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A lattice approach for option pricing under a regime-switching GARCH-jump model

Published online by Cambridge University Press:  29 July 2021

Zhiyu Guo
Affiliation:
Business School, Nankai University, Tianjin 300071, China. E-mail: [email protected]
Yizhou Bai
Affiliation:
College of Science, Civil Aviation University of China, Tianjin 300071, China. E-mail: [email protected]

Abstract

In this study, we consider option pricing under a Markov regime-switching GARCH-jump (RS-GARCH-jump) model. More specifically, we derive the risk neutral dynamics and propose a lattice algorithm to price European and American options in this framework. We also provide a method of parameter estimation in our RS-GARCH-jump setting using historical data on the underlying time series. To measure the pricing performance of the proposed algorithm, we investigate the convergence of the tree-based results to the true option values and show that this algorithm exhibits good convergence. By comparing the pricing results of RS-GARCH-jump model with regime-switching GARCH (RS-GARCH) model, GARCH-jump model, GARCH model, Black–Scholes (BS) model, and Regime-Switching (RS) model, we show that accommodating jump effect and regime switching substantially changes the option prices. The empirical results also show that the RS-GARCH-jump model performs well in explaining option prices and confirm the importance of allowing for both jump components and regime switching.

Type
Research Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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References

Avriel, M., Hilscher, J., & Raviv, A. (2013). Inflation derivatives under inflation target regimes. Journal of Futures Markets 33(10): 911938.CrossRefGoogle Scholar
Ben-Ameur, H., Breton, M., & Martinez, J.M. (2009). Dynamic programming approach for valuing options in the GARCH model. Management Science 55(2): 252266.CrossRefGoogle Scholar
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31(3): 307327.CrossRefGoogle Scholar
Cakici, N. & Topyan, K. (2000). The GARCH option pricing model: a lattice approach. Journal of Computational Finance 3(4): 7185.CrossRefGoogle Scholar
Chan, W.H. & Lee, H.-T. (2015). A regime switching correlated bivariate Poisson jump model for futures hedging. Empirical Economics 28(4): 669685.CrossRefGoogle Scholar
Chan, W.H. & Young, D. (2009). A new look at copper markets: a regime-switching jump model. University of Alberta, Department of Economics.Google Scholar
Chen, C.C. & Hung, M.Y. (2010). Option pricing under Markov-switching GARCH processes. Journal of Futures Markets 30(5): 444464.Google Scholar
Christoffersen, P., Elkamhi, R., Feunou, B., & Jacobs, K. (2010). Option valuation with conditional heteroskedasticity and nonnormality. The Review of Financial Studies 23(5): 21392183.CrossRefGoogle Scholar
Christoffersen, P., Jacobs, K., & Ornthanalai, C. (2012). Dynamic jump intensities and risk premiums: evidence from S&P500 returns and options. Journal of Financial Economics 106(3): 447472.CrossRefGoogle Scholar
Duan, J.C. (1995). The GARCH option pricing model. Mathematical Finance 5(1): 1332.CrossRefGoogle Scholar
Duan, J.C. & Simonato, J.G. (2001). American option pricing under GARCH by a Markov chain approximation. Journal of Economic Dynamics and Control 25(11): 16891718.CrossRefGoogle Scholar
Duan, J.C., Ritchken, P.H., & Sun, Z. (2006). Jump starting GARCH pricing and hedging option with jumps in returns and volatilities. FRB of Cleveland Working Paper.Google Scholar
Elliott, R.J., Siu, T.K., & Chan, L. (2006). Option pricing for GARCH models with Markov switching. International Journal of Theoretical and Applied Finance 9(06): 825841.CrossRefGoogle Scholar
Gray, S.F. (1996). Modeling the conditional distribution of interest rates as a regime-switching process. Journal of Financial Economics 42(1): 2762.CrossRefGoogle Scholar
Hamilton, J.D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57(2): 357384.CrossRefGoogle Scholar
Inclán, C. & Tiao, G.C. (1994). Use of cumulative sums of squares for retrospective detection of changes of variance. Journal of the American Statistical Association 89(427): 913923.Google Scholar
Klaassen, F. (2002). Improving GARCH volatility forecasts with regime-switching GARCH. Empirical Economics 27(2): 363394.CrossRefGoogle Scholar
Lamoureux, C.G. & Lastrapes, W.D. (1990). Persistence in variance, structural change, and the GARCH model. Journal of Business & Economic Statistics 8(2): 225234.Google Scholar
Lee, H.T. (2009). Optimal futures hedging under jump switching dynamics. Journal of Empirical Finance 16(3): 446456.CrossRefGoogle Scholar
Lin, B.H. & Yeh, S.K. (2004). On the distribution and conditional heteroscedasticity in Taiwan stock prices. Journal of Multinational Financial Management 10(3): 367395.CrossRefGoogle Scholar
Lin, B.H., Hung, M.W., Wang, J.Y., & Wu, P.D. (2013). A lattice model for option pricing under GARCH-jump processes. Review of Derivatives Research 16(3): 295329.Google Scholar
Lyuu, Y.D. & Wu, C.N. (2005). On accurate and provably efficient GARCH option pricing algorithms. Quantitative Finance 5(2): 181198.CrossRefGoogle Scholar
Marcucci, J. (2005). Forecasting stock market volatility with regime-switching GARCH models. Studies in Nonlinear Dynamics & Econometrics 9(4): 154.Google Scholar
Newey, W.K. & West, K.D. (1994). Automatic lag selection in covariance matrix estimation. The Review of Economic Studies 61(4): 631653.CrossRefGoogle Scholar
Perez-Quiros, G. & Timmermann, A. (2001). Business cycle asymmetries in stock returns: evidence from higher order moments and conditional densities. Journal of Econometrics 103(1): 259306.CrossRefGoogle Scholar
Rapach, D.E. & Strauss, J.K. (2008). Structural breaks and GARCH models of exchange rate volatility. Journal of Applied Econometrics 23(1): 6590.CrossRefGoogle Scholar
Ritchken, P. & Trevor, R. (1999). Pricing options under generalized GARCH and stochastic volatility processes. Journal of Finance 54(1): 377402.CrossRefGoogle Scholar
Rombouts, J.V.K. & Stentoft, L. (2011). Multivariate option pricing with time varying volatility and correlations. Journal of Banking & Finance 35(9): 22672281.CrossRefGoogle Scholar
Sansó, A., Carrion, J.L., & Aragó, V. (2004). Testing for changes in the unconditional variance of financial time series. Revista de Economía Financiera 4: 3252.Google Scholar
Satoyoshi, K. & Mitsui, H. (2011). Empirical study of Nikkei 225 options with the Markov switching GARCH model. Asia-Pacific Financial Markets 18(1): 5568.CrossRefGoogle Scholar
Shi, Y. & Feng, L. (2016). A discussion on the innovation distribution of the Markov regime-switching GARCH model. Economic Modelling 53: 278288.CrossRefGoogle Scholar
Simonato, J.G. (2019). American option pricing under GARCH with non-normal innovations. Optimization and Engineering 20(3): 853880.CrossRefGoogle Scholar