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Risk Premium Effects On Implied Volatility Regressions

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  • Leonidas S. Rompolis
  • Elias Tzavalis

Abstract

This article provides new insights into the sources of bias of option implied volatility to forecast its physical counterpart. We argue that this bias can be attributed to volatility risk premium effects. The latter are found to depend on high‐order cumulants of the risk‐neutral density. These cumulants capture the risk‐averse behavior of investors in the stock and option markets for bearing the investment risk that is reflected in the deviations of the implied risk‐neutral distribution from the normal distribution. We show that the bias of implied volatility to forecast its corresponding physical measure can be eliminated when the implied volatility regressions are adjusted for risk premium effects. The latter are captured mainly by the third‐order risk‐neutral cumulant. We also show that a substantial reduction of higher order risk‐neutral cumulants biases to predict their corresponding physical cumulants is supported when adjustments for risk premium effects are made.

Suggested Citation

  • Leonidas S. Rompolis & Elias Tzavalis, 2010. "Risk Premium Effects On Implied Volatility Regressions," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(2), pages 125-151, June.
  • Handle: RePEc:bla:jfnres:v:33:y:2010:i:2:p:125-151
    DOI: 10.1111/j.1475-6803.2010.01265.x
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    Cited by:

    1. J. Arismendi-Zambrano & R. Azevedo, 2020. "Implicit Entropic Market Risk-Premium from Interest Rate Derivatives," Economics Department Working Paper Series n303-20.pdf, Department of Economics, National University of Ireland - Maynooth.
    2. George Chalamandaris & Leonidas S. Rompolis, 2021. "Recovering the market risk premium from higher‐order moment risks," European Financial Management, European Financial Management Association, vol. 27(1), pages 147-186, January.
    3. Byun, Suk Joon & Kim, Jun Sik, 2013. "The information content of risk-neutral skewness for volatility forecasting," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 142-161.
    4. Leonidas S. Rompolis & Elias Tzavalis, 2017. "Retrieving risk neutral moments and expected quadratic variation from option prices," Review of Quantitative Finance and Accounting, Springer, vol. 48(4), pages 955-1002, May.
    5. Slim, Skander & Dahmene, Meriam & Boughrara, Adel, 2020. "How informative are variance risk premium and implied volatility for Value-at-Risk prediction? International evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 22-37.
    6. Szu, Wen-Ming & Wang, Ming-Chun & Yang, Wan-Ru, 2011. "The determinants of exchange settlement practices and the implication of volatility smile: Evidence from the Taiwan Futures Exchange," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 826-838, October.
    7. Chalamandaris, Georgios & Rompolis, Leonidas S., 2012. "Exploring the role of the realized return distribution in the formation of the implied volatility smile," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1028-1044.
    8. Bruno Feunou & Jean-Sébastien Fontaine & Roméo Tédongap, 2017. "Implied volatility and skewness surface," Review of Derivatives Research, Springer, vol. 20(2), pages 167-202, July.
    9. Argyropoulos, Efthymios & Tzavalis, Elias, 2021. "The influence of real interest rates and risk premium effects on the ability of the nominal term structure to forecast inflation," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 785-796.

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