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Enhancing exchange rate volatility prediction accuracy: Assessing the influence of different indices on the USD/CNY exchange rate

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  • Luo, Tao
  • Zhang, Lixia
  • Sun, Huaping
  • Bai, Jiancheng

Abstract

In this paper, the GARCH-MIDAS model is used to empirically study the influence of several uncertainties on the volatility of the CNY-USD forex market. The sample and out-of-sample prediction results show that the nine uncertainty indexes significantly impact the volatility of the CNY-USD forex market and can improve the prediction effect of CNY-USD rate volatility. Then using model confidence set and Direction-of-change test out-of-sample test, it is found that Compared with China's Economic policy uncertainty and China's trade policy uncertainty, US Economic Policy Uncertainty and US trade policy uncertainty can improve the prediction accuracy of CNY-USD forex market volatility.

Suggested Citation

  • Luo, Tao & Zhang, Lixia & Sun, Huaping & Bai, Jiancheng, 2023. "Enhancing exchange rate volatility prediction accuracy: Assessing the influence of different indices on the USD/CNY exchange rate," Finance Research Letters, Elsevier, vol. 58(PB).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pb:s1544612323008553
    DOI: 10.1016/j.frl.2023.104483
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    1. 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.
    2. Song, Lu & Tian, Gengyu & Jiang, Yonghong, 2022. "Connectedness of commodity, exchange rate and categorical economic policy uncertainties — Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    3. Li, Yan & Liang, Chao & L.D. Huynh, Toan, 2022. "A new momentum measurement in the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    4. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    5. Yan, Xiang & Bai, Jiancheng & Li, Xiafei & Chen, Zhonglu, 2022. "Can dimensional reduction technology make better use of the information of uncertainty indices when predicting volatility of Chinese crude oil futures?," Resources Policy, Elsevier, vol. 75(C).
    6. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    7. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    8. Chen, Ting & Luo, Wenjie & Xiang, Xunyong, 2022. "Financial constraints, exchange rate changes and export price: Evidence from Chinese exporters," Finance Research Letters, Elsevier, vol. 48(C).
    9. Jiqian Wang & Yisu Huang & Feng Ma & Julien Chevallier, 2020. "Does high-frequency crude oil futures data contain useful information for predicting volatility in the US stock market? New evidence," Post-Print halshs-04250251, HAL.
    10. Ojeda-Joya, Jair & Romero, José Vicente, 2023. "Global uncertainty shocks and exchange-rate expectations in Latin America," Economic Modelling, Elsevier, vol. 120(C).
    11. Liang, Chao & Luo, Qin & Li, Yan & Huynh, Luu Duc Toan, 2023. "Global financial stress index and long-term volatility forecast for international stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    12. Zhou, Zhongbao & Fu, Zhangyan & Jiang, Yong & Zeng, Ximei & Lin, Ling, 2020. "Can economic policy uncertainty predict exchange rate volatility? New evidence from the GARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 34(C).
    13. Long, Shaobo & Zhang, Rui & Hao, Jing, 2022. "Asymmetric impact of Sino-US interest rate differentials and economic policy uncertainty ratio on RMB exchange rate," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
    14. Wang, Jiqian & Huang, Yisu & Ma, Feng & Chevallier, Julien, 2020. "Does high-frequency crude oil futures data contain useful information for predicting volatility in the US stock market? New evidence," Energy Economics, Elsevier, vol. 91(C).
    15. Zhang, Lixia & Bai, Jiancheng & Zhang, Yueyan & Cui, Can, 2023. "Global economic uncertainty and the Chinese stock market: Assessing the impacts of global indicators," Research in International Business and Finance, Elsevier, vol. 65(C).
    16. Jalal, Rubia & Gopinathan, R., 2023. "Time-frequency relationship between energy imports, energy prices, exchange rate, and policy uncertainties in India: Evidence from wavelet quantile correlation approach," Finance Research Letters, Elsevier, vol. 55(PB).
    17. Bai, Jiancheng & Han, Zhiyong & Rizvi, Syed Kumail Abbas & Naqvi, Bushra, 2023. "Green trade or green technology? The way forward for G-7 economies to achieve COP 26 targets while making competing policy choices," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    18. Zhang, Zitao & Qin, Yun, 2022. "Study on the nonlinear interactions among the international oil price, the RMB exchange rate and China's gold price," Resources Policy, Elsevier, vol. 77(C).
    19. Liu, Fengqin & Sim, Jae-yeon & Sun, Huaping & Edziah, Bless Kofi & Adom, Philip Kofi & Song, Shunfeng, 2023. "Assessing the role of economic globalization on energy efficiency: Evidence from a global perspective," China Economic Review, Elsevier, vol. 77(C).
    20. Chen, Juan & Xiao, Zuoping & Bai, Jiancheng & Guo, Hongling, 2023. "Predicting volatility in natural gas under a cloud of uncertainties," Resources Policy, Elsevier, vol. 82(C).
    21. 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.
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    More about this item

    Keywords

    Volatility prediction; Uncertainty; USD/CNY exchange rate;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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