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On the relation between implied and realized volatility indices: Evidence from the BRIC countries

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  • Bentes, Sónia R.

Abstract

This paper investigates the relation between implied (IV) and realized volatility (RV). Using monthly data from the BRIC countries, we assess the informational content of IV in explaining future RV as well as its unbiasedness and efficiency. We employ an ADL (Autoregressive Distributed Lag) and the corresponding EC (Error Correction) model and compare the results with the ones obtained from the OLS regression. Our goal is to assess the fully dynamical relations between these variables and to separate the short from the long-run effects. We found different results for the informational content of IV according to the methodologies used. However, both methods show that IV is an unbiased estimate of RV for India and that IV was not found to be efficient in any of the BRIC countries. Further, EC results reveal the presence of short and long-run effects for India, whereas Russia exhibits only short-run adjustments.

Suggested Citation

  • Bentes, Sónia R., 2017. "On the relation between implied and realized volatility indices: Evidence from the BRIC countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 243-248.
  • Handle: RePEc:eee:phsmap:v:482:y:2017:i:c:p:243-248
    DOI: 10.1016/j.physa.2017.04.071
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    1. Nam, Seung Oh & Oh, SeungYoung & Kim, Hyun Kyung & Kim, Byung Chun, 2006. "An empirical analysis of the price discovery and the pricing bias in the KOSPI 200 stock index derivatives markets," International Review of Financial Analysis, Elsevier, vol. 15(4-5), pages 398-414.
    2. Charles J. Corrado & Thomas W. Miller, Jr., 2005. "The forecast quality of CBOE implied volatility indexes," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(4), pages 339-373, April.
    3. Blair, Bevan J. & Poon, Ser-Huang & Taylor, Stephen J., 2001. "Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns," Journal of Econometrics, Elsevier, vol. 105(1), pages 5-26, November.
    4. Agnolucci, Paolo, 2009. "Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models," Energy Economics, Elsevier, vol. 31(2), pages 316-321, March.
    5. Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, vol. 50(2), pages 125-150, November.
    6. Hsu, Stephen D.H. & Murray, Brian M., 2007. "On the volatility of volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 366-376.
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    Cited by:

    1. Leovardo Mata Mata & José Antonio Núñez Mora & Ramona Serrano Bautista, 2021. "Multivariate Distribution in the Stock Markets of Brazil, Russia, India, and China," SAGE Open, , vol. 11(2), pages 21582440211, April.

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