IDEAS home Printed from https://ideas.repec.org/a/sae/manlab/v49y2024i4p679-703.html
   My bibliography  Save this article

Exchange Rate Models and the Management of Forex Losses in Ghana: Modelling Exchange Rate Volatilities for Businesses

Author

Listed:
  • Abdul-Rashid Abdul-Rahaman
  • Coleman Martha
  • Emmanuel Caesar Ayamba

Abstract

Using the Self-exciting Threshold Autoregressive Model (SETAR_M) and linear models such as the vector error correction model (VECM), and univariate models, this article specifies forecasting models for exchange rate volatilities in Ghana and compares their forecasts accuracy using Diebold–Mariano and Pesaran-Timmermann tests statistics. The relevance of this research is to equip business owners and businesses on managing forex losses and to reduce their impact on profits, productivity and employment in high volatile and unstable currency environments. The research concludes that the non-linear SETAR model is superior to the linear models in predicting short-term volatilities in exchange rates, while the fundamentally based linear model is superior for predicting long-term volatility in exchange rates. Therefore, short-term business commitments or transactions such as raw material purchases, cash expenses or incomes in foreign currencies should be planned or managed using SETAR or a non-linear model, whereas long-term contractual obligations like futures and forward contracts should be planned with a fundamentally based multivariate linear model.

Suggested Citation

  • Abdul-Rashid Abdul-Rahaman & Coleman Martha & Emmanuel Caesar Ayamba, 2024. "Exchange Rate Models and the Management of Forex Losses in Ghana: Modelling Exchange Rate Volatilities for Businesses," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 49(4), pages 679-703, November.
  • Handle: RePEc:sae:manlab:v:49:y:2024:i:4:p:679-703
    DOI: 10.1177/0258042X241233043
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0258042X241233043
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0258042X241233043?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Salachas, Evangelos N. & Laopodis, Nikiforos T. & Kouretas, Georgios P., 2017. "The bank-lending channel and monetary policy during pre- and post-2007 crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 47(C), pages 176-187.
    2. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    3. Lucio Sarno & Giorgio Valente & Mark E. Wohar, 2004. "Monetary Fundamentals and Exchange Rate Dynamics under Different Nominal Regimes," Economic Inquiry, Western Economic Association International, vol. 42(2), pages 179-193, April.
    4. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
    5. Mohamed Safouane Ben Aissa & Mohamed Boutahar & Jamel Jouini, 2004. "Bai and Perron's and spectral density methods for structural change detection in the US inflation process," Applied Economics Letters, Taylor & Francis Journals, vol. 11(2), pages 109-115.
    6. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    7. Carriero, A. & Kapetanios, G. & Marcellino, M., 2009. "Forecasting exchange rates with a large Bayesian VAR," International Journal of Forecasting, Elsevier, vol. 25(2), pages 400-417.
    8. Maravall, Agustin, 1983. "An Application of Nonlinear Time Series Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(1), pages 66-74, January.
    9. Jushan Bai & Pierre Perron, 2003. "Critical values for multiple structural change tests," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 72-78, June.
    10. Cheung, Yin-Wong & Chinn, Menzie D. & Pascual, Antonio Garcia, 2005. "Empirical exchange rate models of the nineties: Are any fit to survive?," Journal of International Money and Finance, Elsevier, vol. 24(7), pages 1150-1175, November.
    11. Bruce Hansen, 1999. "Testing for Linearity," Journal of Economic Surveys, Wiley Blackwell, vol. 13(5), pages 551-576, December.
    12. Engel, Charles & Hamilton, James D, 1990. "Long Swings in the Dollar: Are They in the Data and Do Markets Know It?," American Economic Review, American Economic Association, vol. 80(4), pages 689-713, September.
    13. Hansen,B.E., 1999. "Testing for linearity," Working papers 7, Wisconsin Madison - Social Systems.
    14. Tanya Molodtsova & David H. Papell, 2013. "Taylor Rule Exchange Rate Forecasting during the Financial Crisis," NBER International Seminar on Macroeconomics, University of Chicago Press, vol. 9(1), pages 55-97.
    15. Lee, Hsiu-Yun & Chen, Show-Lin, 2006. "Why use Markov-switching models in exchange rate prediction?," Economic Modelling, Elsevier, vol. 23(4), pages 662-668, July.
    16. Emmanuel Numapau Gyamfi & Kwabena A. Kyei, 2016. "Modeling Stock Market Returns under Self-exciting Threshold Autoregressive Model: Evidence from West Africa," International Journal of Economics and Financial Issues, Econjournals, vol. 6(3), pages 1194-1199.
    17. Mr. Jaewoo Lee & Mr. H. Takizawa & Mr. David Hauner, 2011. "In Which Exchange Rate Models Do Forecasters Trust?," IMF Working Papers 2011/116, International Monetary Fund.
    18. Ince, Onur & Molodtsova, Tanya, 2017. "Rationality and forecasting accuracy of exchange rate expectations: Evidence from survey-based forecasts," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 47(C), pages 131-151.
    19. Paula Hill, 2006. "Ownership Structure and IPO Underpricing," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(1-2), pages 102-126.
    20. Leitch, Gordon & Tanner, J Ernest, 1991. "Economic Forecast Evaluation: Profits versus the Conventional Error Measures," American Economic Review, American Economic Association, vol. 81(3), pages 580-590, June.
    21. Paula Hill, 2006. "Ownership Structure and IPO Underpricing," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(1‐2), pages 102-126, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ince, Onur & Molodtsova, Tanya & Papell, David H., 2016. "Taylor rule deviations and out-of-sample exchange rate predictability," Journal of International Money and Finance, Elsevier, vol. 69(C), pages 22-44.
    2. David G. McMillan, 2017. "Stock return predictability: the role of inflation and threshold dynamics," International Review of Applied Economics, Taylor & Francis Journals, vol. 31(3), pages 357-375, May.
    3. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    4. Jaehun Chung & Yongmiao Hong, 2007. "Model-free evaluation of directional predictability in foreign exchange markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 855-889.
    5. Byrne, Joseph P. & Korobilis, Dimitris & Ribeiro, Pinho J., 2016. "Exchange rate predictability in a changing world," Journal of International Money and Finance, Elsevier, vol. 62(C), pages 1-24.
    6. David Alan Peel & Pantelis Promponas, 2016. "Forecasting the nominal exchange rate movements in a changing world. The case of the U.S. and the U.K," Working Papers 144439514, Lancaster University Management School, Economics Department.
    7. Syllignakis, Manolis N. & Kouretas, Georgios P., 2011. "Markov-switching regimes and the monetary model of exchange rate determination: Evidence from the Central and Eastern European markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(5), pages 707-723.
    8. Martin McCarthy, Stephen Snudden, 2024. "Forecasts of Period-Average Exchange Rates: New Insights from Real-Time Daily Data," LCERPA Working Papers jc0148, Laurier Centre for Economic Research and Policy Analysis, revised Oct 2024.
    9. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    10. Kim, Young Min & Lee, Seojin, 2020. "Exchange rate predictability: A variable selection perspective," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 117-134.
    11. Jaehun Chung & Yongmiao Hong, 2013. "Model-Free Evaluation of Directional Predictability in Foreign Exchange," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    12. Pincheira-Brown, Pablo & Neumann, Federico, 2020. "Can we beat the Random Walk? The case of survey-based exchange rate forecasts in Chile," Finance Research Letters, Elsevier, vol. 37(C).
    13. Sarthak Behera & Hyeongwoo Kim, 2019. "Forecasting Dollar Real Exchange Rates and the Role of Real Activity Factors," Auburn Economics Working Paper Series auwp2019-04, Department of Economics, Auburn University.
    14. Kelly Burns, 2016. "A Reconsideration of the Meese-Rogoff Puzzle: An Alternative Approach to Model Estimation and Forecast Evaluation," Multinational Finance Journal, Multinational Finance Journal, vol. 20(1), pages 41-83, March.
    15. Joscha Beckmann & Dionysius Glycopantis & Keith Pilbeam, 2018. "The dollar–euro exchange rate and monetary fundamentals," Empirical Economics, Springer, vol. 54(4), pages 1389-1410, June.
    16. Eric Hillebrand & Jakob Mikkelsen & Lars Spreng & Giovanni Urga, 2020. "Exchange Rates and Macroeconomic Fundamentals: Evidence of Instabilities from Time-Varying Factor Loadings," CREATES Research Papers 2020-19, Department of Economics and Business Economics, Aarhus University.
    17. repec:wyi:journl:002068 is not listed on IDEAS
    18. Firat Melih Yilmaz & Ozer Arabaci, 2021. "Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 217-245, January.
    19. Akosah, Nana Kwame & Alagidede, Imhotep Paul & Schaling, Eric, 2020. "Testing for asymmetry in monetary policy rule for small-open developing economies: Multiscale Bayesian quantile evidence from Ghana," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    20. Gürkaynak, Refet S. & Kısacıkoğlu, Burçin & Lee, Sang Seok, 2022. "Exchange rate and inflation under weak monetary policy: Turkey verifies theory," CFS Working Paper Series 679, Center for Financial Studies (CFS).
    21. Dal Bianco, Marcos & Camacho, Maximo & Perez Quiros, Gabriel, 2012. "Short-run forecasting of the euro-dollar exchange rate with economic fundamentals," Journal of International Money and Finance, Elsevier, vol. 31(2), pages 377-396.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:manlab:v:49:y:2024:i:4:p:679-703. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: http://www.xlri.ac.in/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.