Revisiting value-at-risk and expected shortfall in oil markets under structural breaks: The role of fat-tailed distributions

Patra, Saswat

doi:10.1016/j.eneco.2021.105452

Cited by 25 publications

(9 citation statements)

References 36 publications

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“…Finally, the relative magnitudes of VaR and ES forecasts are compared. We first follow Patra ( 2021 ) to examine ES to VaR ratio at the 99% confidence level. Table 13 reports the average of the ratio over the entire out-of-sample period and the maximum values of the ratio during each sub-sample evaluation period for Brent and WTI, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Although GARCH-type models with normal innovations can generate data with unconditionally fat tails, they are insufficient to account for all of the unconditional leptokurtosis and skewness observed in oil returns series (e.g., Lux et al. 2016 ; Patra 2021 ). As a result, in addition to the normal distribution, this study compares the non-parametric historical simulation (e.g., Westgaard et al.…”

Section: Methodsmentioning

confidence: 99%

“… 2017 ), generalized pareto (Marimoutou et al. 2009 ), and Pearson’s type IV and Johnson’s Su distributions (Patra 2021 ). According to these studies, choosing the appropriate distribution for the innovation process is critical because it affects the accuracy of the required quantile estimates.…”

Section: Introductionmentioning

confidence: 99%

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Oil tail-risk forecasts: from financial crisis to COVID-19

Kuang

2022

Risk Manag

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The coronavirus outbreak has caused unprecedented volatility in oil prices. This paper extends previous studies on oil Value-at-Risk (VaR) by providing extra insights into Expected Shortfall (ES) forecasting over the last decade, including several oil crises. We introduce a conditional volatility model combined with the Cornish–Fisher expansion for ES forecasting. In comparison to the widely used volatility models and innovation distributions, this approach is superior for predicting the ES of long positions but overestimates VaR for short positions. Overall, the volatility model addressing leverage effects with skewed t innovation produces the most accurate joint VaR and ES forecasting. Moreover, the magnitude of ES relative to VaR varies across models and time, implying that ES should be used in conjunction with VaR to inform timely risk management decisions. The results would be of interest to the regulatory authorities, energy companies, and financial institutions for oil tail-risk forecasting.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Oil tail-risk forecasts: from financial crisis to COVID-19

Kuang

2022

Risk Manag

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“…To the best of our knowledge, the existing literature has been mainly focused on the tail behaviour of gold and cryptocurrencies (see, for example, Reboredo, 2013 ; Osterrieder and Lorenz, 2017 ; Feng et al, 2018 ; Gkillas and Katsiampa, 2018 ; Selmi et al, 2018 ; Shahzad et al, 2019b ; Stavroyiannis, 2018 ; Conlon and McGee, 2020 ; Conlon et al, 2020 ; Kwon, 2020 ). Not many studies consider other commodities, such as, silver, platinum and palladium ( Hammoudeh et al, 2011 ), oil ( Patra, 2021 ), and overall commodity indexes ( Bouri et al, 2020 ; Mehlitz and Auer, 2021 ). Besides, only few among them investigate the average of all losses which are greater or equal than VaR, that is to consider the ES modelling, in a separate setting to their VaR estimation.…”

Section: Related Literaturementioning

confidence: 99%

Does safe haven exist? Tail risks of commodity markets during COVID-19 pandemic

Enilov

Mensi

Stankov

2023

Journal of Commodity Markets

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“…So far, it has not found application with distributional neural networks. However, it is often used in many other areas, e.g., energy and finance markets or medicine [56][57][58][59] and also in electricity price forecasting using regression frameworks [60][61][62] . Based on Figure 3 we suspect that the Johnson's SU is more suitable for modeling the electricity prices than the normal.…”

Section: The Datamentioning

confidence: 99%

Distributional neural networks for electricity price forecasting

Marcjasz¹,

Narajewski²,

Weron³

et al. 2022

Preprint

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We present a novel approach to probabilistic electricity price forecasting (EPF) which utilizes distributional artificial neural networks. The novel network structure for EPF is based on a regularized distributional multilayer perceptron (DMLP) which contains a probability layer. Using the TensorFlow Probability framework, the neural network's output is defined to be a distribution, either normal or potentially skewed and heavy-tailed Johnson's SU (JSU). The method is compared against state-of-the-art benchmarks in a forecasting study. The study comprises forecasting involving day-ahead electricity prices in the German market. The results show evidence of the importance of higher moments when modeling electricity prices.

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Revisiting value-at-risk and expected shortfall in oil markets under structural breaks: The role of fat-tailed distributions

Cited by 25 publications

References 36 publications

Oil tail-risk forecasts: from financial crisis to COVID-19

Oil tail-risk forecasts: from financial crisis to COVID-19

Does safe haven exist? Tail risks of commodity markets during COVID-19 pandemic

Distributional neural networks for electricity price forecasting

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