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

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Author Info
Xibin Zhang ()
Robert D. Brooks ()
Maxwell L. King ()

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Abstract

Multivariate kernel regression is an important tool for investigating the relationship between a response and a set of explanatory variables. It is generally accepted that the performance of a kernel regression estimator largely depends on the choice of bandwidth rather than the kernel function. This nonparametric technique has been employed in a number of empirical studies including the state-price density estimation pioneered by Aït-Sahalia and Lo (1998). However, the widespread usefulness of multivariate kernel regression has been limited by the difficulty in computing a data-driven bandwidth. In this paper, we present a Bayesian approach to bandwidth selection for multivariate kernel regression. A Markov chain Monte Carlo algorithm is presented to sample the bandwidth vector and other parameters in a multivariate kernel regression model. A Monte Carlo study shows that the proposed bandwidth selector is more accurate than the rule-of-thumb bandwidth selector known as the normal reference rule according to Scott (1992) and Bowman and Azzalini (1997). The proposed bandwidth selection algorithm is applied to a multivariate kernel regression model that is often used to estimate the state-price density of Arrow-Debreu securities. When applying the proposed method to the S&P 500 index options and the DAX index options, we find that for short-maturity options, the proposed Bayesian bandwidth selector produces an obviously different state-price density from the one produced by using a subjective bandwidth selector discussed in Aït-Sahalia and Lo (1998).

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Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 11/07.

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Length: 30 pages
Date of creation: Aug 2007
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Handle: RePEc:msh:ebswps:2007-11

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Related research
Keywords: Black-Scholes formula; Likelihood; Markov chain Monte Carlo; Posterior density.;

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Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
G12 - Financial Economics - - General Financial Markets - - - Asset Pricing

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References listed on IDEAS
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  6. Inci, Ahmet Can & Lu, Biao, 2004. "Exchange rates and interest rates: can term structure models explain currency movements?," Journal of Economic Dynamics and Control, Elsevier, vol. 28(8), pages 1595-1624, June. [Downloadable!] (restricted)
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    Other versions:
  8. Zhang, Xibin & King, Maxwell L. & Hyndman, Rob J., 2006. "A Bayesian approach to bandwidth selection for multivariate kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3009-3031, July. [Downloadable!] (restricted)
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  15. Kim, Sangjoon & Shephard, Neil & Chib, Siddhartha, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Blackwell Publishing, vol. 65(3), pages 361-93, July. [Downloadable!] (restricted)
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  16. Fernandes, Marcelo, 2006. "Financial crashes as endogenous jumps: estimation, testing and forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 30(1), pages 111-141, January. [Downloadable!] (restricted)
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  19. Backus, David & Foresi, Silverio & Zin, Stanley, 1998. "Arbitrage Opportunities in Arbitrage-Free Models of Bond Pricing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 13-26, January.
    Other versions:
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