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Raggi, Davide (2004) Adaptive MCMC Methods for Inference on Discretely Observed Affine Jump Diffusion Models. [Working Paper] WORKING PAPER SERIES, 1/2004 . , PADOVA (Inedito)

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Abstract (english)

In the present paper we generalize in a Bayesian framework the inferential solution proposed by Eraker, Johannes & Polson (2003) for stochastic volatility models with jumps and affine structure. We will use an adaptive sampling methodology known as Delayed Rejection suggested in Tierney & Mira (1999) in a Markov Chain Monte Carlo settings in order to reduce the asymptotic variance of the estimates. Furthermore, the use of a particle filtering procedure allows to compute the Bayes factor.


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EPrint type:Working Paper
Anno di Pubblicazione:01 January 2004
Key Words:Jump Diffusion, Adaptive MCMC, Particle Filters, Bayes factor.
Settori scientifico-disciplinari MIUR:Area 13 - Scienze economiche e statistiche > SECS-S/01 Statistica
Struttura di riferimento:Dipartimenti > Dipartimento di Scienze Statistiche
Codice ID:7060
Depositato il:09 Oct 2014 10:20
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