Empirical Research on Chinese Warrants Market Based on the Montecarlo Pricing Options Under Levy Process
American Journal of Applied Mathematics
Volume 3, Issue 3, June 2015, Pages: 129-137
Received: May 4, 2015; Accepted: May 13, 2015; Published: May 27, 2015
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Li Zhou, School of Information, Beijing Wuzi University, Beijing, China
Hong Zhang, School of Information, Beijing Wuzi University, Beijing, China
Jian Guo, School of Information, Beijing Wuzi University, Beijing, China
Anjie Deng, School of Banking and Finance, University of International Business and Economic, Beijing, China
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After the 2008 financial crisis, the global derivatives trading volume in options proportion is growing, more and more investors build portfolios using options to hedge or arbitrage, our futures and stock options will soon open. Theoretical research of options is also changing, option pricing models under Levy processes developed rapidly. In this context, a review of the China's warrants market and the introduction of option pricing models can not only help us to reflect Chinese financial derivatives market regulation, but also to explore the option pricing theory for China’s financial market environment. In the framework of Monte Carlo simulation pricing,we established mufti-Levy process option pricing models, the structural model for the given parameter estimation and risk-neutral adjustment method are discussed, the last part of this chapter is an empirical analysis of China warrants trading data in order to prove the validate of Levy models.
Levy Stochastic Processes, Option Pricing Models, Chinese warrants Market, American Option Pricing, Risk-Neutral Adjustment, Variance Reduction Techniques
To cite this article
Li Zhou, Hong Zhang, Jian Guo, Anjie Deng, Empirical Research on Chinese Warrants Market Based on the Montecarlo Pricing Options Under Levy Process, American Journal of Applied Mathematics. Vol. 3, No. 3, 2015, pp. 129-137. doi: 10.11648/j.ajam.20150303.19
Box G, Mervin E. Muller. A note on the generation of random normal deviates[J]. The Annals of Mathematical Statistics, 1958, 29(2): 610-611.
Boyle P. Options: A Monte Carlo approach[J]. Journal of Financial Economics, 1977, 4(3):323-338.
Broadie M, Yamamoto Y. Application of the fast Gauss transform to option pricing[J]. Managment Science, 2003, 49(8): 1071-1088.
Broadie M, Yamamoto Y. A double-exponential fast Gauss transform for pricing discrete pathdependent options[J]. Operations Research, 2005, 53(5): 764-779.
Byun SJ, Min B. Conditional volatility and the GARCH option pricing model with non-normal innovations[J]. 3ournal of Futures Market, 2413, 33(1): 1-28.
Carr P, Madan D B. Option valuation using the fast Fourier transform[J] Journal of Computational Finance, 1999, 2(4): 61-73.
Carr P, Geman H, Madan D H and Yor M. The fine structure of asset returns: an empirical investigation[J]. Journal of Business, 2002, 75(2): 305-332.
Carr P and Wu L R. The finite moment log stable process and option pricing[J]. Journal of Finance, 2003, 58(2): 753-777.
Carriere J F. Valuation of the early exercise price for options using simulations and nonparametric regression[J]. Insurance: Mathematics and Economics, 1996, 19(1): 19-30;
Carrosco M, Chernov M, Florens JP, Ghysels. Efficient estimation of general dynamic models with a continuum of moment conditions[J]. Journal of Econometrics, 2007, 140(2): 529-573.
Chen Z, Feng L and Lin X. Simulating Levy process from their characteristic functions and financial applications[J]. ACM Transactions on Modeling and Computer Simulation, 2012, 22(3).
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