Dimitrakopoulos, S orcid.org/0000-0002-0043-180X and Kolossiatis, M (2020) Bayesian analysis of moving average stochastic volatility models: modeling in-mean effects and leverage for financial time series. Econometric Reviews, 39 (4). pp. 319-343. ISSN 0747-4938
Abstract
We propose a moving average stochastic volatility in mean model and a moving average stochastic volatility model with leverage. For parameter estimation, we develop efficient Markov chain Monte Carlo algorithms and illustrate our methods, using simulated and real data sets. We compare the proposed specifications against several competing stochastic volatility models, using marginal likelihoods and the observed-data Deviance information criterion. We also perform a forecasting exercise, using predictive likelihoods, the root mean square forecast error and Kullback-Leibler divergence. We find that the moving average stochastic volatility model with leverage better fits the four empirical data sets used.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2019 Taylor & Francis Group, LLC. This is an author produced version of an article published in Econometric Reviews . Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | In-mean effects; leverage; Markov chain Monte Carlo; moving average; stochastic volatility |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Economics Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 May 2019 11:01 |
Last Modified: | 02 Aug 2020 00:38 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/07474938.2019.1630075 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:146531 |