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A censored stochastic volatility approach to the estimation of price limit moves

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  • Hsieh, Ping-Hung
  • Yang, J. Jimmy

Abstract

A censored stochastic volatility model is developed to reconstruct a return series censored by price limits, one popular form of market stabilization mechanisms. When price limits are reached, the observed prices are truncated and the equilibrium prices are unobservable, which makes further financial analyses difficult. The model offers theoretically sound estimates of censored returns and is demonstrated via simulations to outperform existing approaches with respect to the estimates of model parameters, unconditional means, and standard deviations. The algorithm is applied to model stock and futures returns and results are consistent with the simulation outcomes.

Suggested Citation

  • Hsieh, Ping-Hung & Yang, J. Jimmy, 2009. "A censored stochastic volatility approach to the estimation of price limit moves," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 337-351, March.
    • Handle: RePEc:eee:empfin:v:16:y:2009:i:2:p:337-351
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    1. Kon, Stanley J, 1984. "Models of Stock Returns-A Comparison," Journal of Finance, American Finance Association, vol. 39(1), pages 147-165, March.
    2. Chib, Siddhartha & Greenberg, Edward, 1994. "Bayes inference in regression models with ARMA (p, q) errors," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 183-206.
    3. Andersen, Torben G & Sorensen, Bent E, 1996. "GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 328-352, July.
    4. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
    5. Giorgio Calzolari & Gabriele Fiorentini, 1998. "A tobit model with garch errors," Econometric Reviews, Taylor & Francis Journals, vol. 17(1), pages 85-104.
    6. Cho, David D. & Russell, Jeffrey & Tiao, George C. & Tsay, Ruey, 2003. "The magnet effect of price limits: evidence from high-frequency data on Taiwan Stock Exchange," Journal of Empirical Finance, Elsevier, vol. 10(1-2), pages 133-168, February.
    7. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 361-393.
    8. Roll, Richard, 1984. "Orange Juice and Weather," American Economic Review, American Economic Association, vol. 74(5), pages 861-880, December.
    9. McCurdy, Thomas H. & Morgan, Ieuan G., 1987. "Tests of the martingale hypothesis for foreign currency futures with time-varying volatility," International Journal of Forecasting, Elsevier, vol. 3(1), pages 131-148.
    10. Lee, Lung-fei, 1999. "Estimation of dynamic and ARCH Tobit models," Journal of Econometrics, Elsevier, vol. 92(2), pages 355-390, October.
    11. George, Thomas J. & Hwang, Chuan-Yang, 1995. "Transitory Price Changes and Price-Limit Rules: Evidence from the Tokyo Stock Exchange," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 30(2), pages 313-327, June.
    12. Kodres, Laura E, 1993. "Tests of Unbiasedness in the Foreign Exchange Futures Markets: An Examination of Price Limits and Conditional Heteroscedasticity," The Journal of Business, University of Chicago Press, vol. 66(3), pages 464-490, July.
    13. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models: Comments: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 413-417, October.
    14. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, vol. 61(1), pages 43-76, July.
    15. Jeff Chung & Li Gan, 2005. "Estimating the effect of price limits on limit-hitting days," Econometrics Journal, Royal Economic Society, vol. 8(1), pages 79-96, March.
    16. Andersen, Torben G. & Chung, Hyung-Jin & Sorensen, Bent E., 1999. "Efficient method of moments estimation of a stochastic volatility model: A Monte Carlo study," Journal of Econometrics, Elsevier, vol. 91(1), pages 61-87, July.
    17. Wei, Steven X., 2002. "A censored-GARCH model of asset returns with price limits," Journal of Empirical Finance, Elsevier, vol. 9(2), pages 197-223, March.
    18. Morgan, I G & Trevor, R G, 1999. "Limit Moves as Censored Observations of Equilibrium Futures Price in GARCH Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 397-408, October.
    19. Renate Meyer & Jun Yu, 2000. "BUGS for a Bayesian analysis of stochastic volatility models," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 198-215.
    20. Miller, M.H., 1989. "Commentary: Volatility, Prices Resolution, And Effectiveness Of Price Limits," Papers t8, Columbia - Center for Futures Markets.
    21. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
    22. Nardari, Federico & Scruggs, John T., 2007. "Bayesian Analysis of Linear Factor Models with Latent Factors, Multivariate Stochastic Volatility, and APT Pricing Restrictions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 42(4), pages 857-891, December.
    23. Kim, Kenneth & Rhee, S Ghon, 1997. "Price Limit Performance: Evidence from the Tokyo Stock Exchange," Journal of Finance, American Finance Association, vol. 52(2), pages 885-899, June.
    24. Wei, K. C. John & Chiang, Raymond, 2004. "A GMM approach for estimation of volatility and regression models when daily prices are subject to price limits," Pacific-Basin Finance Journal, Elsevier, vol. 12(4), pages 445-461, September.
    25. Subrahmanyam, Avanidhar, 1994. "Circuit Breakers and Market Volatility: A Theoretical Perspective," Journal of Finance, American Finance Association, vol. 49(1), pages 237-254, March.
    26. Lee, Sang Bin & Kim, Dae Joong, 1997. "Price Limits and Beta," Review of Quantitative Finance and Accounting, Springer, vol. 9(1), pages 35-52, July.
    27. Ma, C.K. & Rao, R.P. & Sears, R.S., 1989. "Volatility, Price Resolution, And The Effectiveness Of Price Limits," Papers t7, Columbia - Center for Futures Markets.
    28. Steven Wei, 1999. "A bayesian approach to dynamic tobit models," Econometric Reviews, Taylor & Francis Journals, vol. 18(4), pages 417-439.
    29. Chou, Pin-Huang, 1997. "A Gibbs sampling approach to the estimation of linear regression models under daily price limits," Pacific-Basin Finance Journal, Elsevier, vol. 5(1), pages 39-62, February.
    30. Durham, Garland B., 2007. "SV mixture models with application to S&P 500 index returns," Journal of Financial Economics, Elsevier, vol. 85(3), pages 822-856, September.
    31. Jacquier, Eric & Polson, Nicholas G. & Rossi, P.E.Peter E., 2004. "Bayesian analysis of stochastic volatility models with fat-tails and correlated errors," Journal of Econometrics, Elsevier, vol. 122(1), pages 185-212, September.
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    Cited by:

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    3. Eymen Errais & Dhikra Bahri, 2016. "Is Standard Deviation a Good Measure of Volatility? the Case of African Markets with Price Limits," Annals of Economics and Finance, Society for AEF, vol. 17(1), pages 145-165, May.
    4. Levy, Tamir & Qadan, Mahmod & Yagil, Joseph, 2013. "Predicting the limit-hit frequency in futures contracts," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 141-148.

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