OPEC News and Exchange Rate Forecasting Using Dynamic Bayesian Learning

Sheng, Xin, Gupta, Rangan, Salisu, Afees A. and Bouri, Elie (2022) OPEC News and Exchange Rate Forecasting Using Dynamic Bayesian Learning. Finance Research Letters, 45. p. 102125. ISSN 1544-6123

Preview

Text Accepted Version Available under the following license: Creative Commons Attribution Non-commercial No Derivatives. Download (918kB) | Preview

Abstract

We consider whether a newspaper article count index related to the organization of the petroleum exporting countries (OPEC), which rises in response to important OPEC meetings and events connected with OPEC production levels, contains predictive power for the foreign exchange rates of G10 countries. The applied Bayesian inference methodology synthesizes a wide array of established approaches to modelling exchange rate dynamics, whereby various vector-autoregressive models are considered. Monthly data from 1996:01 to 2020:08 (given an in-sample of 1986:02 to 1995:12), shows that incorporating the OPEC news-related index into the proposed methodology leads to statistical gains in out-of-sample forecasts.

Item Type:	Journal Article
Keywords:	Opec news, Exchange rate forecasting, Bayesian Dynamic Learning
Faculty:	Faculty of Business & Law
SWORD Depositor:	Symplectic User
Depositing User:	Symplectic User
Date Deposited:	10 Mar 2022 10:35
Last Modified:	12 May 2022 01:02
URI:	https://arro.anglia.ac.uk/id/eprint/707390

Actions (login required)

Edit Item