A Journal "Theory of Probability and Mathematical Statistics"
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
1973
1972
1971
1970


Archive

About   Editorial Board   Contacts   Template   Publication Ethics   Peer Review Process   Special Issues   History  

Theory of Probability and Mathematical Statistics



Gatheral double stochastic volatility model with Skorokhod reflection

Yuliya Mishura, Andrey Pilipenko and Kostiantyn Ralchenko

Link

Abstract: We investigate the Gatheral model of double mean-reverting stochastic volatility, in which the drift term itself follows a mean-reverting process, and the overall model exhibits mean-reverting behavior. We demonstrate that such processes can attain values arbitrarily close to zero and remain near zero for extended periods, making them practically and statistically indistinguishable from zero. To address this issue, we propose a modified model incorporating Skorokhod reflection, which preserves the model’s flexibility while preventing volatility from approaching zero.

Keywords: Stochastic volatility, Gatheral model, Cox–Ingersoll–Ross process, CKLS model, mean reversion, stochastic differential equation, strong solution, Skorokhod reflection

Bibliography:
• M. Albuhayri, S. Silvestrov, M. Dimitrov, Y. Ni, and A. Malyarenko, Numerical studies of the implied volatility expansions up to third order under the Gatheral model, 7th Stochastic Modeling Techniques and Data Analysis International Conference (SMTDA2022) and Demographics 2022 Workshop, 2022.
• Leif B. G. Andersen and Vladimir V. Piterbarg, Moment explosions in stochastic volatility models, Finance Stoch. 11 (2007), no. 1, 29–50. MR 2284011, DOI 10.1007/s00780-006-0011-7
• Christian Bayer, Jim Gatheral, and Morten Karlsmark, Fast Ninomiya-Victoir calibration of the double-mean-reverting model, Quant. Finance 13 (2013), no. 11, 1813–1829. MR 3175945, DOI 10.1080/14697688.2013.818245
• Milan Borkovec and Claudia Klüppelberg, Extremal behavior of diffusion models in finance, Extremes 1 (1998), no. 1, 47–80. MR 1652940, DOI 10.1023/A:1009961817149
• K. C. Chan, G. A. Karolyi, F. A. Longstaff, and A. B Sanders, An empirical comparison of alternative models of the short-term interest rate, J. Finance 47 (1992), no. 3, 1209–1227.
• Liu Chang, Shouting Chen, and Ailin Zhu, Properties of the Cox-Ingersoll-Ross interest rate processes with two-sided reflections, Comm. Statist. Theory Methods 44 (2015), no. 4, 657–670. MR 3315779, DOI 10.1080/03610926.2013.813043
• Olena Dehtiar, Yuliya Mishura, and Kostiantyn Ralchenko, Two methods of estimation of the drift parameters of the Cox-Ingersoll-Ross process: continuous observations, Comm. Statist. Theory Methods 51 (2022), no. 19, 6818–6833. MR 4471421, DOI 10.1080/03610926.2020.1866611
• G. Di Nunno, K. Kubilius, Y. Mishura, and A. Yurchenko-Tytarenko, From constant to rough: A survey of continuous volatility modeling, Mathematics 11 (2023), no. 19, 4201.
• J. Gatheral, Consistent modeling of SPX and VIX options, Bachelier Congress, vol. 37, 2008, pp. 39–51.
• Arnaud Gloter and Nakahiro Yoshida, Non-adaptive estimation for degenerate diffusion processes, Theory Probab. Math. Statist. 110 (2024), 75–99. MR 4751811, DOI 10.1090/tpms/1207
• Nobuyuki Ikeda and Shinzo Watanabe, A comparison theorem for solutions of stochastic differential equations and its applications, Osaka Math. J. 14 (1977), no. 3, 619–633. MR 471082
• Nobuyuki Ikeda and Shinzo Watanabe, Stochastic differential equations and diffusion processes, 2nd ed., North-Holland Mathematical Library, vol. 24, North-Holland Publishing Co., Amsterdam; Kodansha, Ltd., Tokyo, 1989. MR 1011252
• Ioannis Karatzas and Steven E. Shreve, Brownian motion and stochastic calculus, 2nd ed., Graduate Texts in Mathematics, vol. 113, Springer-Verlag, New York, 1991. MR 1121940, DOI 10.1007/978-1-4612-0949-2
• Fima C. Klebaner, Introduction to stochastic calculus with applications, 3rd ed., Imperial College Press, London, 2012. MR 2933773, DOI 10.1142/p821
• Yuliya Mishura, Kostiantyn Ralchenko, and Olena Dehtiar, Parameter estimation in CKLS model by continuous observations, Statist. Probab. Lett. 184 (2022), Paper No. 109391, 10. MR 4382072, DOI 10.1016/j.spl.2022.109391
• Y. Mishura, K. Ralchenko, and S. Kushnirenko, Driven by Brownian motion Cox–Ingersoll–Ross and squared Bessel processes: Interaction and phase transition, Physics of Fluids 37 (2025), no. 1, 017115.
• A. Pilipenko, An introduction to stochastic differential equations with reflection, Lectures in Pure and Applied Mathematics, vol. 1, Potsdam University Press, 2014.
• Olha Prykhodko and Kostiantyn Ralchenko, Asymptotic normality of estimators for all parameters in the Vasicek model by discrete observations, Theory Probab. Math. Statist. 111 (2024), 123–135. MR 4823519, DOI 10.1090/tpms/1218
• Daniel Revuz and Marc Yor, Continuous martingales and Brownian motion, 3rd ed., Grundlehren der mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences], vol. 293, Springer-Verlag, Berlin, 1999. MR 1725357, DOI 10.1007/978-3-662-06400-9
• Yiwei Shi, Huisheng Shu, Chunyang Wang, and Xuekang Zhang, Parameter estimation for Cox-Ingersoll-Ross process with two-sided reflections, Statist. Probab. Lett. 221 (2025), Paper No. 110352, 10. MR 4864973, DOI 10.1016/j.spl.2024.110352
• A. V. Skorokhod, Asymptotic methods in the theory of stochastic differential equations, Translations of Mathematical Monographs, vol. 78, American Mathematical Society, Providence, RI, 1989. Translated from the Russian by H. H. McFaden. MR 1020057, DOI 10.1090/mmono/078
• —, Studies in the theory of random processes, Dover Publications, Mineola, New York, 2017.