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

Zhaoyi Xu (), Yuqing Zeng (), Yangrong Xue () and Shenggang Yang ()
Additional contact information
Zhaoyi Xu: Hunan University
Yuqing Zeng: Jinan University
Yangrong Xue: Yangtze Delta Region Research Institute of Tsinghua University
Shenggang Yang: Hunan University

Computational Economics, 2022, vol. 60, issue 4, No 5, 1293-1315

Abstract: 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.

Keywords: Deep learning; Exchange rate fluctuation; Neural network; VaR measure; Risk (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-021-10144-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:60:y:2022:i:4:d:10.1007_s10614-021-10144-3

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-021-10144-3

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().