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A survey of sampling-based Bayesian analysis of financial data

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Author Info
James M. Sfiridis
Alan E. Gelfand
Abstract

The capability of implementing a complete Bayesian analysis of experimental data has emerged over recent years due to computational advances developed within the statistical community. The objective of this paper is to provide a practical exposition of these methods in the illustrative context of a financial event study. The customary assumption of Gaussian errors underlying development of the model is later supplemented by considering Student-t errors, thus permitting a Bayesian sensitivity analysis. The supplied data analysis illustrates the advantages of the sampling-based Bayesian approach in allowing investigation of quantities beyond the scope of classical methods.

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Publisher Info
Article provided by Taylor and Francis Journals in its journal Applied Mathematical Finance.

Volume (Year): 9 (2002)
Issue (Month): 4 (December)
Pages: 273-291
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Handle: RePEc:taf:apmtfi:v:9:y:2002:i:4:p:273-291

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Related research
Keywords: Event Studies; Inference; Bayesian; Markov Chain Monte Carlo; Gibbs Sampler;

References listed on IDEAS
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  12. Giaccotto, Carmelo & Sfiridis, James M., 1996. "Hypothesis testing in event studies: The case of variance changes," Journal of Economics and Business, Elsevier, vol. 48(4), pages 349-370, October. [Downloadable!] (restricted)
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