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Advanced Risk Analysis and Short-Term Forecast Model for Global Energy Market

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (27 June 2023) | Viewed by 4644

Special Issue Editors


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Guest Editor
Department of Methods and Models for Economics, Territory and Finance, Sapienza University of Rome, 00184 Rome, Italy
Interests: bayesian inference; quantile regression; tail risk measures and models; time series
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
MEMOTEF Department, Sapienza University of Rome, 00185 Rome, Italy
Interests: Volatility; mixed-frequency methods; tail risk measures and models; financial time series

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Guest Editor
Department of Statistical Sciences DISS - interfaculty, Sapienza University of Rome, 5, 00185 Roma RM, Italy
Interests: financial risks; energy finance; quantitative finance

Special Issue Information

Dear Colleagues,

During recent decades, the growth of energy markets has spurred the interest of academics, risk managers, and regulators. Initially dominated by the presence of non-renewable resources such as petroleum, energy markets nowadays include several energy providers, such as coal, natural gas, and a variety of renewables. Besides, the financialization of commodities has called for new statistical and econometric theories and methodologies to model commodity time series and monitor the propagation of commodity risk that arises from fluctuations in commodity future price values. How energy sources contribute to systemic risk, how they affect other energy sources, how and to what extent energy returns and volatilities can be forecasted are only a few examples of relevant questions in the global energy markets.

The goal of this Special Issue is to present high-quality papers dealing with (but not limited to) the following topics:

  • Modeling the risk of energy commodities;
  • Forecasting models for global energy markets;
  • Tail risk;
  • Risk measures for global energy markets;
  • Systemic risk of energy commodities;
  • Measuring the spillovers and the co-movements of energy returns;
  • Forecasting the volatility of energy returns, including the potential influence of mixed-frequency variables;
  • Investigating the profitability of using machine learning tools in energy markets.

Prof. Lea Petrella
Prof. Vincenzo Candila
Prof. Giacomo Morelli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • energy commodities
  • forecasting models
  • financial econometrics of energy commodities
  • quantitative energy finance
  • spillovers
  • risk measures
  • tail risk interdependence

Published Papers (2 papers)

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Research

17 pages, 2582 KiB  
Article
Risk Dependence and Risk Spillovers Effect from Crude Oil on the Chinese Stock Market and Gold Market: Implications on Portfolio Management
by Bin Mo, Juan Meng and Guannan Wang
Energies 2023, 16(5), 2141; https://doi.org/10.3390/en16052141 - 22 Feb 2023
Cited by 4 | Viewed by 1533
Abstract
We analyze crude oil’s dependence and the risk spillover effect on the Chinese stock market and the gold market. We compare both static and dynamic copula functions and calculate the average upward and downward spillover effect using the time-varying Copula model and the [...] Read more.
We analyze crude oil’s dependence and the risk spillover effect on the Chinese stock market and the gold market. We compare both static and dynamic copula functions and calculate the average upward and downward spillover effect using the time-varying Copula model and the conditional value-at-risk approach. By utilizing daily data on crude oil prices, China’s stock market, and the gold market, we observe an asymmetric spillover effect: the downside spillover effects from crude oil prices on the Chinese stock market and gold market are larger than the upside spillover effect. We then identify changes in the structure of the sample periods and calculate the dynamic conditional correlation between them. In addition, we explore the optimal weight and hedge ratios in diversified portfolios to mitigate potential risks. Our results suggest that investors and portfolio managers should frequently adjust their portfolio strategies, particularly during extreme events like COVID-19, when financial assets become more volatile. Furthermore, crude oil can help reduce the risk in the Chinese stock market and gold market to some extent during different sub-periods. Full article
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21 pages, 3421 KiB  
Article
Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic
by Krzysztof Echaust and Małgorzata Just
Energies 2021, 14(14), 4147; https://doi.org/10.3390/en14144147 - 9 Jul 2021
Cited by 16 | Viewed by 2359
Abstract
This study investigates the dependence between extreme returns of West Texas Intermediate (WTI) crude oil prices and the Crude Oil Volatility Index (OVX) changes as well as the predictive power of OVX to generate accurate Value at Risk (VaR) forecasts for crude oil. [...] Read more.
This study investigates the dependence between extreme returns of West Texas Intermediate (WTI) crude oil prices and the Crude Oil Volatility Index (OVX) changes as well as the predictive power of OVX to generate accurate Value at Risk (VaR) forecasts for crude oil. We focus on the COVID-19 pandemic period as the most violate in the history of the oil market. The static and dynamic conditional copula methodology is used to measure the tail dependence coefficient (TDC) between the variables. We found a strong relationship in the tail dependence between negative returns on crude oil and OVX changes and the tail independence for positive returns. The time-varying copula discloses the strongest tail dependence of negative oil price shocks and the index changes during the COVID-19 health crisis. The findings indicate the ability of the OVX index to be a fear gauge with respect to the oil market. However, we cannot confirm the ability of OVX to improve one day-ahead forecasts of the Value at Risk. The impact of investors’ expectations embedded in OVX on VaR forecasts seems to be negligible. Full article
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