Parameter estimation risk in asset pricing and risk management: A Bayesian approach

Radu Tunaru and Teng Zheng

International Review of Financial Analysis, 2017, vol. 53, issue C, 80-93

Abstract: Parameter estimation risk is non-trivial in both asset pricing and risk management. We adopt a Bayesian estimation paradigm supported by the Markov Chain Monte Carlo inferential techniques to incorporate parameter estimation risk in financial modelling. In option pricing activities, we find that the Merton's Jump-Diffusion (MJD) model outperforms the Black-Scholes (BS) model both in-sample and out-of-sample. In addition, the construction of Bayesian posterior option price distributions under the two well-known models offers a robust view to the influence of parameter estimation risk on option prices as well as other quantities of interest in finance such as probabilities of default. We derive a VaR-type parameter estimation risk measure for option pricing and we show that parameter estimation risk can bring significant impact to Greeks' hedging activities. Regarding the computation of default probabilities, we find that the impact of parameter estimation risk increases with gearing level, and could alter important risk management decisions.

Keywords: Parameter estimation risk; Bayesian option pricing; Greeks; Probability of default (search for similar items in EconPapers)
JEL-codes: C53 G13 G17 G32 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:53:y:2017:i:c:p:80-93

DOI: 10.1016/j.irfa.2017.08.004

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