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

Long Hai Vo, Duc Hong Vo

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article number2843
Number of pages19
JournalSustainability
Volume11
Issue number10
DOIs
Publication statusPublished - 2 May 2019

Cite this

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title = "Application of Wavelet-Based Maximum Likelihood Estimator in Measuring Market Risk for Fossil Fuel",
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.",
keywords = "Wavelet methodology, Long-range dependence, Risk measurement, Fossil fuels, Climate change, LONG-MEMORY, TIME-SERIES, UNIT-ROOT, OIL, VOLATILITY, PRICES, EFFICIENT, RANGE, MODEL",
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year = "2019",
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language = "English",
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journal = "Sustainability (Switzerland)",
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Application of Wavelet-Based Maximum Likelihood Estimator in Measuring Market Risk for Fossil Fuel. / Vo, Long Hai; Vo, Duc Hong.

In: Sustainability, Vol. 11, No. 10, 2843, 02.05.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Application of Wavelet-Based Maximum Likelihood Estimator in Measuring Market Risk for Fossil Fuel

AU - Vo, Long Hai

AU - Vo, Duc Hong

PY - 2019/5/2

Y1 - 2019/5/2

N2 - 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.

AB - 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.

KW - Wavelet methodology

KW - Long-range dependence

KW - Risk measurement

KW - Fossil fuels

KW - Climate change

KW - LONG-MEMORY

KW - TIME-SERIES

KW - UNIT-ROOT

KW - OIL

KW - VOLATILITY

KW - PRICES

KW - EFFICIENT

KW - RANGE

KW - MODEL

U2 - 10.3390/su11102843

DO - 10.3390/su11102843

M3 - Article

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JO - Sustainability (Switzerland)

JF - Sustainability (Switzerland)

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ER -