IDEAS home Printed from https://ideas.repec.org/a/eco/journ1/v15y2024i1id17618.html

Modelling Cambodia's Foreign Exchange Rate Dynamics: A Markov-Switching Autoregressive Model

Author

Listed:
  • Lim, Siphat

    (CamEd Business School, Phnom Penh, Cambodia)

Abstract

The analysis of the daily fluctuations in the foreign exchange rate between the Khmer Riel and the US dollar was performed utilizing a Markov- switching autoregressive model. This study covered a time frame from January 04, 2005, to August 22, 2024, encompassing a total of 4989 days of data. The research findings revealed that the MS(2)-AR(1) model emerged as the most appropriate model for the analysis. The empirical results from both state 1 and state 2 models demonstrated that the intercept term, along with the AR(1) component, exerted a statistically significant positive effect on the exchange rate at a 1% significance level. Furthermore, the intercept term, which represents the average exchange rate, along with the volatility of the exchange rate in the state 2 model, was found to be higher than that observed in the state 1 model. The analysis of the probability transition matrix indicated that there was a 15.53% likelihood for FX to transition from state 2 to state 1. In contrast, the probability of FX departing from state 1 and returning to state 2 was recorded at 29.34%. Additionally, the chance of FX maintaining its position in state 1 was assessed to be 70.66%, while the probability of it remaining in state 2 was significantly higher at 84.47%.

Suggested Citation

  • Lim, Siphat, 2024. "Modelling Cambodia's Foreign Exchange Rate Dynamics: A Markov-Switching Autoregressive Model," International Journal of Economics and Financial Issues, International Journal of Economics and Financial Issues, vol. 15(1), pages 330-336, December.
  • Handle: RePEc:eco:journ1:v:15:y:2024:i:1:id:17618
    DOI: 10.32479/ijefi.17618
    as

    Download full text from publisher

    File URL: https://econjournals.com/index.php/ijefi/article/download/17618/8497
    Download Restriction: no

    File URL: https://libkey.io/10.32479/ijefi.17618?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. Seuk Wai Phoong & Seuk Yen Phoong & Shi Ling Khek, 2022. "Systematic Literature Review With Bibliometric Analysis on Markov Switching Model: Methods and Applications," SAGE Open, , vol. 12(2), pages 21582440221, April.
    2. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    3. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
    4. Aleksandr V. Gevorkyan, 2019. "Exchange market pressure and primary commodity – exporting emerging markets," Applied Economics, Taylor & Francis Journals, vol. 51(22), pages 2390-2412, May.
    5. Josh Stillwagon & Peter Sullivan, 2020. "Markov switching in exchange rate models: will more regimes help?," Empirical Economics, Springer, vol. 59(1), pages 413-436, July.
    6. 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.
    7. Maud Korley & Evangelos Giouvris, 2021. "The Regime-Switching Behaviour of Exchange Rates and Frontier Stock Market Prices in Sub-Saharan Africa," JRFM, MDPI, vol. 14(3), pages 1-30, March.
    8. Michel Cyrille Samba, 2018. "Exchange Market Pressure in the Central African Economic and Monetary Community (CAEMC) Area: Empirical Assessment of the Macroeconomic Determinants," International Economic Journal, Taylor & Francis Journals, vol. 32(3), pages 470-482, July.
    9. Klaus Adam & Albert Marcet & Johannes Beutel, 2017. "Stock Price Booms and Expected Capital Gains," American Economic Review, American Economic Association, vol. 107(8), pages 2352-2408, August.
    10. Francis Y. Kumah, 2011. "A Markov‐switching approach to measuring exchange market pressure," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 16(2), pages 114-130, April.
    11. Akaike, Hirotugu, 1981. "Likelihood of a model and information criteria," Journal of Econometrics, Elsevier, vol. 16(1), pages 3-14, May.
    12. Dai, Wei & Serletis, Apostolos, 2019. "On the Markov switching welfare cost of inflation," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
    13. Cheung, Yin-Wong & Erlandsson, Ulf G., 2005. "Exchange Rates and Markov Switching Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 314-320, July.
    14. Utkarsh Kumar & Wasim Ahmad & Gazi Salah Uddin, 2024. "Bayesian Markov switching model for BRICS currencies' exchange rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2322-2340, September.
    15. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    16. Taofeek Olusola Ayinde & Abiodun S.Bankole & Oluwatosin Adeniyi, 2020. "Modelling central bank behaviour in Nigeria : A Markov-switching approach," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 20(4), pages 213-221.
    17. Nikolsko-Rzhevskyy, Alex & Prodan, Ruxandra, 2012. "Markov switching and exchange rate predictability," International Journal of Forecasting, Elsevier, vol. 28(2), pages 353-365.
    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. 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.
    2. Suman Das & Saikat Sinha Roy, 2021. "Predicting regime switching in BRICS currency volatility: a Markov switching autoregressive approach," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 48(2), pages 165-180, June.
    3. Johanna Posch & Fabio Rumler, 2015. "Semi‐Structural Forecasting of UK Inflation Based on the Hybrid New Keynesian Phillips Curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 145-162, March.
    4. Constandina Koki & Stefanos Leonardos & Georgios Piliouras, 2020. "Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models," Papers 2011.03741, arXiv.org, revised Dec 2020.
    5. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    6. Nikolsko-Rzhevskyy, Alex & Prodan, Ruxandra, 2012. "Markov switching and exchange rate predictability," International Journal of Forecasting, Elsevier, vol. 28(2), pages 353-365.
    7. T. G. Saji, 2019. "Can BRICS Form a Currency Union? An Analysis under Markov Regime-Switching Framework," Global Business Review, International Management Institute, vol. 20(1), pages 151-165, February.
    8. Koki, Constandina & Leonardos, Stefanos & Piliouras, Georgios, 2022. "Exploring the predictability of cryptocurrencies via Bayesian hidden Markov models," Research in International Business and Finance, Elsevier, vol. 59(C).
    9. Levent Bulut, 2017. "Does Statistical Significance Help to Evaluate Predictive Performance of Competing Models?," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 1(1), pages 1-13.
    10. Oliveira, Lucas M. & Alencar, Airlane P., 2025. "When timing matters: Regime-dependent delays in exchange rate fundamentals," Finance Research Letters, Elsevier, vol. 86(PG).
    11. Konstantinos N. Konstantakis & Ioannis G. Melissaropoulos & Theodoros Daglis & Panayotis G. Michaelides, 2023. "The euro to dollar exchange rate in the Covid‐19 era: Evidence from spectral causality and Markov‐switching estimation," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 2037-2055, April.
    12. Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.
    13. Chen, Qitong & Hong, Yongmiao & Li, Haiqi, 2024. "Time-varying forecast combination for factor-augmented regressions with smooth structural changes," Journal of Econometrics, Elsevier, vol. 240(1).
    14. Ayse Kabukcuoglu & Enrique Martínez-García, 2016. "What Helps Forecast U.S. Inflation?—Mind the Gap!," Koç University-TUSIAD Economic Research Forum Working Papers 1615, Koc University-TUSIAD Economic Research Forum.
    15. Hyeongwoo Kim, 2018. "Fiscal Policy, Wages, and Jobs in the U.S," Auburn Economics Working Paper Series auwp2018-02, Department of Economics, Auburn University.
    16. Marc Burri & Daniel Kaufmann, 2020. "A daily fever curve for the Swiss economy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 156(1), pages 1-11, December.
    17. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    18. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    19. Cavit Pakel & Neil Shephard & Kevin Sheppard, 2009. "Nuisance parameters, composite likelihoods and a panel of GARCH models," OFRC Working Papers Series 2009fe03, Oxford Financial Research Centre.
    20. Ashley, Richard, 2003. "Statistically significant forecasting improvements: how much out-of-sample data is likely necessary?," International Journal of Forecasting, Elsevier, vol. 19(2), pages 229-239.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eco:journ1:v:15:y:2024:i:1:id:17618. 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: Monica Sinhat (email available below). General contact details of provider: https://econjournals.com/index.php/ijefi .

    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.