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Bayesian predictive distributions of oil returns using mixed data sampling volatility models
Department of Quantitative Methods, CUNEF Universidad, Calle Pirineos 55, 28040 Madrid, Spain.ORCID iD: 0000-0002-7117-6906
Linköping University, Department of Management and Engineering, Production Economics. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-0682-8584
Discipline of Business Analytics, the University of Sydney Business School, Australia.
2023 (English)In: Resources policy, ISSN 0301-4207, E-ISSN 1873-7641, Vol. 86, article id 104167Article in journal (Refereed) Published
Abstract [en]

This study explores the benefits of incorporating fat-tailed innovations, asymmetric volatility response, and an extended information set into crude oil return modeling and forecasting. To this end, we utilize standard volatility models such as Generalized Autoregressive Conditional Heteroskedastic (GARCH), Generalized Autoregressive Score (GAS), and Stochastic Volatility (SV), along with Mixed Data Sampling (MIDAS) regressions, which enable us to incorporate the impacts of relevant financial/macroeconomic news into asset price movements. For inference and prediction, we employ an innovative Bayesian estimation approach called the density-tempered sequential Monte Carlo method. Our findings indicate that the inclusion of exogenous variables is beneficial for GARCH-type models while offering only a marginal improvement for GAS and SV-type models. Notably, GAS-family models exhibit superior performance in terms of in-sample fit, out-of-sample forecast accuracy, as well as Value-at-Risk and Expected Shortfall prediction.

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 86, article id 104167
Keywords [en]
ES; GARCH; GAS; Log marginal likelihood; MIDAS; SV; VaR
National Category
Economics Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-198443DOI: 10.1016/j.resourpol.2023.104167ISI: 001083792500001Scopus ID: 2-s2.0-85172936796OAI: oai:DiVA.org:liu-198443DiVA, id: diva2:1804462
Note

Funding: Spanish State Research Agency (Ministerio de Ciencia e Innovacion) [PID2022-138289 NB-I00]; Jan Wallanders and Tom Hedelius Foundation [BFV22-0005]; Swedish Research Council [2022-06725]

Available from: 2023-10-12 Created: 2023-10-12 Last updated: 2024-03-14Bibliographically approved

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Nguyen, Hoang

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