NDLI: Pricing American options under partial observation of stochastic volatility

Content Provider	IEEE Xplore Digital Library
Author	Fan Ye Enlu Zhou
Copyright Year	2011
Description	Author affiliation: Department of Industrial & Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, 61801, USA (Fan Ye; Enlu Zhou)
Abstract	Stochastic volatility models capture the impact of time-varying volatility on the financial markets, and hence are heavily used in financial engineering. However, stochastic volatility is not directly observable in reality, but is only “partially” observable through the inference from the observed asset price. Most of the past research studied American option pricing in stochastic volatility models under the assumption that the volatility is fully observable, which often leads to overpricing of the option. In this paper, we treat the problem under the more realistic assumption of partially observable stochastic volatility, and propose a numerical solution method by extending the regression method and the martingale duality approach to the partially observable case. More specifically, we develop a filtering-based martingale duality approach that complements a lower bound on the option price with an approximate upper bound. Numerical experiments show that our method reduces overpricing of the option with a moderate computational cost.
Starting Page	3755
Ending Page	3766
File Size	211174
Page Count	12
File Format	PDF
ISBN	9781457721083
ISSN	08917736
e-ISBN	9781457721090
e-ISBN	9781457721076
DOI	10.1109/WSC.2011.6148068
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Pricing Approximation methods Upper bound Computational modeling Dynamic programming
Content Type	Text
Resource Type	Article
Subject	Modeling and Simulation Computer Science Applications Software

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