DOI QR코드

DOI QR Code

Asset Pricing From Log Stochastic Volatility Model: VKOSPI Index

로그SV 모형을 이용한 자산의 가치평가에 관한 연구: VKOSPI 지수

Oh, Yu-Jin
오유진

  • Received : 20101100
  • Accepted : 20101100
  • Published : 2011.02.28

Abstract

This paper examines empirically Durham's (2008) asset pricing models to the KOSPI200 index. This model Incorporates the VKOSPI index as a proxy for 1 month integrated volatility. This approach uses option prices to back out implied volatility states with an explicitly speci ed risk-neutral measure and risk premia estimated from the data. The application uses daily observations of the KOSPI200 and VKOSPI indices from January 2, 2003 to September 24, 2010. The empirical results show that non-affine model perform better than affine model.

Keywords

Asset pricing;Log Stochastic Volatility Model;KOPSPI200;Volatility;VKOSPI

References

  1. 김명작, 장국현 (1996). KOSPI200 지수의 확률변동성 측정방법, <선물연구>, 4, 131-156.
  2. Andersen, T., Benzoni, L. and Lund, J. (2002). An empirical investigation of continuous time equity return models, Journal of Finance, 57, 1239-1284. https://doi.org/10.1111/1540-6261.00460
  3. Andersen, T., Bollerslev, T., Diebold, F. and Labys, P. (2003). Modeling and forecasting realized volatility, Econometrica, 71, 579-625. https://doi.org/10.1111/1468-0262.00418
  4. Bakshi, G., Cao, C. and Chen, Z. (1997). Empirical performance of alternative option pricing models, Journal of Finance, 52, 2003-2409. https://doi.org/10.2307/2329472
  5. Britten-Jones, M. and Neuberger, A. (2000). Option prices, implied price processes, and stochastic volatility, Journal of Finance, 55, 839-866. https://doi.org/10.1111/0022-1082.00228
  6. Broadie, M., Chernov, M. and Johannes, M. (2007). Model specication and risk premia: Evidence from futures options, Journal of Finance, 62, 1453-1490. https://doi.org/10.1111/j.1540-6261.2007.01241.x
  7. Chernov, M. and Ghysels, E. (2000). A study towards a unied approach to the joint estimation of objective and risk neutral measures for the purposes of options valuation, Journal of Financial Economics, 56, 407-458. https://doi.org/10.1016/S0304-405X(00)00046-5
  8. Christoersen, P., Jacobs, K. and Mimouni, K. (2006). Models for S&P500 dynamics: Evidence from realized volatility, daily returns, and options prices, mimeo, McGill University.
  9. Durham, G. B. (2006). Monte carlo methods for estimating, smoothing, and ltering one- and two-factor volatility models, Journal of Econometrics, 133, 273-305. https://doi.org/10.1016/j.jeconom.2005.03.016
  10. Durham, G. B. (2008). Risk-neutral modelling with ane and non-ane models, mimeo, University of Colorado at Boulder.
  11. Eraker, B. (2001). MCMC analysis of diusion models with application to nance, Journal of Business and Economic Statistics, 19, 177-191. https://doi.org/10.1198/073500101316970403
  12. Gallant, A. R. and Tauchen, G. E. (1996). Which moments to match?, Econometric Theory, 12, 657-681. https://doi.org/10.1017/S0266466600006976
  13. Ghysels, E., Santa-Clara, P. and Valkanov, R. (2006). Predicting volatility: Getting the most out of return data sampled at dierent frequencies, Journal of Econometrics, 131, 59-95. https://doi.org/10.1016/j.jeconom.2005.01.004
  14. Jacquier, E., Polson, N. G. and Rossi, P. E. (1994). Bayesian analysis of stochastic volatility models, Journal of Business and Economic Statistics, 12, 371-389. https://doi.org/10.2307/1392199
  15. Jones, C. S. (2003). The dynamics of stochastic volatility: Evidence from underlying and option markets, Journal of Econometrics, 116, 181-224 https://doi.org/10.1016/S0304-4076(03)00107-6