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Multiscale dependence analysis and portfolio risk modeling for precious metal markets

Author

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  • He, Kaijian
  • Liu, Youjin
  • Yu, Lean
  • Lai, Kin Keung

Abstract

In this paper, we propose a new Bivariate EMD copula based approach to analyze and model the multiscale dependence structure in the precious metal markets. The proposed model constructs the Copula based dependence structure formulation in the Bivariate Empirical Mode Decomposition (BEMD) transformed multiscale domain. We further propose the BEMD Copula based Portfolio Value at Risk (PVaR) model to estimate the precious metal market risk measure. Empirical studies in the typical precious metal markets have been conducted. We found the evidence of multiscale structure of the time varying dependence structure among precious metal markets. We show that significantly improved portfolio risk forecasting performance could be achieved with the proposed model when the multiscale dependence structure is taken into account during the modeling process.

Suggested Citation

  • He, Kaijian & Liu, Youjin & Yu, Lean & Lai, Kin Keung, 2016. "Multiscale dependence analysis and portfolio risk modeling for precious metal markets," Resources Policy, Elsevier, vol. 50(C), pages 224-233.
    • Handle: RePEc:eee:jrpoli:v:50:y:2016:i:c:p:224-233
    DOI: 10.1016/j.resourpol.2016.09.011
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    6. Ji, Qiang & Geng, Jiang-Bo & Tiwari, Aviral Kumar, 2018. "Information spillovers and connectedness networks in the oil and gas markets," Energy Economics, Elsevier, vol. 75(C), pages 71-84.
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    8. Al-Yahyaee, Khamis Hamed & Rehman, Mobeen Ur & Wanas Al-Jarrah, Idries Mohammad & Mensi, Walid & Vo, Xuan Vinh, 2020. "Co-movements and spillovers between prices of precious metals and non-ferrous metals: A multiscale analysis," Resources Policy, Elsevier, vol. 67(C).
    9. Martha Carpinteyro & Francisco Venegas-Martínez & Alí Aali-Bujari, 2021. "Modeling Precious Metal Returns through Fractional Jump-Diffusion Processes Combined with Markov Regime-Switching Stochastic Volatility," Mathematics, MDPI, vol. 9(4), pages 1-17, February.
    10. Zou, Yingchao & Yu, Lean & Tso, Geoffrey K.F. & He, Kaijian, 2020. "Risk forecasting in the crude oil market: A multiscale Convolutional Neural Network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).

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