Exploiting the European Volatility Index Features: Anti-Persistence, Skewness, Kurtosis, and the Role of the Hurst Exponent
- 1 Department of Business and Law, School of Economics and Management, University of Siena, Siena, Italy
- 2 Department of Business and Law, School of Economics and Management, University of Siena, Siena, Italy
- 3 Department of Business and Economics, University of Cagliari, Cagliari, Italy
Abstract
Volatility indices are fundamental in the study of stock markets. In this paper, we analyzed the classical statistical characteristics of the main volatility index of the European stock markets (VStoxx) and evidenced some interesting connections and cause-effect relationships between the Hurst exponent and the moments of the distribution. Our results suggest that the market volatility is characterized by anti-persistence and mean reversion and that the Hurs t exponent variations seem to anticipate the variations of the other moments of the distribution such as skewness and kurtosis , so that the Hurst exponent variations can possibly signal near-term market reversals.
- Bagato, L., Gioia, A., & Mandelli, E. (2018). Reflexivity and Interactions in Modern Financial Markets: The Case of Volatility Indices. Rivista Internazionale di Scienze Sociali: Università Cattolica del Sacro Cuore, 3, 231-254.
- Barunik, J., & Kristoufek, L. (2010). On Hurst Exponent Estimation under Heavy-Tailed Distributions. Physica A: Statistical Mechanics and Its Applications, 389, 3844-3855. https://doi.org/10.1016/j.physa.2010.05.025
- Chow, V., Jiang, W., & Li, J. (2014). Does VIX Truly Measure Return Volatility? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2489345
- Engle, R. F., & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55, 251-276. https://doi.org/10.2307/1913236
- Fahling, E. J., Steurer, E., Ulbig, M., & Bamberger, B. (2019). Empirical Analysis of VDAX and VSTOXX as Major Volatility Indices in the EU Including Forecasting Tools. Journal of Financial Risk Management, 8, 315-332. https://doi.org/10.4236/jfrm.2019.84022
- Fleming, J., Ostdiek, B., & Whaley, R. E. (1995). Predicting Stock Market Volatility: A New Measure. Journal of Futures Markets, 15, 265-302. https://doi.org/10.1002/fut.3990150303
- Gatheral, J., Jaisson, T., & Rosenbaum, M. (2014). Volatility Is Rough. SSRN Electronic Journal. https://arxiv.org/pdf/1410.3394v1.pdf https://doi.org/10.2139/ssrn.2509457
- Giot, P. (2005). Relationships between Implied Volatility Indexes and Stock Index Returns. Journal of Portfolio Management, 26, 12-17. https://doi.org/10.3905/jpm.2005.500363
- Gonzalo, J., & Granger, C. (1995). Estimation of Common Long-Memory Components in Cointegrated Systems. Journal of Business & Economic Statistics, 13, 27-35. https://doi.org/10.1080/07350015.1995.10524576
- Iqbal, A. S. (2018). Volatility: Practical Options Theory. Wiley Finance Series. Wiley.
- Jenkins, D. G., & Quintana-Ascencio, P. F. (2020). A Solution to Minimum Sample Size for Regressions. PLOS ONE, 15, e0229345. https://doi.org/10.1371/journal.pone.0229345
- Joanes, D. N., & Gill, C. A. (1998). Comparing Measures of Sample Skewness and Kurtosis. Journal of the Royal Statistical Society: Series D (The Statistician), 47, 183-189. https://doi.org/10.1111/1467-9884.00122
- Liew, V. K.-S. (2004). What Lag Selection Criteria Should We Employ? Economics Bulletin, 33, 1-9.