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Early Warning of Chinese Yuan’s Exchange Rate Fluctuation and Value at Risk Measure Using Neural Network Joint Optimization Algorithm

Author

Listed:
  • Zhaoyi Xu

    (Hunan University)

  • Yuqing Zeng

    (Jinan University)

  • Yangrong Xue

    (Yangtze Delta Region Research Institute of Tsinghua University)

  • Shenggang Yang

    (Hunan University)

Abstract

The aims are to analyze the fluctuation forecast of exchange rate markets, including the Chinese Yuan (CNY), and discuss applying the neural network model in the Value at Risk (VaR) measure. Therefore, six exchange rate markets are selected as the research objects, with the CNY exchange rate market as the main body, to analyze and explain the exchange rate fluctuation risks. Second, based on the overview of the neural network model, the Deep Belief Network (DBN), Multilayer Perceptron (MLP), and Long Short-Term Memory Network (LSTM) are introduced, and a VaR method based on risk measurement is proposed. Finally, based on the number of excess days (Exc) and the Kupiec test, the VaR measure results under different models are analyzed. Results demonstrate that the CNY exchange rate market’s historical data are relatively concentrated, with minor fluctuations, and the overall change is a sharp right shift. Compared with the benchmark model Generalized AutoRegressive Conditional Heteroskedasticity, the three neural network models show excellent risk measurement performance for different exchange rate markets. Based on Exc, the DBN model has the optimal risk forecast performance. In the CNY exchange rate market, the Exc values corresponding to the DBN and LSTM models are small, and the forecast performance is fair. Based on the Kupiec test, in addition to the Great Britain Pound exchange rate market, the three neural network models perform well in measuring the risks of the other five exchange rate markets. Besides, the MLP model has the optimal performance in measuring the CNY risks. Hence, the neural network models have excellent applicability in measuring the risks of exchange rate markets.

Suggested Citation

  • Zhaoyi Xu & Yuqing Zeng & Yangrong Xue & Shenggang Yang, 2022. "Early Warning of Chinese Yuan’s Exchange Rate Fluctuation and Value at Risk Measure Using Neural Network Joint Optimization Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1293-1315, December.
    • Handle: RePEc:kap:compec:v:60:y:2022:i:4:d:10.1007_s10614-021-10144-3
    DOI: 10.1007/s10614-021-10144-3
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    References listed on IDEAS

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    1. Guglielmo Maria Caporale & Luis Gil-Alana & Alex Plastun, 2017. "Searching for Inefficiencies in Exchange Rate Dynamics," Computational Economics, Springer;Society for Computational Economics, vol. 49(3), pages 405-432, March.
    2. Guilherme Armando Almeida Pereira & Álvaro Veiga, 2019. "Periodic Copula Autoregressive Model Designed to Multivariate Streamflow Time Series Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(10), pages 3417-3431, August.
    3. Yuchen Zhang & Shigeyuki Hamori, 2020. "The Predictability of the Exchange Rate When Combining Machine Learning and Fundamental Models," JRFM, MDPI, vol. 13(3), pages 1-16, March.
    4. Sharif Mozumder & M. Humayun Kabir & Michael Dempsey, 2018. "Pricing and hedging options with GARCH-stable proxy volatilities," Applied Economics, Taylor & Francis Journals, vol. 50(56), pages 6034-6046, December.
    5. Bi, Junna & Cai, Jun, 2019. "Optimal investment–reinsurance strategies with state dependent risk aversion and VaR constraints in correlated markets," Insurance: Mathematics and Economics, Elsevier, vol. 85(C), pages 1-14.
    6. Huseyin Ince & Ali Fehim Cebeci & Salih Zeki Imamoglu, 2019. "An Artificial Neural Network-Based Approach to the Monetary Model of Exchange Rate," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 817-831, February.
    7. Cao, Guangxi & Zhang, Qi & Li, Qingchen, 2017. "Causal relationship between the global foreign exchange market based on complex networks and entropy theory," Chaos, Solitons & Fractals, Elsevier, vol. 99(C), pages 36-44.
    8. Stelios Bekiros & Nikolaos Loukeris & Iordanis Eleftheriadis & Christos Avdoulas, 2019. "Tail-Related Risk Measurement and Forecasting in Equity Markets," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 783-816, February.
    9. Shuaiqiang Liu & Cornelis W. Oosterlee & Sander M. Bohte, 2019. "Pricing Options and Computing Implied Volatilities using Neural Networks," Risks, MDPI, vol. 7(1), pages 1-22, February.
    10. 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.
    11. Marc Sanchez-Roger & Esther Puyol-Antón, 2021. "Digital Bank Runs: A Deep Neural Network Approach," Sustainability, MDPI, vol. 13(3), pages 1-18, February.
    12. Joon Yul Choi & Tae Keun Yoo & Jeong Gi Seo & Jiyong Kwak & Terry Taewoong Um & Tyler Hyungtaek Rim, 2017. "Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-16, November.
    13. Edward M. H. Lin & Edward W. Sun & Min-Teh Yu, 2018. "Systemic risk, financial markets, and performance of financial institutions," Annals of Operations Research, Springer, vol. 262(2), pages 579-603, March.
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