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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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    Cited by:

    1. 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.
    2. Jana, Rabin K. & Ghosh, Indranil, 2023. "Time-varying relationship between geopolitical uncertainty and agricultural investment," Finance Research Letters, Elsevier, vol. 52(C).
    3. 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.
    4. 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.
    5. 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).

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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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