted change in VIX exceeds 5% Trade in 73 of 313 weeks (23.3% of the time) Correct direction forecast: 63.0% Mean return if trading (weekly): 5.95% Mean return overall (weekly): 1.39% Standard deviation (weekly): 5.73% Cumulative return (10/95 – 1/03): only 4,591%

Final Model Results

Issues and Recommendations  VIX is Not Tradable Develop an options trading strategy using VIX forecasts; i.e. buy/sell OEX straddles Examine the effects of transactions costs  Naive Entry/Exit Signals Double moving average crossovers Various thresholds for forecasted VIX changes

Conclusions  Our simple model predicts VIX direction with reasonable precision  With further research, similar model could be used for profitable trading