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Co-movement and Dynamic Correlation of Financial and Energy Markets: An Integrated Framework of Nonlinear Dynamics, Wavelet Analysis and DCC-GARCH

Author

Listed:
  • Indranil Ghosh

    (Calcutta Business School
    University of Kalyani)

  • Manas K. Sanyal

    (University of Kalyani)

  • R. K. Jana

    (Indian Institute of Management Raipur)

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.

Suggested Citation

  • Indranil Ghosh & Manas K. Sanyal & R. K. Jana, 2021. "Co-movement and Dynamic Correlation of Financial and Energy Markets: An Integrated Framework of Nonlinear Dynamics, Wavelet Analysis and DCC-GARCH," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 503-527, February.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:2:d:10.1007_s10614-019-09965-0
    DOI: 10.1007/s10614-019-09965-0
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    as
    1. repec:dau:papers:123456789/13716 is not listed on IDEAS
    2. Ross C Phillips & Denise Gorse, 2018. "Cryptocurrency price drivers: Wavelet coherence analysis revisited," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-21, April.
    3. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871, October.
    4. Pradiptarathi Panda & Malabika Deo, 2014. "Asymmetric and Volatility Spillover Between Stock Market and Foreign Exchange Market: Indian Experience," The IUP Journal of Applied Finance, IUP Publications, vol. 20(4), pages 69-82, October.
    5. Liu, Xueyong & An, Haizhong & Huang, Shupei & Wen, Shaobo, 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 and its Applications, Elsevier, vol. 465(C), pages 374-383.
    6. Kim, Jong-Min & Jung, Hojin, 2016. "Linear time-varying regression with Copula–DCC–GARCH models for volatility," Economics Letters, Elsevier, vol. 145(C), pages 262-265.
    7. Cengiz KARATAS & Gazanfer UNAL & Adil YILMAZ, 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 (CJUES), World Scientific Publishing Co. Pte. Ltd., vol. 5(02), pages 1-18, June.
    8. Henriques, Irene & Sadorsky, Perry, 2008. "Oil prices and the stock prices of alternative energy companies," Energy Economics, Elsevier, vol. 30(3), pages 998-1010, May.
    9. Robert F. Engle & Kevin Sheppard, 2001. "Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH," NBER Working Papers 8554, National Bureau of Economic Research, Inc.
    10. Creti, Anna & Ftiti, Zied & Guesmi, Khaled, 2014. "Oil price and financial markets: Multivariate dynamic frequency analysis," Energy Policy, Elsevier, vol. 73(C), pages 245-258.
    11. Basher, Syed Abul & Sadorsky, Perry, 2016. "Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH," Energy Economics, Elsevier, vol. 54(C), pages 235-247.
    12. Tiwari, Aviral Kumar & Jana, R.K. & Roubaud, David, 2019. "The policy uncertainty and market volatility puzzle: Evidence from wavelet analysis," Finance Research Letters, Elsevier, vol. 31(C).
    13. Khalfaoui, R. & Boutahar, M. & Boubaker, H., 2015. "Analyzing volatility spillovers and hedging between oil and stock markets: Evidence from wavelet analysis," Energy Economics, Elsevier, vol. 49(C), pages 540-549.
    14. Genc, Talat S., 2017. "OPEC and demand response to crude oil prices," Energy Economics, Elsevier, vol. 66(C), pages 238-246.
    15. Sharif, Arshian & Jammazi, Rania & Raza, Syed Ali & Shahzad, Syed Jawad Hussain, 2017. "Electricity and growth nexus dynamics in Singapore : Fresh insights based on wavelet approach," Energy Policy, Elsevier, vol. 110(C), pages 686-692.
    16. Jones, Paul M. & Olson, Eric, 2013. "The time-varying correlation between uncertainty, output, and inflation: Evidence from a DCC-GARCH model," Economics Letters, Elsevier, vol. 118(1), pages 33-37.
    17. Reboredo, Juan C. & Rivera-Castro, Miguel A. & Ugolini, Andrea, 2017. "Wavelet-based test of co-movement and causality between oil and renewable energy stock prices," Energy Economics, Elsevier, vol. 61(C), pages 241-252.
    18. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
    19. Fink, Jason D. & Fink, Kristin E., 2013. "Hurricane forecast revisions and petroleum refiner equity returns," Energy Economics, Elsevier, vol. 38(C), pages 1-11.
    20. Kedong YIN & Hengda ZHANG & Wenbo ZHANG & Qian WEI, 2013. "Fractal Analysis of the Gold Market in China," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 144-163, October.
    21. Kroner, Kenneth F. & Sultan, Jahangir, 1993. "Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 28(4), pages 535-551, December.
    22. Abounoori, Esmaiel & Elmi, Zahra (Mila) & Nademi, Younes, 2016. "Forecasting Tehran stock exchange volatility; Markov switching GARCH approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 264-282.
    23. Lu Han & Ruihuan Ge, 2017. "Wavelets Analysis on Structural Model for Default Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 111-140, June.
    24. Pan, Zhiyuan & Wang, Yudong & Yang, Li, 2014. "Hedging crude oil using refined product: A regime switching asymmetric DCC approach," Energy Economics, Elsevier, vol. 46(C), pages 472-484.
    25. Zhang, Jin-Liang & Zhang, Yue-Jun & Zhang, Lu, 2015. "A novel hybrid method for crude oil price forecasting," Energy Economics, Elsevier, vol. 49(C), pages 649-659.
    26. Jammazi, Rania & Ferrer, Román & Jareño, Francisco & Shahzad, Syed Jawad Hussain, 2017. "Time-varying causality between crude oil and stock markets: What can we learn from a multiscale perspective?," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 453-483.
    27. Jones, Charles M & Kaul, Gautam, 1996. "Oil and the Stock Markets," Journal of Finance, American Finance Association, vol. 51(2), pages 463-491, June.
    28. Das, Debojyoti & Bhowmik, Puja & Jana, R.K., 2018. "A multiscale analysis of stock return co-movements and spillovers: Evidence from Pacific developed markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 379-393.
    29. Cipollini, Andrea & Cascio, Iolanda Lo & Muzzioli, Silvia, 2015. "Volatility co-movements: A time-scale decomposition analysis," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 34-44.
    30. Liu, Li & Ma, Feng & Wang, Yudong, 2015. "Forecasting excess stock returns with crude oil market data," Energy Economics, Elsevier, vol. 48(C), pages 316-324.
    31. Ghosh, Sajal & Kanjilal, Kakali, 2016. "Co-movement of international crude oil price and Indian stock market: Evidences from nonlinear cointegration tests," Energy Economics, Elsevier, vol. 53(C), pages 111-117.
    32. Mensi, Walid & Tiwari, Aviral & Bouri, Elie & Roubaud, David & Al-Yahyaee, Khamis H., 2017. "The dependence structure across oil, wheat, and corn: A wavelet-based copula approach using implied volatility indexes," Energy Economics, Elsevier, vol. 66(C), pages 122-139.
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    Cited by:

    1. Ghosh, Indranil & Jana, Rabin K., 2024. "Clean energy stock price forecasting and response to macroeconomic variables: A novel framework using Facebook's Prophet, NeuralProphet and explainable AI," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    2. Esparcia, Carlos & Jareño, Francisco & Umar, Zaghum, 2022. "Revisiting the safe haven role of Gold across time and frequencies during the COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    3. Yousfi, Mohamed & Farhani, Ramzi & Bouzgarrou, Houssam, 2024. "From the pandemic to the Russia–Ukraine crisis: Dynamic behavior of connectedness between financial markets and implications for portfolio management," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 1178-1197.
    4. Radu Lupu & Adrian Cantemir Călin & Cristina Georgiana Zeldea & Iulia Lupu, 2021. "Systemic Risk Spillovers in the European Energy Sector," Energies, MDPI, vol. 14(19), pages 1-23, October.
    5. Jana, Rabin K. & Ghosh, Indranil, 2023. "Time-varying relationship between geopolitical uncertainty and agricultural investment," Finance Research Letters, Elsevier, vol. 52(C).
    6. Ngo Thai Hung & Xuan Vinh Vo, 2023. "Multi-scale Features of Interdependence Between Oil Prices and Stock Prices," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 30(3), pages 475-504, September.
    7. Awatef Ourir & Elie Bouri & Essahbi Essaadi, 2023. "Hedging the Risks of MENA Stock Markets with Gold: Evidence from the Spectral Approach," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 197-231, January.
    8. Mejri, Sami & Aloui, Chaker & Khan, Nasir, 2024. "The gold stock nexus: Assessing the causality dynamics based on advanced multiscale approaches," Resources Policy, Elsevier, vol. 88(C).
    9. Riccardo Lucchetti & Luca Pedini, 2024. "The Spherical Parametrisation for Correlation Matrices and its Computational Advantages," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1023-1046, August.

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    More about this item

    Keywords

    Financial market; Nonlinear dynamics; Continuous wavelet transform; Discrete wavelet transform; Conditional correlation;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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