Please wait a minute...
Frontiers of Mathematics in China

ISSN 1673-3452

ISSN 1673-3576(Online)

CN 11-5739/O1

Postal Subscription Code 80-964

2018 Impact Factor: 0.565

Front. Math. China    2018, Vol. 13 Issue (4) : 809-832    https://doi.org/10.1007/s11464-018-0705-0
RESEARCH ARTICLE
Moderate deviations for Euler-Maruyama approximation of Hull-White stochastic volatility model
Yunshi GAO1, Hui JIANG1, Shaochen WANG2()
1. Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2. School of Mathematics, South China University of Technology, Guangzhou 510640, China
 Download: PDF(438 KB)  
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

We consider the Euler-Maruyama discretization of stochastic volatility model dSt=σtStdWt,dσt=ωσtdZt,t[0,T] which has been widely used in nancial practice, where Wt,Zt,t[0,T] are two uncorrelated standard Brownian motions. Using asymptotic analysis techniques, the moderate deviation principles for log Sn (or log |Sn| in case Sn is negative) are obtained as n under different discretization schemes for the asset price process St and the volatility process σt: Numerical simulations are presented to compare the convergence speeds in different schemes.

Keywords Euler-Maruyama discretization      Hull-White stochastic volatility model      moderate deviation principle     
Corresponding Author(s): Shaochen WANG   
Issue Date: 14 August 2018
 Cite this article:   
Yunshi GAO,Hui JIANG,Shaochen WANG. Moderate deviations for Euler-Maruyama approximation of Hull-White stochastic volatility model[J]. Front. Math. China, 2018, 13(4): 809-832.
 URL:  
https://academic.hep.com.cn/fmc/EN/10.1007/s11464-018-0705-0
https://academic.hep.com.cn/fmc/EN/Y2018/V13/I4/809
1 Bally V, Talay D. The law of the Euler scheme for stochastic differential equations. I. Convergence rate of the distribution function. Probab Theory Related Fields, 1995, 104: 43–60
https://doi.org/10.1007/BF01303802
2 Dembo A, Zeitouni O. Large Deviations Techniques and Applications. Berlin: Springer-Verlag, 1988
3 Ellis R. Entropy, Large Deviations, and Statistical Mechanics. Grundlehren Math Wiss, Vol 271. Berlin: Springer, 2005
4 Fabian V. On asymptotic normality in stochastic approximation. Ann Math Statist, 1968, 39: 1327–1332
https://doi.org/10.1214/aoms/1177698258
5 Friz P, Gerhold S, Pinter A. Option pricing in the moderate deviations regime. Math Finance,
6 Gao F, Wang S. Asymptotic behaviors for functionals of random dynamical systems. Stoch Anal Appl, 2016, 34(2): 258–277
https://doi.org/10.1080/07362994.2015.1119050
7 Gulisashvili A, Stein E M. Implied volatility in the Hull-White model. Math Finance, 2009, 19(2): 303–327
https://doi.org/10.1111/j.1467-9965.2009.00368.x
8 Guyon J. Euler scheme and tempered distributions. Stochastic Process Appl, 2006, 116(6): 877–904
https://doi.org/10.1016/j.spa.2005.11.011
9 Hull J, White A. Pricing of options on assets with stochastic volatilities. J Finance, 1987, 42: 281–300
https://doi.org/10.1111/j.1540-6261.1987.tb02568.x
10 Jiang H, Wang S. Moderate deviation principles for classical likelihood ratio tests of high-dimensional normal distributions. J Multivariate Anal, 2017, 156: 57–69
https://doi.org/10.1016/j.jmva.2017.02.004
11 Kloeden P E, Platen E. Numerical Solution of Stochastic Differential Equations. Berlin: Springer, 1992
https://doi.org/10.1007/978-3-662-12616-5
12 Pan G, Wang S, Zhou W. Limit theorems for linear spectrum statistics of orthogonal polynomial ensembles and their applications in random matrix theory. J Math Phys, 2017, 58: 103301
https://doi.org/10.1063/1.5006507
13 Pirjol D, Zhu L. On the growth rate of a linear stochastic recursion with Markovian dependence. J Stat Phys, 2015, 160: 1354–1388
https://doi.org/10.1007/s10955-015-1280-3
14 Pirjol D, Zhu L. Asymptotics for the Euler-discretized Hull-White stochastic volatility model. Methodol Comput Appl Probab, 2017, 2: 1–43
15 Renlund H. Limit theorems for stochastic approximation algorithms. arXiv: 1102.4741
16 Revuz D, Yor M. Continuous Martingales and Brownian Motion. Berlin: Springer-Verlag, 1999
https://doi.org/10.1007/978-3-662-06400-9
17 Talay D, Tubaro L. Expansion of the global error for numerical schemes solving stochastic differential equations. Stoch Anal Appl, 1990, 8(4): 483–509
https://doi.org/10.1080/07362999008809220
18 Varadhan S R S. Large Deviations and Applications. Philadelphia: SIAM, 1984
https://doi.org/10.1137/1.9781611970241
19 Wang S. Moderate deviations for a class of recursions. Statist Probab Lett, 2013, 83: 2348–2352
https://doi.org/10.1016/j.spl.2013.06.002
20 Zhang X. Euler-Maruyama approximations for SDEs with non-Lipschitz coecients and applications. J Math Anal Appl, 2006, 316(2): 447–458
https://doi.org/10.1016/j.jmaa.2005.04.052
[1] Yongqiang SUO, Jin TAO, Wei ZHANG. Moderate deviation and central limit theorem for stochastic differential delay equations with polynomial growth[J]. Front. Math. China, 2018, 13(4): 913-933.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed