Abstract
In this paper, we analyze the inherent evolutionary dynamics of financial and energy markets. We study their inter-relationships and perform predictive analysis using an integrated nonparametric framework. We consider the daily closing prices of BSE Energy Index, Crude Oil, DJIA Index, Natural Gas, and NIFTY Index representing natural resources, developing and developed economies from January 2012 to March 2017 for this purpose. DJIA and NIFTY account for the global financial market while the other three-time series represent the energy market. First, we investigate the empirical characteristics of the underlying temporal dynamics of the financial time series through the technique of nonlinear dynamics to extract the key insights. Results suggest the existence of a strong trend component and long-range dependence as the underlying pattern. Then we apply the continuous wavelet transformation based multiscale exploration to investigate the co-movements of considered assets. We discover the long and medium-range co-movements among the heterogeneous assets. The findings of dynamic time-varying association reveal interesting insights that may assist portfolio managers in mitigating risk. Finally, we deploy a wavelet-based time-varying dynamic approach for estimating the conditional correlation among the said assets to determine the hedge ratios for practical implications.
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Abounoori, E., Elmi, Z., & Nademi, Y. (2016). Forecasting Tehran stock exchange volatility; Markov switching GARCH approach. Physica A: Statistical Mechanics and its Applications, 445, 264–282.
Basher, S. A., & Sadorsky, P. (2016). Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH. Energy Economics, 54, 235–247.
Cipollini, A., Cascio, I. L., & Muzzioli, S. (2015). Volatility co-movements: A time-scale decomposition analysis. Journal of Empirical Finance, 34, 34–44.
Cornish, C. R., Bretherton, C. S., & Percival, D. B. (2006). Maximal overlap wavelet statistical analysis with application to atmospheric turbulence. Boundary-Layer Meteorology, 119, 339–374.
Creti, A., Ftiti, Z., & Guesmi, K. (2014). Oil price and financial markets: Multivariate dynamic frequency analysis. Energy Policy, 73, 245–258.
Das, D., Bhowmik, P., & Jana, R. K. (2018). A multiscale analysis of stock return co-movements and spillovers: Recent evidence from Pacific developed markets. Physica A: Statistical Mechanics and its Applications, 502, 379–393.
Das, D., Roux, C. L. L., Jana, R. K., & Dutta, A. (2019). Does Bitcoin hedge crude oil implied volatility and structural shocks? A comparison with gold, commodity and the US Dollar. Finance Research Letters. https://doi.org/10.1016/j.frl.2019.101335.
Engle, R. (2002). Dynamic conditional correlation. Journal of Business and Economic Statistics, 20, 339–350.
Engle, R., & Sheppard, K. (2001). Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH (No. 8554). MA.
Fink, J. D., & Fink, K. E. (2013). Hurricane forecast revisions and petroleum refiner equity returns. Energy Economics, 38, 1–11.
Genc, T. S. (2017). OPEC and demand response to crude oil prices. Energy Economics, 66, 238–246.
Gencay, R., Selcuk, F., & Whitcher, B. (2002). An introduction to wavelets and other filtering methods in finance and economics. Academic Press.
Ghosh, I., Jana, R. K., & Sanyal, M. K. (2019). Analysis of temporal pattern, causal interaction and predictive modeling of financial markets using nonlinear dynamics, econometric models and machine learning algorithms. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2019.105553.
Ghosh, S., & Kanjilal, K. (2016). Co-movement of international crude oil price and Indian stock market: Evidences from nonlinear cointegration tests. Energy Economics, 53, 111–117.
Ghosh, I., Sanyal, M. K., & Jana, R. K. (2017). Fractal inspection and machine learning-based predictive modelling framework for financial markets. Arabian Journal for Science and Engineering, 43, 4273–4287.
Han, L., & Ge, R. (2017). Wavelets analysis on structural model for default prediction. Computational Economics, 50(1), 111–140.
Henriques, I., & Sadorsky, P. (2008). Oil prices and the stock prices of alternative energy companies. Energy Economics, 30, 998–1010.
Hurst, H. E. (1951). Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116, 770–808.
Jammazi, R., Ferrer, R., Jareno, F., & Shahzad, J. (2017). Time-varying causality between crude oil and stock markets: What can we learn from a multiscale perspective? International Review of Economics & Finance, 49, 453–483.
Jones, C. M., & Kaul, G. (1996). Oil and the stock markets. The Journal of Finance, 51, 463–491.
Jones, P. M., & Olson, E. (2013). The time-varying correlation between uncertainty, output, and inflation: Evidence from a DCC-GARCH model. Economics Letters, 118, 33–37.
Kao, L. J., Chiu, C. C., Lu, C. J., & Chang, C. H. (2013). A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting. Decision Support Systems, 54, 1228–1244.
Karatas, C., Unal, G., & Yilmaz, A. (2017). Co-movement and forecasting analysis of major real estate markets by wavelet coherence and multiple wavelet coherence. Chinese Journal of Urban and Environmental Studies, 5(2), 1750010. https://doi.org/10.1142/S2345748117500105.
Khalfaoui, R., Boutahar, M., & Boubaker, H. (2015). Analyzing volatility spillovers and hedging between oil and stock markets: Evidence from wavelet analysis. Energy Economics, 49, 540–549.
Kim, J. M., Jung, H., & Qin, L. (2016). Linear time-varying regression with Copula–DCC–GARCH models for volatility. Applied Economics Letters, 48, 1573–1582.
Kroner, K. F., & Sultan, J. (1993). Time-varying distributions and dynamic hedging with foreign currency futures. The Journal of Financial and Quantitative Analysis, 28, 535–551.
Liu, X., An, H., Huang, S., & Wen, S. (2017). The evolution of spillover effects between oil and stock markets across multi-scales using wavelet based GARCH-BEKK model. Physica A: Statistical Mechanics and its Applications, 465, 374–383.
Liu, L., Ma, F., & Wang, Y. (2015). Forecasting excess stock returns with crude oil market data. Energy Economics, 48, 316–324.
Mandelbrot, B., & Wallis, J. (1968). Noah, Joseph and operational hydrology. Water Resources Research, 4, 909–918.
Mantegna, R. N., & Stanley, H. E. (1999). An introduction to econophysics: Correlation and complexity in finance. Cambridge: Cambridge University Press.
Mensi, W., Tiwari, A., Bouri, E., Roubaud, D., & Al-Yahyaee, K. (2017). The dependence structure across oil, wheat, and corn: A wavelet-based copula approach using implied volatility indices. Energy Economics, 66, 122–139.
Pan, Z., Wang, Y., & Yang, L. (2014). Hedging crude oil using refined product: a regime switching asymmetric DCC approach. Energy Economics, 46, 472–484.
Panda, P., & Deo, M. (2014). Asymmetric and volatility spillover between stock market and foreign exchange market: Indian Experience. IUP Journal of Applied Finance, 20, 69–82.
Phillips, R. C., & Gorse, D. (2018). Cryptocurrency price drivers: Wavelet coherence analysis revisited. PLoS ONE, 13, e0195200. https://doi.org/10.1371/journal.pone.0195200.
Priyadarshini, E., & Babu, A. C. (2012). Fractal analysis of Indian financial markets: An empirical approach. Asia-Pacific Journal of Management Research and Innovation, 8, 271–281.
Reboredo, J. C., Rivera-Castro, M. A., & Ugolini, A. (2017). Wavelet-based test of co-movement and causality between oil and renewable energy stock prices. Energy Economics, 61, 241–252.
Sharif, A., Jammazi, R., Ali Raza, S., & Shahzad, J. (2017). Electricity and growth nexus dynamics in Singapore: Fresh insights based on wavelet approach. Energy Policy, 110, 686–692.
Singh, R., Das, D., Jana, R. K., & Tiwari, A. K. (2018). A wavelet analysis for exploring the relationship between economic policy uncertainty and tourist footfalls in the USA. Current Issues in Tourism, 22(15), 1789–1796.
Tiwari, A. K., Jana, R. K., & Roubaud, D. (2019). The policy uncertainty and market volatility puzzle: Evidence from wavelet analysis. Finance Research Letters, 31, 278–284.
Torrence, C., & Webster, P. (1999). Interdecadal changes in the ESNO-Monsoon system. Journal of Climate, 12, 2679–2690.
Wang, J., & Ma, J. (2011). Gold markets price analysis and application studies based on complexity theories. Complex Systems and Complexity Science, 5, 54–59.
Wang, J. Z., Wang, J. J., Zhang, Z. G., & Guo, S. P. (2011). Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38, 14346–14355.
Yin, K., Zhang, H., Zhang, W., & Wei, Q. (2013). Fractal analysis of gold market in China. Romanian Journal of Economic Forecasting, 16, 144–163.
Zhang, J. L., Zhang, Y. J., & Zhang, L. (2015). A novel hybrid method for crude oil price forecasting. Energy Economics, 49, 649–659.
Zhao, Y., Li, J., & Yu, L. (2017). A deep learning approach for crude oil price forecasting. Energy Economics, 66, 9–16.
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Ghosh, I., Sanyal, M.K. & Jana, R.K. Co-movement and Dynamic Correlation of Financial and Energy Markets: An Integrated Framework of Nonlinear Dynamics, Wavelet Analysis and DCC-GARCH. Comput Econ 57, 503–527 (2021). https://doi.org/10.1007/s10614-019-09965-0
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DOI: https://doi.org/10.1007/s10614-019-09965-0
Keywords
- Financial market
- Nonlinear dynamics
- Continuous wavelet transform
- Discrete wavelet transform
- Conditional correlation