Predictability of Financial Crisis via Pair Coupling of Commodity Market and Stock Market
Journal of Finance and Accounting
Volume 7, Issue 1, January 2019, Pages: 9-16
Received: Dec. 20, 2018; Accepted: Jan. 14, 2019; Published: Jan. 31, 2019
Views 740      Downloads 264
Wei Cao, School of Economics, Hefei University of Technology, Hefei, P. R. China
Tingting He, School of Economics, Hefei University of Technology, Hefei, P. R. China
Article Tools
Follow on us
The complex interactions between stock market and commodity market in financial crisis has been investigated by many researchers, but there is less known about how useful the pair coupling of the two markets for predicting financial crisis, where the pair coupling is the hidden essence of market interactions. This article investigates three kinds of couplings, namely time coupling, frequency coupling and space coupling, which are the different aspects of the pair coupling. In addition, a two-layer model, namely CHMM-ANN, is proposed to investigate the couplings and evaluate the predicting abilities based on the couplings. Coupled Hidden Markov Model (CHMM) is adopted at the bottom level to capture the hidden couplings, and then the couplings are put as input to classical Artificial Neural Network (ANN) at the top level to predict financial crisis. The experiment results on real financial data confirm the advantages of the pair coupling in predicting financial crisis.
Financial Crisis Predictability, Pair Coupling, Stock Market, Commodity Market
To cite this article
Wei Cao, Tingting He, Predictability of Financial Crisis via Pair Coupling of Commodity Market and Stock Market, Journal of Finance and Accounting. Vol. 7, No. 1, 2019, pp. 9-16. doi: 10.11648/j.jfa.20190701.12
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Aloui, R. and Ben Assa, M. S. (2016), ‘Relationship between oil, stock prices and exchange rates: a vine copula based garch method’, The North American Journal of Economics and Finance.
Liu, X., An, H., Huang, S. and Wen, S. (2017), ‘The evolution of spillover effects between oil and stock markets across multi-scales using a wavelet-based garch–bekk model’, Physica A Statistical Mechanics & Its Applications, 465, 374-383.
Ewing, B. T. and Malik, F. (2016), ‘Volatility spillovers between oil prices and the stock market under structural breaks’, Global Finance Journal, 29, 12-23.
Oliver, N. M., Rosario, B. and Pentland, A. P. (2000), ‘A bayesian computer vision system for modeling human interactions’, IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 831-843.
Kaminsky, G. L. and Reinhart, C. M. (1999), ‘The twin crises: the causes of banking and balance-of-payments problems’, International Finance Discussion Papers 89(3), 473-500.
Berg, A. and Pattillo, C. (1999), ‘Predicting currency crises: The indicators approach and an alternative’, Journal of International Money and Finance 18(4), 561–586.
Alvarez-Plata, P. and Schrooten, M. (2004), ‘Misleading indicators? the argentinean currency crisis’, Journal of Policy Modeling 26(5), 587–603.
Goldstein, M., Kaminsky, G. L. and Reinhart, C. M. (2000), ‘Assessing financial vulnerability: an early warning system for emerging markets’, Peterson Institute.
Peng, D. and Bajona, C. (2008), ‘China’s vulnerability to currency crisis: a klr signals approach’, China Economic Review 19(2), 0-151.
Yu, L., Wang, S., Lai, K. K. and Wen, F. (2010), ‘A multiscale neural network learning paradigm for financial crisis forecasting’, Neurocomputing 73(4-6), 716-725.
Sunderlin, W. D., Angelsen, A., Resosudarmo, D. P., Dermawan, A. and Rianto, E. (2001), ‘Economic crisis, small farmer well-being, and forest cover change in indonesia’, World Development 29(5), 767-782.
Kumar, M., Moorthy, U. and Perraudin, W. (2003), ‘Predicting emerging market currency crashes’, Journal of Empirical Finance 10(4), 427-454.
Beckmann, D., Menkhoff, L. and Sawischlewski, K. (2006), ‘Robust lessons about practical early warning systems’, Journal of Policy Modeling 28(2), 163-193.
Kalotychou, E. and Staikouras, S. K. (2006), ‘An empirical investigation of the loan concentration risk in latin america’, Journal of Multinational Financial Management 16(4), 363-384.
Bussiere, M. and Fratzscher, M. (2006), ‘Towards a new early warning system of financial crises’, Journal of International Money & Finance 25(6), 0-973.
Eichengreen, B., Rose, A. K., Wyplosz, C., Eichengreen, B., Rose, A. K. and Wyplosz, C. (1995), ‘Exchange market mayhem: the antecedents and aftermath of speculative attacks’, Economic Policy 10(21), 249-312.
Kim, T. Y., Hwang, C. and Lee, J. (2004), ‘Korean economic condition indicator using a neural network trained on the 1997 crisis’, Journal of Data Science 2(4), 371–381.
Shin, K., Lee, T. S. and Kim, H. (2006), ‘An application of support vector machines in bankruptcy prediction model’, Journal of Financial Research 28(1), 127-135.
Lin, C. S., Khan, H. A., Chang, R. Y. and Wang, Y. C. (2008), ‘A new approach to modeling early warning systems for currency crises: can a machine-learning fuzzy expert system predict the currency crises effectively?’, Journal of International Money and Finance 27(7), 0-1121.
Fioramanti M. Predicting sovereign debt crises using artificial neural networks: A comparative approach[J]. Journal of Financial Stability, 2008, 4(2):0-164.
Sonnhammer, E. L. L., Heijne, G. V. and Krogh, A. (1998), ‘A hidden Markov model for predicting transmembrane helices in protein sequences’, PubMed. 6: 175-82.
Zhong, Shi, and Joydeep Ghosh. (2001). ‘A new formulation of coupled hidden Markov models’, Technical report. Tech. Report, Dept. of Electronic and Computer Engineering, U. of Texas at Austin, USA.
Hassoun, M H. (1996), ‘Fundamentals of Artificial Neural Networks’, Proceedings of the IEEE 84 (6): 906.
Chen, Wei Sen, and Yin Kuan Du. (2009), ‘Using neural networks and data mining techniques for the financial distress prediction model’, Expert Systems with Applications 36 (2): 4075-4086.
Bishop, Christopher M. (2006), ‘Pattern Recognition and Machine Learning (Information Science and Statistics)’, Springer-Verlag New York, Inc.
Baur, Dirk G. (2012), ‘Financial contagion and the real economy’, Journal of Banking & Finance 36 (10): 2680-2692.
Lessmann, S., Baesens, B., Seow, H., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124-136.
Zhou, L., Lu, D. and Fujita, H. (2015), ‘The performance of corporate financial distress prediction models with features selection guided by domain knowledge and data mining approaches’, Knowledge-Based Systems, 85, 52-61.
Bastianin, A., Conti, F. and Manera, M. (2016), ‘The impacts of oil price shocks on stock market volatility Evidence from the G7 countries’, Energy Policy 98, 160-169.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186