References
- An, P., Li, H., Zhou, J., Li, Y., Sun, B., Guo, S. and Qi, Y., Volatility spillover of energy stocks in different periods and clusters based on structural break recognition and network method. Energy, 2020, 191, 116585. doi: https://doi.org/10.1016/j.energy.2019.116585
- Bank for International Settlements, Foreign exchange turnover in April 2016: Preliminary global results. Monetary and Economic Department, Bank for International Settlements, 2016.
- Bierens, H.J., Testing the unit root with drift hypothesis against nonlinear trend stationarity, with an application to the US price level and interest rate. J. Econom., 1997, 81, 29–64. doi: https://doi.org/10.1016/S0304-4076(97)00033-X
- Bishop, C.M., Pattern Recognition and Machine Learning, 2006 (Springer-Verlag: New York).
- Buhler, J. and Tompa, M., Finding motifs using random projections. J. Comput. Biol., 2002, 9, 225–242. doi: https://doi.org/10.1089/10665270252935430
- Cheung, Y.W. and Rime, D., The offshore Renminbi exchange rate: Microstructure and links to the onshore market. J. Int. Money. Finance, 2014, 49, 170–189. doi: https://doi.org/10.1016/j.jimonfin.2014.05.012
- Craig, R., Hua, C., Ng, P. and Yuen, R., Chinese capital account liberalization and the internationalization of the Renminbi. IMF Working Paper WP/13/f268, 2013.
- Deng, W., Wang, G. and Xu, J., Piecewise two-dimensional normal cloud representation for time-series data mining. Inf. Sci. (Ny), 2016, 374, 32–50. doi: https://doi.org/10.1016/j.ins.2016.09.027
- Du, J. and Lai, K.K., Copula-based risk management models for multivariable RMB exchange rate in the process of RMB internationalization. J. Syst. Sci. Complex., 2017, 30, 660–679. doi: https://doi.org/10.1007/s11424-017-5147-3
- Funke, M., Shu, C., Cheng, X. and Eraslan, S., Assessing the CNH–CNY pricing differential: Role of fundamentals, contagion and policy. J. Int. Money. Finance, 2015, 59, 245–262. doi: https://doi.org/10.1016/j.jimonfin.2015.07.008
- Hallac, D., Vare, S., Boyd, S. and Leskovec, J., Toeplitz inverse covariance-based clustering of multivariate time series data. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 215–223, 2017 (Association for Computing Machinery: Halifax).
- Johansen, S., Statistical analysis of cointegration vectors. J. Econ. Dyn. Control, 1988, 12, 231–254. doi: https://doi.org/10.1016/0165-1889(88)90041-3
- Lacasa, L., Luque, B., Ballesteros, F., Luque, J. and Nuno, J.C., From time series to complex networks: The visibility graph. P. Natl. Acad. Sci., 2008, 105, 4972–4975. doi: https://doi.org/10.1073/pnas.0709247105
- Leung, D. and Fu, J., Interactions between CNY and CNH money and forward exchange markets. Hong Kong Institute for Monetary Research Working Paper No. 13/2014, 2014.
- Liang, Y., Shi, K., Wang, L. and Xu, J., Fluctuation and reform: A tale of two RMB markets. China Econ. Rev., 2019, 53, 30–52. doi: https://doi.org/10.1016/j.chieco.2018.08.003
- Liu, R. and Chen, Y., Analysis of stock price motion asymmetry via visibility-graph algorithm. Front. Phys., 2020, 8, 539521.
- Malevergne, Y. and Sornette, D., Extreme Financial Risks: From Dependence to Risk Management, 2006 (Springer-Verlag: Berlin Heidelberg).
- Molchan, G.M., Earthquake prediction as a decision-making problem. Pure Appl. Geophys., 1997, 149, 233–247. doi: https://doi.org/10.1007/BF00945169
- Owyong, D., Wong, W. and Horowitz, I., Cointegration and causality among the onshore and offshore markets for China's currency. J. Asian. Econ., 2015, 41, 20–38. doi: https://doi.org/10.1016/j.asieco.2015.10.004
- Procacci, P.F. and Aste, T., Forecasting market states. Quant. Finance, 2019, 19, 1491–1498. doi: https://doi.org/10.1080/14697688.2019.1622313
- Qin, J., Relationship between onshore and offshore Renminbi exchange markets: Evidence from multiscale cross-correlation and nonlinear causal effect analyses. Physica A, 2019, 527, 121183. doi: https://doi.org/10.1016/j.physa.2019.121183
- Shu, C., He, D. and Cheng, X., One currency, two markets: The Renminbi's growing influence in Asia-Pacific. China Econ. Rev., 2015, 33, 163–178. doi: https://doi.org/10.1016/j.chieco.2015.01.013
- Sornette, D., Why Stock Markets Crash: Critical Events in Complex Financial Systems, 2017 (Princeton University Press: Princeton).
- SWIFT, RMB Tracker-April 2013. SWIFT, 2013.
- SWIFT, RMB Tracker-May 2019. SWIFT, 2019.
- Xu, H., Zhou, W. and Sornette, D., Time-dependent lead-lag relationship between the onshore and offshore Renminbi exchange rates. J. Int. Financ. Mark. Inst. Money, 2017, 49, 173–183. doi: https://doi.org/10.1016/j.intfin.2017.05.001
- Yan, D. and Lai, K.K., An analysis of China's onshore and offshore exchange rates–adjusted thermal optimal path approach based on pruning and path segmentation. Entropy, 2019, 21, 499. doi: https://doi.org/10.3390/e21050499
- Yan, W. and van Serooskerken, E.V.T., Forecasting financial extremes: A network degree measure of super-exponential growth. PLoS ONE, 2015, 10, e0128908. doi: https://doi.org/10.1371/journal.pone.0128908
- Yang, Y., Wang, J., Yang, H. and Mang, J., Visibility graph approach to exchange rate series. Physica A, 2009, 388, 4431–4437. doi: https://doi.org/10.1016/j.physa.2009.07.016
- Zhou, L., Du, G., Tao, D., Chen, H., Cheng, J. and Gong, L., Clustering multivariate time series data via multi-nonnegative matrix factorization in multi-relational networks. IEEE Access., 2018, 6, 74747–74761. doi: https://doi.org/10.1109/ACCESS.2018.2882798