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Estimation of objective and risk-neutral distributions based on moments of integrated volatility

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Author Info

  • Garcia, René
  • Lewis, Marc-André
  • Pastorello, Sergio
  • Renault, Éric

Abstract

In this paper, we present an estimation procedure which uses both option prices and high-frequency spot price feeds to estimate jointly the objective and risk-neutral parameters of stochastic volatility models. The procedure is based on a method of moments that uses analytical expressions for the moments of the integrated volatility and series expansions of option prices and implied volatilities. This results in an easily implementable and rapid estimation technique. An extensive Monte Carlo study compares various procedures and shows the efficiency of our approach. Empirical applications to the Deutsche mark-US dollar exchange rate futures and the S&P 500 index provide evidence that the method delivers results that are in line with the ones obtained in previous studies where much more involved estimation procedures were used.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 160 (2011)
Issue (Month): 1 (January)
Pages: 22-32

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Handle: RePEc:eee:econom:v:160:y:2011:i:1:p:22-32

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Web page: http://www.elsevier.com/locate/jeconom

Related research

Keywords: Realized volatility Implied volatility Volatility risk premium Moments of integrated volatility Objective distribution Risk-neutral distribution;

References

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Citations

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Cited by:
  1. Isao Ishida & Michael McAleer & Kosuke Oya, 2011. "Estimating the Leverage Parameter of Continuous-time Stochastic Volatility Models Using High Frequency S&P 500 and VIX," Working Papers in Economics 11/11, University of Canterbury, Department of Economics and Finance.
  2. Maneesoonthorn, Worapree & Martin, Gael M. & Forbes, Catherine S. & Grose, Simone D., 2012. "Probabilistic forecasts of volatility and its risk premia," Journal of Econometrics, Elsevier, vol. 171(2), pages 217-236.
  3. Bollerslev, Tim & Zhou, Hao, 2002. "Estimating stochastic volatility diffusion using conditional moments of integrated volatility," Journal of Econometrics, Elsevier, vol. 109(1), pages 33-65, July.
  4. Ishida, I. & McAleer, M.J. & Oya, K., 2011. "Estimating the Leverage Parameter of Continuous-time Stochastic Volatility Models Using High Frequency S&P 500 VIX," Econometric Institute Research Papers EI 2011-10, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  5. Stanislav Khrapov, 2011. "Pricing Central Tendency in Volatility," Working Papers w0168, Center for Economic and Financial Research (CEFIR).
  6. Bregantini, Daniele, 2013. "Moment-based estimation of stochastic volatility," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4755-4764.
  7. Corradi, Valentina & Distaso, Walter & Mele, Antonio, 2013. "Macroeconomic determinants of stock volatility and volatility premiums," Journal of Monetary Economics, Elsevier, vol. 60(2), pages 203-220.
  8. René Garcia & Eric Ghysels & Éric Renault, 2004. "The Econometrics of Option Pricing," CIRANO Working Papers 2004s-04, CIRANO.
  9. Andersen, Torben G. & Bollerslev, Tim & Meddahi, Nour, 2011. "Realized volatility forecasting and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 220-234, January.
  10. Torben G. Andersen & Viktor Todorov, 2009. "Realized Volatility and Multipower Variation," CREATES Research Papers 2009-49, School of Economics and Management, University of Aarhus.
  11. Kyungsub Lee, 2013. "Probabilistic and statistical properties of realized moments and their use in inference, estimation and risk management," Papers 1311.5036, arXiv.org.

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