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

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%.

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Publication Details

Journal
International Journal of Economics and Financial Issues
Published
2024-12-06
DOI
https://doi.org/10.32479/ijefi.17618
Primary Topic
Financial Risk and Volatility Modeling
Type
article
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article

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

Siphat Lim
International Journal of Economics and Financial Issues
Financial Risk and Volatility Modeling
article

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

Siphat Lim
article en

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%.

International Journal of Economics and Financial IssuesVol. 15(1)
Documentation Center of Cambodia (KH), CamEd Business School (KH)
Partnerships for the goals
Openalex Percentile: Top 70%
Financial Risk and Volatility Modeling
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