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A New Forecasting Model for USD/CNY Exchange Rate

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  • Cai Zongwu

    (University of North Carolina at Charlotte)

  • Chen Linna

    (Xiamen University)

  • Fang Ying

    (Xiamen University)

Abstract

This paper models the return series of USD/CNY exchange rate by considering the conditional mean and conditional volatility simultaneously. An index type functional-coefficient model is adopted to model the conditional mean part and a GARCH type model with a policy dummy variable is applied to the conditional volatility model. We show that the government policy indeed has an impact on the exchange rate dynamic. To evaluate the out-of-sample forecasting ability, a prediction interval is computed by employing nonparametric conditional quantile regression. Our method outperforms other popular models in terms of various criteria.

Suggested Citation

  • Cai Zongwu & Chen Linna & Fang Ying, 2012. "A New Forecasting Model for USD/CNY Exchange Rate," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(3), pages 1-20, September.
    • Handle: RePEc:bpj:sndecm:v:16:y:2012:i:3:n:4
    DOI: 10.1515/1558-3708.1878
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    Cited by:

    1. Vespignani, Joaquin L. & Ratti, Ronald A., 2013. "Chinese Monetary Expansion and the US Economy," Working Papers 16874, University of Tasmania, Tasmanian School of Business and Economics, revised 05 Aug 2013.
    2. repec:zbw:bofitp:2012_002 is not listed on IDEAS
    3. Kang, Wensheng & Ratti, Ronald A. & Vespignani, Joaquin L., 2016. "Chinese liquidity increases and the U.S. economy," Economic Modelling, Elsevier, vol. 52(PB), pages 764-771.
    4. Fang, Ying & Huang, Shicheng & Niu, Linlin, 2012. "De facto currency baskets of China and East Asian economies : The rising weights," BOFIT Discussion Papers 2/2012, Bank of Finland, Institute for Economies in Transition.
    5. Zuzana Rowland & George Lazaroiu & Ivana Podhorská, 2020. "Use of Neural Networks to Accommodate Seasonal Fluctuations When Equalizing Time Series for the CZK/RMB Exchange Rate," Risks, MDPI, vol. 9(1), pages 1-21, December.
    6. Marek Vochozka & Jakub Horák & Petr Šuleř, 2019. "Equalizing Seasonal Time Series Using Artificial Neural Networks in Predicting the Euro–Yuan Exchange Rate," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    7. Vespignani, Joaquin L. & Ratti, Ronald A., 2016. "Not all international monetary shocks are alike for the Japanese economy," Economic Modelling, Elsevier, vol. 52(PB), pages 822-837.
    8. Nan Cai & Zongwu Cai & Ying Fang & Qiuhua Xu, 2015. "Forecasting major Asian exchange rates using a new semiparametric STAR model," Empirical Economics, Springer, vol. 48(1), pages 407-426, February.
    9. Zongwu Cai & Linna Chen & Ying Fang, 2015. "Semiparametric Estimation of Partially Varying-Coefficient Dynamic Panel Data Models," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 695-719, December.
    10. Fang, Ying & Huang, Shicheng & Niu, Linlin, 2012. "De facto currency baskets of China and East Asian economies: The rising weights," BOFIT Discussion Papers 2/2012, Bank of Finland Institute for Emerging Economies (BOFIT).
    11. Kang, Wensheng & Ratti, Ronald A. & Vespignani, Joaquin L., 2014. "Liquidity expansion in China and the U.S. economy," MPRA Paper 59338, University Library of Munich, Germany.
    12. Lin, Yong & Wang, Renyu & Gong, Xingyue & Jia, Guozhu, 2022. "Cross-correlation and forecast impact of public attention on USD/CNY exchange rate: Evidence from Baidu Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).

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