Large-Scale Empirical Tests of the Markov Tree Model

International Journal of Financial Studies

Harish S. Bhat¹  Nitesh Kumar² 

[1] Applied Mathematics Unit, University of California, Merced, CA 95343, USA;Skytree, Inc., San Jose, CA 95110, USA; E-Mail:

关键词: option pricing;    short-term memory in asset prices;    empirical performance;    hedging errors;   

DOI  :  10.3390/ijfs3030280

来源: mdpi

【 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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.