Forecasting Vix

Degiannakis, Stavros (2008): Forecasting Vix. Published in: Journal of Money, Investment and Banking No. 4 (2008): pp. 5-19.

Abstract

Implied volatility index of the S&P500 is considered as a dependent variable in a fractionally integrated ARMA model, whereas volatility measures based on interday and intraday datasets are considered as explanatory variables. The next trading day’s implied volatility forecasts provide positive average daily profits. All the forecasting information is provided by the VIX index itself. There is no incremental predictability from both realized volatility computed from intraday data and conditional volatility extracted from an Arch model. Hence, neither the interday volatility nor the use of intraday data yield any added value in forecasting the S&P500 implied volatility index. However, an agent cannot utilize VIX predictions in creating abnormal returns in implied volatility futures market.

• Item Type: MPRA Paper
• Language: English
• Keywords: ARCH, ARFIMAX, Fractional Integration, Volatility Forecasting, VIX Index
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets

References

Andersen, T. and Bollerslev, T. (1998). "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts." International Economic Review, 39, 885-905.

Andersen, T., Bollerslev, T., Diebold, F.X. and Ebens, H. (2001). "The Distribution of Realized Stock Return Volatility." Journal of Financial Economics, 61, 43-76.

Angelidis, T., Benos, A. and Degiannakis, S. (2004). "The Use of GARCH Models in VaR Estimation." Statistical Methodology, 1(2), 105-128.

Blair, B.J., Poon, S-H and Taylor S.J. (2001). "Forecasting S&P100 Volatility: The Incremental Information Content of Implied Volatilities and High-Frequency Index Returns." Journal of Econometrics, 105, 5-26.

Brooks, C. and Burke, S.P. (2003). "Information Criteria for GARCH Model Selection." European Journal of Finance, 9, 557-580.

Degiannakis, S. and Xekalaki, E. (2007). "Assessing the Performance of a Prediction Error Criterion Model Selection Algorithm in the Context of ARCH Models." Applied Financial Economics, 17, 149-171.

Diebold, F.X. and Mariano, R. (1995). "Comparing Predictive Accuracy." Journal of Business and Economic Statistics, 13(3), 253-263.

Ebens, H. (1999). "Realized Stock Volatility." Department of Economics, Johns Hopkins University, Working Paper, 420.

Engle, R.F. (1982). "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation." Econometrica, 50, 987-1008.

Engle, R.F. and Sun, Z. (2005). "Forecasting Volatility Using Tick by Tick Data." European Finance Association, 32th Annual Meeting, Moscow.

Fleming, J., Ostdiek, B. and Whaley, R.E. (1995). "Predicting Stock Market Volatility: A New Measure." Journal of Futures Markets, 15, 265-302

Giot, P. and Laurent, S. (2004). "Modelling Daily Value-at-Risk Using Realized Volatility and ARCH Type Models." Journal of Empirical Finance, 11, 379-398.

Glosten, L., Jagannathan, R. and Runkle, D. (1993). "On the Relation between Expected Value and the Volatility of the Nominal Excess Return on Stocks." Journal of Finance, 48, 1779-1801.

Granger, C.W.J. (1980). "Long Memory Relationships and the Aggregation of Dynamic Models." Journal of Econometrics, 14, 227-238.

Granger, C.W.J. and Joyeux, R. (1980). "An Introduction to Long Memory Time Series Models and Fractional Differencing." Journal of Time Series Analysis, 1, 15-39.

Hansen, P.R. and Lunde, A. (2005). "A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data." Journal of Financial Econometrics, 3(4), 525-554.

Koopman, S.J., Jungbacker, B. and Hol, E. (2005). "Forecasting Daily Variability of the S&P100 Stock Index Using Historical, Realised and Implied Volatility Measurements. Journal of Empirical Finance, 12(3), 445-475.

Latane, H.A. and Rendleman, R.J. (1976). "Standard Deviations of Stock Price Ratios Implied in Option Prices." Journal of Finance, 31, 369-381.

Martens, M. (2002). "Measuring and Forecasting S&P500 Index-Futures Volatility Using High-Frequency Data." Journal of Futures Markets, 22, 497-518.

Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis, Chapman and Hall.

Thomakos, D.D. and Wang, T. (2003). "Realized Volatility in the Futures Markets." Journal of Empirical Finance, 10(3), 321-353.

Whaley, R.E. (1993). "Derivatives on Market Volatility: Hedging Tools Long Overdue." Journal of Derivatives, 1, 71-84.

Zhang, L., Mykland, P.A. and Ait-Sahalia, Y. (2003). "A Tale of Two Time Scales: Determining Inttegrated Volatility with Noisy High-Frequency Data." National Bureau of Economic Research, Working Paper, 10111.