文章基本信息

标题：Large-Scale Empirical Tests of the Markov Tree Model
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作者：Bhat, Harish S. ; Kumar, Nitesh
期刊名称：International Journal of Financial Studies
印刷版ISSN：2227-7072
出版年度：2015
卷号：3
期号：3
页码：280-318
出版社：MDPI, Open Access Journal
摘要：The Markov Tree model is a discrete-time option pricing model that accounts for short-term memory of the underlying asset. In this work, we compare the empirical performance of the Markov Tree model against that of the Black-Scholes model and Heston’s stochastic volatility model. Leveraging a total of five years of individual equity and index option data, and using three new methods for fitting the Markov Tree model, we find that the Markov Tree model makes smaller out-of-sample hedging errors than competing models. This comparison includes versions of Markov Tree and Black-Scholes models in which volatilities are strike- and maturity-dependent. Visualizing the errors over time, we find that the Markov Tree model yields more accurate and less risky single instrument hedges than Heston’s stochastic volatility model. A statistical resampling method indicates that the Markov Tree model’s superior hedging performance is due to its robustness with respect to noise in option data.
关键词：option pricing; short-term memory in asset prices; empirical performance; hedging errors