Large-Scale Empirical Tests of the Markov Tree Model

Harish S. Bhat and Nitesh Kumar
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Harish S. Bhat: Applied Mathematics Unit, University of California, Merced, CA 95343, USA
Nitesh Kumar: Skytree, Inc., San Jose, CA 95110, USA

IJFS, 2015, vol. 3, issue 3, 1-39

Abstract: 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.

Keywords: option pricing; short-term memory in asset prices; empirical performance; hedging errors (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2015
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