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Optimal Prediction Periods for New and Old Volatility Indexes in USA and German Markets

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In 1993, the Chicago Board of Options Exchange (CBOE) introduced the VXO, a volatility index based on implied volatilities on S&P 100 index. In 2003, the CBOE changed their volatility index design and introduced the VIX in order to enhance its economic significance and to facilitate hedging. In this paper, using data from the USA and the German stock markets, we compare the forecasting capability of the volatility indexes with that of historical volatility and conditional volatility models. Following this analysis, we have studied whether it may be the case that volatility indexes forecast the realized volatilities more accurately for a different period to 30 (or 45) days, attempting to answer the question: what time horizon is the informational content of volatility indexes best adjusted for? The optimal prediction period of each volatility index (VXO, VIX, VDAX and V1X) in terms of coefficient of determination is analysed. The results identify a difference between the observed optimal forecasting period and the theoretical one. This could be explained from different perspectives such as the index’s design, investor cognitive bias or overreaction.

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  1. More information about VIX can be obtained at

  2. More information about VDAX and VDAX-NEW can be obtained at

  3. Traditionally a constant period of 22 trading days is used, but in this paper, the exact number of trading days for each of the 30 calendar-day period is used.


  • Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: Yes standard volatility models do provide accurate forecasts. International Economic Review, 39, 885–905.

    Article  Google Scholar 

  • Andersen, T. G., Bollerslev, T., Diebold, F. X., & Ebens, H. (2001). The distribution of realized stock return volatility. Journal of Financial Economics, 61, 43–76.

    Article  Google Scholar 

  • Bird, R., & Yeung, D. (2012). How do investors react under uncertainty? Pacific-Basin Finance Journal, 20(2), 310–327.

    Article  Google Scholar 

  • Blair, B. J., Poon, S. H., & Taylor, S. J. (2001). Forecasting S&P 100 volatility: The incremental information content of implied volatilities and high-frequency index returns. Journal of Econometrics, 105, 5–26.

    Article  Google Scholar 

  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307–327.

    Article  Google Scholar 

  • Brenner, M., & Galai, D. (1989). New financial instruments for hedging changes in volatility. Financial Analysts Journal, 45(4), 61–65.

    Article  Google Scholar 

  • Canina, L., & Figlewski, S. (1993). The informational content of implied volatility. Review of Financial Studies, 6(3), 659–681.

    Article  Google Scholar 

  • Carr, P., & Wu, L. (2006). A tale of two indexes. Journal of Derivatives, 13(3), 13–29.

    Article  Google Scholar 

  • Christensen, B. J., & Prabhala, N. R. (1998). The relation between implied and realized volatility. Journal of Financial Economics, 50, 125–150.

    Article  Google Scholar 

  • Christoffersen, P., Heston, S., & Jacobs, K. (2013). Capturing option anomalies with a variance-dependent pricing kernel. The Review of Financial Studies, 26(8), 1963–2006.

    Article  Google Scholar 

  • Chung, S. L., Tsai, W. C., Wang, Y. H., & Weng, P. S. (2011). The information content of the S&P 500 index and VIX options on the dynamics of the S&P 500 index. Journal of Futures Markets, 31(12), 1170–1201.

    Article  Google Scholar 

  • Cox, J. C., & Rubinstein, M. (1985). Options markets (Vol. 340). Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Day, F., & Lewis, C. M. (1992). Stock market volatility and the information content of stock index options. Journal of Econometrics, 52(1–2), 267–287.

    Article  Google Scholar 

  • Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13, 253–263.

    Google Scholar 

  • Eraker, B., Johannes, M., & Polson, N. (2003). The impact of jumps in volatility and returns. The Journal of Finance, 58(3), 1269–1300.

    Article  Google Scholar 

  • Fleming, J., Ostdiek, B., & Whaley, R. E. (1995). Predicting stock market volatility: A new measure. The Journal of Futures Markets, 15(3), 265–302.

    Article  Google Scholar 

  • Glosten, L., Jagannathan, R., & Runkle, D. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48, 1779–1802.

    Article  Google Scholar 

  • Harvey, D. I., Leybourne, S. J., & Newbold, P. (1998). Tests for forecast encompassing. Journal of Business & Economic Statistics, 16(2), 254–259.

    Google Scholar 

  • Lehnert, T., Lin, Y. & Martelin, N. (2013). Steis’s Overreaction Puzzle: Option Anomaly or Perfectly Rational Behavior?. LSF Research Working Paper Series, n\(^{o}\) (pp. 13–11).

  • Luo, X., & Zhang, J. E. (2012). The term structure of VIX. Journal of Futures Market, 32(12), 1092–1123.

    Article  Google Scholar 

  • Newey, W. K., & West, K. D. (1987). A simple, positive semi-definitive, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55, 703–708.

    Article  Google Scholar 

  • Poteshman, A. M. (2001). Underreaction, overreaction, and increasing misrreaction to information in the options market. Journal of Finance, 56(3), 851–876.

    Article  Google Scholar 

  • Rosillo, R., Giner, J., & De la Fuente, D. (2014). The effectiveness of the combined use of VIX and Support Vector Machines on the prediction of S&P500. Neural Computing and Applications, 25(2), 321–332.

    Google Scholar 

  • Stein, J. (1989). Overreactions in the options market. Journal of Finance, 4(4), 1011–1023.

    Article  Google Scholar 

  • Whaley, R. E. (1993). Derivatives on market volatility: Hedging tools long overdue. The Journal of Derivatives, 1(1), 71–84.

    Article  Google Scholar 

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We would like to thank the anonymous reviewers and the editor for their suggestions and comments. Sandra Morini: Financial support granted by the Spanish Ministry of Economy and Competitiveness (ECO2011-23189).

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Correspondence to Javier Giner.

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Giner, J., Morini, S. & Rosillo, R. Optimal Prediction Periods for New and Old Volatility Indexes in USA and German Markets. Comput Econ 47, 527–549 (2016).

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