Series Vol. 31 , 10 November 2023
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Accurate forecasting of the RMB exchange rate is crucial for global financial market participants. This study proposes a Bayesian quantile regression approach to enhance the forecasting method. This paper uses RMB and US dollar exchange rate data from the State Administration of Foreign Exchange from 2018 to 2022 to build a Bayesian quantile regression model and empirically analyze the RMB exchange rate forecast. The results show that the proposed Bayesian quantile regression model yields accurate forecasts, with a root mean squared error (RMSE) of 1.8329 and a mean absolute error (MAE) of 1.2988. Furthermore, robustness and sensitivity analyses confirm the model's reliability. The findings of this study have practical implications for financial market participants and policymakers in managing and responding to foreign exchange risk.
Bayesian quantile regression, forecasting performance, financial markets
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.