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Application of Wavelet-Based Maximum Likelihood Estimator in Measuring Market Risk for Fossil Fuel

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  • Long Hai Vo

    (Economics Department, Business School, The University of Western Australia, Perth WA 6009, Australia
    Faculty of Finance, Banking and Business Administration, Quy Nhon University, Quy Nhon 590000, Vietnam)

  • Duc Hong Vo

    (Business and Economics Research Group, Ho Chi Minh City Open University, Hồ Chí Minh City 700000, Vietnam)

Abstract

Energy commodity prices are inherently volatile, since they are determined by the volatile global demand and supply of fossil fuel extractions, which in the long-run will affect the observed climate patterns. Measuring the risk associated with energy price changes, therefore, ultimately provides us with an important tool to study the economic drivers of climate changes. This study examines the potential use of long-memory estimation methods in capturing such risk. In particular, we are interested in investigating the energy markets’ efficiency at the aggregated level, using a novel wavelet-based maximum likelihood estimator (waveMLE). We first compare the performance of various conventional estimators with this new method. Our simulated results show that waveMLE in general outperforms these previously well-established estimators. Additionally, we document that while energy returns realizations follow a white-noise and are generally independent, volatility processes exhibits a certain degree of long-range dependence.

Suggested Citation

  • Long Hai Vo & Duc Hong Vo, 2019. "Application of Wavelet-Based Maximum Likelihood Estimator in Measuring Market Risk for Fossil Fuel," Sustainability, MDPI, vol. 11(10), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2843-:d:232387
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    1. Hai Vo, Long & Hong Vo, Duc, 2020. "Long-run dynamics of exchange rates: A multi-frequency investigation," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    2. Thang Cong Nguyen & Tan Ngoc Vu & Duc Hong Vo & Michael McAleer, 2020. "Systematic Risk at the Industry Level: A Case Study of Australia," Risks, MDPI, vol. 8(2), pages 1-12, April.

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