Stochastic volatility with leverage: fast likelihood inference

Neil Shephard, Yashurio Omori, Faculty of Economics, University of Tokyo, Siddhartha Chib, Olin School of Business, Washington University, Jouchi Nakajima, Faculty of Economics and University of Tokyo

No 2004-FE-16, Economics Series Working Papers from University of Oxford, Department of Economics

Abstract: Kim, Shephard, and Chib (1998) provided a Bayesian analysis of stochastic volatility models based on a fast and reliable Markov chain Monte Carlo (MCMC) algorithm. Their method rules out the leverage effect, which is known to be important in applications. Despite this, their basic method has been extensively used in the financial economics literature and more recently in macroeconometrics. In this paper we show how the basic approach can be extended in a novel way to stochastic volatility models with leverage without altering the essence of the original approach. Several illustrative examples are provided.

Keywords: Leverage Effect; Markov Chain Monte Carlo; Mixture Sampler; Stochastic Volatility; Stock Returns (search for similar items in EconPapers)
Date: 2004-09-01
References: Add references at CitEc
Citations: View citations in EconPapers (3)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Working Paper: Stochastic volatility with leverage: fast likelihood inference (2004)
Working Paper: Stochastic Volatility with Leverage: Fast Likelihood Inference (2004)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oxf:wpaper:2004-fe-16

Access Statistics for this paper

More papers in Economics Series Working Papers from University of Oxford, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Anne Pouliquen ( this e-mail address is bad, please contact ).