OPEC news and exchange rate forecasting using dynamic Bayesian learning

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Authors

Sheng, Xin
Gupta, Rangan
Salisu, Afees A.
Bouri, Elie

Journal Title

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Publisher

Elsevier

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.

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Keywords

Organization of the petroleum exporting countries (OPEC), Opec news, Exchange rate forecasting, Bayesian dynamic learning

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Citation

Sheng, X., Gupta, R., Salisu, A.A. et al. 2022, 'OPEC news and exchange rate forecasting using dynamic Bayesian learning', Finance Research Letters, vol. 45, art. 102125, pp. 1-7, doi : 10.1016/j.frl.2021.102125.