A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation

Xibin Zhang (), Robert Brooks and Maxwell King

Journal of Econometrics, 2009, vol. 153, issue 1, 21-32

Abstract: This paper presents a Bayesian approach to bandwidth selection for multivariate kernel regression. A Monte Carlo study shows that under the average squared error criterion, the Bayesian bandwidth selector is comparable to the cross-validation method and clearly outperforms the bootstrapping and rule-of-thumb bandwidth selectors. The Bayesian bandwidth selector is applied to a multivariate kernel regression model that is often used to estimate the state-price density of Arrow-Debreu securities with the S&P 500 index options data and the DAX index options data. The proposed Bayesian bandwidth selector represents a data-driven solution to the problem of choosing bandwidths for the multivariate kernel regression involved in the nonparametric estimation of the state-price density pioneered by Aït-Sahalia and Lo [Aït-Sahalia, Y., Lo, A.W., 1998. Nonparametric estimation of state-price densities implicit in financial asset prices. The Journal of Finance, 53, 499, 547.]

Keywords: Black-Scholes; formula; Bootstrapping; Cross-validation; Markov; chain; Monte; Carlo; Time; to; maturity (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

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Working Paper: A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:153:y:2009:i:1:p:21-32

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