IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v59y2022i4d10.1007_s10614-021-10172-z.html
   My bibliography  Save this article

Analysis of Early Warning of RMB Exchange Rate Fluctuation and Value at Risk Measurement Based on Deep Learning

Author

Listed:
  • Chunyi Lu

    (Shanghai Lixin University of Accounting and Finance)

  • Zhuoqi Teng

    (Henan Finance University)

  • Yu Gao

    (Qingdao University)

  • Renhong Wu

    (Guangdong Ocean University)

  • Md. Alamgir Hossain

    (Hajee Mohammad Danesh Science and Technology University)

  • Yuantao Fang

    (Shanghai Lixin University of Accounting and Finance)

Abstract

To improve the RMB exchange rate prediction and risk measurement, the RMB exchange rate prediction model is constructed based on deep learning approaches. Value at risk (VaR) risk measurement related data are used, and this model is combined with the autoregressive moving average model-generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) model to fabricate an integrated VaR risk measurement model. The effectiveness of the proposed model is verified on specific example data. The results show that the proposed deep learning RMB exchange rate prediction model has better performance than traditional exchange rate prediction models in predicting exchange rates in different international foreign exchange markets, with accuracy of 74.92%. ARMA-GARCH risk prediction model has good measurement performance for the market, and its accuracy is significantly higher than that of the traditional measurement model. The deep confidence network model has stable performance and ideal forecasting effects both in the forecast of exchange rate fluctuations and in risk measurement. In short, this research can improve China’s research on exchange rate fluctuations and effectively strengthens the ability of forecasting and risk assessment of the foreign exchange market.

Suggested Citation

  • Chunyi Lu & Zhuoqi Teng & Yu Gao & Renhong Wu & Md. Alamgir Hossain & Yuantao Fang, 2022. "Analysis of Early Warning of RMB Exchange Rate Fluctuation and Value at Risk Measurement Based on Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1501-1524, April.
  • Handle: RePEc:kap:compec:v:59:y:2022:i:4:d:10.1007_s10614-021-10172-z
    DOI: 10.1007/s10614-021-10172-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-021-10172-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10614-021-10172-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zongxin Qian & Jingyun Gan & Yonghong Tu & Fang Wang, 2019. "International policy coordination and RMB internationalisation: theory and historical experience," Economic and Political Studies, Taylor & Francis Journals, vol. 7(1), pages 87-105, January.
    2. Cindy K Soo, 2018. "Quantifying Sentiment with News Media across Local Housing Markets," The Review of Financial Studies, Society for Financial Studies, vol. 31(10), pages 3689-3719.
    3. Kramarz, Francis & Martin, Julien & Mejean, Isabelle, 2020. "Volatility in the small and in the large: The lack of diversification in international trade," Journal of International Economics, Elsevier, vol. 122(C).
    4. Han, Yingying & Gong, Pu & Zhou, Xiang, 2016. "Correlations and risk contagion between mixed assets and mixed-asset portfolio VaR measurements in a dynamic view: An application based on time varying copula models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 940-953.
    5. Al-Shboul, Mohammad & Alsharari, Nizar, 2019. "The dynamic behavior of evolving efficiency: Evidence from the UAE stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 73(C), pages 119-135.
    6. Douglas Kai Tim Wong, 2020. "The forward‐looking ability of the real exchange rate and its misalignment to forecast the economic performance and the stock market return," The World Economy, Wiley Blackwell, vol. 43(10), pages 2723-2741, October.
    7. Ajai S Gaur & Xufei Ma & Zhujun Ding, 2018. "Home country supportiveness/unfavorableness and outward foreign direct investment from China," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 49(3), pages 324-345, April.
    8. Rebecca Lewis & John McPartland & Rajeev Ranjan, 2017. "Blockchain and Financial Market Innovation," Economic Perspectives, Federal Reserve Bank of Chicago, issue 7, pages 2-12.
    9. Yang, Lu & Cai, Xiao Jing & Hamori, Shigeyuki, 2017. "Does the crude oil price influence the exchange rates of oil-importing and oil-exporting countries differently? A wavelet coherence analysis," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 536-547.
    10. Yu, Miao, 2019. "Forecasting Bitcoin volatility: The role of leverage effect and uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    11. Korobilis, D & Yilmaz, K, 2018. "Measuring Dynamic Connectedness with Large Bayesian VAR Models," Essex Finance Centre Working Papers 20937, University of Essex, Essex Business School.
    12. Giannaros, Theodore M. & Melas, Dimitrios & Ziomas, Ioannis, 2017. "Performance evaluation of the Weather Research and Forecasting (WRF) model for assessing wind resource in Greece," Renewable Energy, Elsevier, vol. 102(PA), pages 190-198.
    13. Waverly Duck, 2017. "The Complex Dynamics of Trust and Legitimacy: Understanding Interactions between the Police and Poor Black Neighborhood Residents," The ANNALS of the American Academy of Political and Social Science, , vol. 673(1), pages 132-149, September.
    14. 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).
    15. Chen, Liming & Du, Ziqing & Hu, Zhihao, 2020. "Impact of economic policy uncertainty on exchange rate volatility of China," Finance Research Letters, Elsevier, vol. 32(C).
    16. Ji, Qiang & Bouri, Elie & Roubaud, David & Shahzad, Syed Jawad Hussain, 2018. "Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model," Energy Economics, Elsevier, vol. 75(C), pages 14-27.
    17. S. M. Abdullah & Salina Siddiqua & Muhammad Shahadat Hossain Siddiquee & Nazmul Hossain, 2017. "Modeling and forecasting exchange rate volatility in Bangladesh using GARCH models: a comparison based on normal and Student’s t-error distribution," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-19, December.
    18. Mitra, Sovan, 2017. "Efficient option risk measurement with reduced model risk," Insurance: Mathematics and Economics, Elsevier, vol. 72(C), pages 163-174.
    19. Charles Kwofie & Richard Kwame Ansah, 2018. "A Study of the Effect of Inflation and Exchange Rate on Stock Market Returns in Ghana," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2018, pages 1-8, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mehmet Sahiner, 2024. "Volatility Spillovers and Contagion During Major Crises: An Early Warning Approach Based on a Deep Learning Model," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2435-2499, June.
    2. Tang, Pan & Xu, Wei & Wang, Haosen, 2024. "Network-Based prediction of financial cross-sector risk spillover in China: A deep learning approach," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Abir Abid & Christophe Rault, 2021. "On the Exchange Rates Volatility and Economic Policy Uncertainty Nexus: A Panel VAR Approach for Emerging Markets," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(3), pages 403-425, September.
    2. Chaturvedi, Priya & Kumar, Kuldeep, 2022. "Econometric modelling of exchange rate volatility using mixed-frequency data," MPRA Paper 115222, University Library of Munich, Germany.
    3. Xinyu Yuan & Jiechen Tang & Wing-Keung Wong & Songsak Sriboonchitta, 2020. "Modeling Co-Movement among Different Agricultural Commodity Markets: A Copula-GARCH Approach," Sustainability, MDPI, vol. 12(1), pages 1-17, January.
    4. Hedi Ben Haddad & Imed Mezghani & Abdessalem Gouider, 2021. "The Dynamic Spillover Effects of Macroeconomic and Financial Uncertainty on Commodity Markets Uncertainties," Economies, MDPI, vol. 9(2), pages 1-22, June.
    5. Wu, Xinyu & Xie, Haibin & Zhang, Huanming, 2022. "Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    6. Yue Liu & Pierre Failler & Jiaying Peng & Yuhang Zheng, 2020. "Time-Varying Relationship between Crude Oil Price and Exchange Rate in the Context of Structural Breaks," Energies, MDPI, vol. 13(9), pages 1-17, May.
    7. Lin Liu, 2022. "Economic Uncertainty and Exchange Market Pressure: Evidence From China," SAGE Open, , vol. 12(1), pages 21582440211, January.
    8. Fasanya, Ismail O. & Adekoya, Oluwasegun B. & Adetokunbo, Abiodun M., 2021. "On the connection between oil and global foreign exchange markets: The role of economic policy uncertainty," Resources Policy, Elsevier, vol. 72(C).
    9. 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).
    10. Bing‐Yue Liu & Qiang Ji & Duc Khuong Nguyen & Ying Fan, 2021. "Dynamic dependence and extreme risk comovement: The case of oil prices and exchange rates," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2612-2636, April.
    11. Al-Shboul, Mohammad & Assaf, Ata & Mokni, Khaled, 2023. "Does economic policy uncertainty drive the dynamic spillover among traditional currencies and cryptocurrencies? The role of the COVID-19 pandemic," Research in International Business and Finance, Elsevier, vol. 64(C).
    12. Kim Karlsson, Hyunjoo & Li, Yushu, 2024. "Investigation of Swedish krona exchange rate volatility by APARCH-Support Vector Regression," Working Papers in Economics and Statistics 10/2024, Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    13. Luo, Changqing & Liu, Lan & Wang, Da, 2021. "Multiscale financial risk contagion between international stock markets: Evidence from EMD-Copula-CoVaR analysis," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    14. Bush, Georgia & López Noria, Gabriela, 2021. "Uncertainty and exchange rate volatility: Evidence from Mexico," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 704-722.
    15. Lee A. Smales, 2022. "The influence of policy uncertainty on exchange rate forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 997-1016, August.
    16. Abuzayed, Bana & Al-Fayoumi, Nedal, 2021. "Risk spillover from crude oil prices to GCC stock market returns: New evidence during the COVID-19 outbreak," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    17. Seiler, Volker, 2024. "The relationship between Chinese and FOB prices of rare earth elements – Evidence in the time and frequency domain," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 160-179.
    18. Jose Carreno, 2020. "Housing Booms and the U.S. Productivity Puzzle," Working Papers 20-4, Center for Economic Studies, U.S. Census Bureau.
    19. Theresa Kuchler & Monika Piazzesi & Johannes Stroebel, 2022. "Housing Market Expectations," CESifo Working Paper Series 9665, CESifo.
    20. Wu, Kai & Zhu, Jingran & Xu, Mingli & Yang, Lu, 2020. "Can crude oil drive the co-movement in the international stock market? Evidence from partial wavelet coherence analysis," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:59:y:2022:i:4:d:10.1007_s10614-021-10172-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.