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OPEC News and Exchange Rate Forecasting Using Dynamic Bayesian Learning

Author

Listed:
  • Xin Sheng

    (Lord Ashcroft International Business School, Anglia Ruskin University, Chelmsford, CM1 1SQ, United Kingdom)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

  • Afees A. Salisu

    (Centre for Econometric & Allied Research, University of Ibadan, Ibadan, Nigeria)

  • Elie Bouri

    (Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon)

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.

Suggested Citation

  • Xin Sheng & Rangan Gupta & Afees A. Salisu & Elie Bouri, 2021. "OPEC News and Exchange Rate Forecasting Using Dynamic Bayesian Learning," Working Papers 202101, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202101
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    Cited by:

    1. Taufeeque Ahmad Siddiqui & Haseen Ahmed & Mohammad Naushad & Uzma Khan, 2023. "The Relationship between Oil Prices and Exchange Rate: A Systematic Literature Review," International Journal of Energy Economics and Policy, Econjournals, vol. 13(3), pages 566-578, May.
    2. Gulati, Vishal, 2023. "Bibliometric review of research on exchange rate predictability and fundamentals," Finance Research Letters, Elsevier, vol. 58(PA).

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    More about this item

    Keywords

    OPEC News; Exchange Rate Forecasting; Bayesian Dynamic Learning;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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