Weak-Form Efficiency of the USD/CNY Exchange Rate Before, During, and After COVID-19

Authors

Kok Sook Ching

Centre for Economic Development and Policy, Universiti Malaysia Sabah\Faculty of Business, Economics and Accountancy, Universiti Malaysia Sabah (Malaysia)

Saizal Pinjaman

Centre for Economic Development and Policy, Universiti Malaysia Sabah\Faculty of Business, Economics and Accountancy, Universiti Malaysia Sabah (Malaysia)

Kasim Mansur

Centre for Economic Development and Policy, Universiti Malaysia Sabah\Faculty of Business, Economics and Accountancy, Universiti Malaysia Sabah (Malaysia)

Mohd. Rahimie Abd. Karim

Faculty of Business, Economics and Accountancy, Universiti Malaysia Sabah (Malaysia)

Suryaningsih

Faculty of Economics, Universitas Borneo Tarakan (Indonesia)

Article Information

DOI: 10.47772/IJRISS.2026.1015EC0072

Subject Category: Education

Volume/Issue: 10/15 | Page No: 1005-1017

Publication Timeline

Submitted: 2026-07-08

Accepted: 2026-07-13

Published: 2026-07-27

Abstract

This study examines the weak-form efficiency of the nominal exchange rate between the Chinese yuan and the United States dollar across the periods before, during, and after the COVID-19 pandemic. Daily observations of the USD/CNY exchange rate from 2 January 2015 to 31 December 2025 were obtained from the Federal Reserve Economic Data database. Exchange rate efficiency was assessed using the runs test, the heteroskedasticity robust variance ratio test, and the Ljung Box Q test. The runs test failed to reject randomness for the full sample and all three subperiods. The variance ratio test also supported the random walk condition at most holding periods, although significant positive serial dependence was detected at the 20-day horizon for the full sample and the pre-COVID-19 period. The Ljung Box Q test showed no significant serial correlation before COVID 19, limited dependence during the pandemic, and significant serial correlation at all tested lags after COVID 19. These findings indicate that the USD/CNY exchange rate was predominantly consistent with weak-form efficiency before and during the pandemic, while the post-COVID-19 period produced mixed evidence. Historical exchange rate movements, therefore, contained limited and period-dependent predictive information rather than providing a stable basis for forecasting future movements. The findings highlight the importance of assessing foreign exchange market efficiency across different economic conditions and statistical horizons.

Keywords

Chinese yuan, United States dollar, exchange rate, weak-form efficiency, random walk, COVID-19

Downloads

References

1. Ajami, R.A. (2019). US Trading Partners: Uncertainty and Challenges. Journal of Asia-Pacific Business, 20(2), 79-81. [Google Scholar] [Crossref]

2. Bekkers, E. and Schroeter, S. (2020). An Economic Analysis of US-China Trade Conflict. Staff Working Paper Economic Research and Statistics Division, 2020-04. [Google Scholar] [Crossref]

3. Board of Governors of the Federal Reserve System. (2026). Chinese yuan renminbi to U.S. dollar spot exchange rate [DEXCHUS]. FRED, Federal Reserve Bank of St. Louis. Retrieved July 11, 2026. [Google Scholar] [Crossref]

4. Cai, Z., Chen, L. and Fang, Y. (2012). A New Forecasting Model for USD/CNY Exchange Rate. Studies in Nonlinear Dynamics & Econometrics, 16(3), 1-18. [Google Scholar] [Crossref]

5. Czech, K.A. and Waszkowski, A. (2012). Foreign Exchange Market Efficiency: Empirical Results for the USD/EUR Market. E-Finance: Financial Internet Quarterly, 8(3), 1-9. [Google Scholar] [Crossref]

6. Derbali, A.M.S. (2025). USD/CNY Exchange Rate Correlation: What Dynamic Mechanism During Unexpected Shocks? Economics Open, 1, 1-13. [Google Scholar] [Crossref]

7. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417. [Google Scholar] [Crossref]

8. Fama, E. F. (1991). Efficient capital markets: II. The Journal of Finance, 46(5), 1575–1617. [Google Scholar] [Crossref]

9. Kok, S. C., Pinjaman, S., and Geetha, C. (2025). The predictability of the Ringgit Malaysia against the United States dollar exchange rate. International Journal of Research and Innovation in Social Science, 9(15), 1018–1026. https://doi.org/10.47772/IJRISS.2025.915EC00727 [Google Scholar] [Crossref]

10. Kuncoro, H., and Pinjaman, S. (2026). Market intervention in the inflation targeting regime: The case of Indonesia. Quantitative Finance and Economics, 10(1), 41–62. https://doi.org/10.3934/QFE.2026003 [Google Scholar] [Crossref]

11. Lee, Z. H., Wong, H. T., Pinjaman, S., and Mansur, K. (2022). Co movement of COVID 19, the S&P 500 and stock markets in ASEAN: A wavelet coherence analysis. International Journal of Economics and Management, 16(1), 119–134. [Google Scholar] [Crossref]

12. Ljung, G. M., & Box, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297–303. [Google Scholar] [Crossref]

13. Lo, A. W., & MacKinlay, A. C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. The Review of Financial Studies, 1(1), 41–66. [Google Scholar] [Crossref]

14. Molnar, A. and Viktor, P. (2023). China’s Impact on the US Dollar Exchange Rate. The Eurasia Proceedings of Educational & Social Sciences, 32, 71-79. [Google Scholar] [Crossref]

15. Pinjaman, S., Kok, S. C., and Janin, Y. (2025). Market efficiency of the Malaysian ringgit against selected ASEAN currency exchange rates for pre, during, and post COVID 19 pandemic. International Journal of Research and Innovation in Social Science, 9(15), 1451–1460. [Google Scholar] [Crossref]

16. Pinjaman, S., Kok, S. C., Aralas, S., Basuki, A. T., Hadi, S., Nugraha, P., and Darsono, S. N. A. C. (2026). Pandemic shocks and time varying weak form efficiency in ASEAN foreign exchange markets: An empirical investigation. Optimum: Jurnal Ekonomi dan Pembangunan, 16(1), 138–147. https://doi.org/10.12928/optimum.v16i1.15560 [Google Scholar] [Crossref]

17. Rapp, T.A. and Sharma, S.C. (1999). Exchange rate market efficiency: across and within countries. Journal of Economics and Business, 51(5), 423-439. [Google Scholar] [Crossref]

18. Samambgwa, H. and Musora, T. (2025). Modelling and Forecasting the United States Dollar-Chinese Yuan Exchange Rate Nonlinear Autoregressive Neural Network vs Seasonal Autoregressive Integrated Moving Average. World Journal of Advanced Engineering Technology and Sciences, 17(3), 481-490. [Google Scholar] [Crossref]

19. Tsuji, C. (2025). Forecasting Exchange Rates with Artificial Intelligence: A Study of European Currencies. International Business Research, 18(5), 24-34. [Google Scholar] [Crossref]

20. Wald, A., & Wolfowitz, J. (1940). On a test whether two samples are from the same population. The Annals of Mathematical Statistics, 11(2), 147–162. [Google Scholar] [Crossref]

21. Wong, V. K., Aralas, S., and Pinjaman, S. (2023). Islamic stock price and exchange rate: A wavelet analysis for ASEAN 5. Global Business and Management Research: An International Journal, 15(1), 142–148. [Google Scholar] [Crossref]

22. Yang, T., Lau, W-Y. and Abdul Bahri, E.N. (2025). The Impact of US-China Trade War on China’s Exports: Evidence From Difference-in-Differences Model, SAGE Open, 1-15. [Google Scholar] [Crossref]

23. Zhang, X. (2024). The Time Series Forecasting for CNY-USD Exchange Rate. Highlights in Science, Engineering and Technology, 88, 948-953. [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles