Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 7th International Conference on Economic Management and Green Development
Series Vol. 31 , 10 November 2023
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A Study of Bayesian Quantile Regression for Forecasting RMB Exchange Rates
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Abstract
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.
Keywords
Bayesian quantile regression, forecasting performance, financial markets
References
1. Ahmed, S. (2009). Are Chinese exports sensitive to changes in the exchange rate? FRB International Finance Discussion Paper, (987).
2. Yu, K., Lu, Z., & Stander, J. (2003). Quantile regression: applications and current research areas. Journal of the Royal Statistical Society: Series D (The Statistician), 52(3), 331-350.
3. Yang, Y., Li, S., Li, W., & Qu, M. (2018). Power load probability density forecasting using Gaussian process quantile regression. Applied Energy, 213, 499-509.
4. He, Y., Qin, Y., Wang, S., Wang, X., & Wang, C. (2019). Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network. Applied Energy, 233, 565-575.
5. Van de Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., ... & Yau, C. (2021). Bayesian statistics and modeling. Nature Reviews Methods Primers, 1(1), 1.
6. Glickman, M. E., & Van Dyk, D. A. (2007). Basic Bayesian methods. Topics in Biostatistics, 319-338.
7. Coleman, T. (2011). A practical guide to risk management. CFA Institute Research Foundation M2011-2.
8. Verbano, C., & Venturini, K. (2011). Development paths of risk management: approaches, methods, and fields of application. Journal of Risk Research, 14(5), 519-550.
9. Karandikar, R. L. (2006). On the markov chain monte carlo (MCMC) method. Sadhana, 31, 81-104.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 7th International Conference on Economic Management and Green Development
- ISBN (Print)
- 978-1-83558-083-7
- ISBN (Online)
- 978-1-83558-084-4
- Published Date
- 10 November 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/31/20231487
- Copyright
- 10 November 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited