1 / 10

Forecasting Implied Volatility

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

hagen
Download Presentation

Forecasting Implied Volatility

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


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

  2. 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

  3. 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

  4. 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%

  5. Regression Analysis Summary Statistics

  6. 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%

  7. 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%

  8. Final Model Results

  9. 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

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

More Related