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Reference: Yasuhiro Omori, Siddhartha Chib, Neil Shephard et al., (2007). Stochastic Volatility with Leverage: Fast and Efficient Likelihood Inference. Journal of Econometrics, 140 (2), 425 - 449.Citable link to this page:

 

Stochastic Volatility with Leverage: Fast and Efficient Likelihood Inference.

Abstract: This paper is concerned with the Bayesian analysis of stochastic volatility (SV) models with leverage. Specifically, the paper shows how the often used Kim et al., [1998. Stochastic volatility: likelihood inference and comparison with ARCH models. Review of Economic Studies 65, 361-393] method that was developed for SV models without leverage can be extended to models with leverage. The approach relies on the novel idea of approximating the joint distribution of the outcome and volatility innovations by a suitably constructed ten-component mixture of bivariate normal distributions. The resulting posterior distribution is summarized by MCMC methods and the small approximation error in working with the mixture approximation is corrected by a reweighting procedure. The overall procedure is fast and highly efficient. We illustrate the ideas on daily returns of the Tokyo Stock Price Index. Finally, extensions of the method are described for superposition models (where the log-volatility is made up of a linear combination of heterogenous and independent autoregressions) and heavy-tailed error distributions (student and log-normal).

Bibliographic Details

Host: Journal of Econometricssee more from them

Issue Date: 2007Identifiers

Doi: https://doi.org/10.1016/j.jeconom.2006.07.008

Urn: uuid:e9199508-29f0-4865-aa3e-18b06e25934f Item Description

Type: info:eu-repo/semantics/article;

Language: en

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Author: Yasuhiro Omori - - - Siddhartha Chib - - - Neil Shephard - - - Jouchi Nakajima - - - - Bibliographic Details Host: Journal of Eco

Source: https://ora.ox.ac.uk/objects/uuid:e9199508-29f0-4865-aa3e-18b06e25934f



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