Does Mixed Frequency Information Help To Forecast the Value at Risk of the Crude Oil Market?

The international crude oil market is complicated in itself and with the rapid development of China in recent years, the dramatic changes of the international crude oil market have brought some risk to the security of Chinas oil market and the economic development of China. Value at risk (VaR), an effective measurement of financial risk, can be used to assess the risk of refined oil retail sales as well. However, VaR, as a model that can be applied to complicated nonlinear data, has not yet been widely researched. Therefore, an improved Historical Simulation Approach, historical stimulation of genetic algorithm to parameters selection of support vector machine, HSGA-SVMF, in this paper, is proposed, which is based on an approach the historical simulation with ARMA forecasts, HSAF. By comparing it with the HSAF and HSGA-SVMF approach, this paper gives evidence to show that HSGA-SVMF has a more effective forecasting power in the field of amount of refined oil.

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Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic

Energies ◽

10.3390/en14144147 ◽

2021 ◽

Vol 14 (14) ◽

pp. 4147

Author(s):

Krzysztof Echaust ◽

Małgorzata Just

Keyword(s):

At Risk ◽

Crude Oil ◽

Value At Risk ◽

Tail Dependence ◽

Oil Price ◽

Health Crisis ◽

Oil Market ◽

Volatility Index ◽

Conditional Copula ◽

The Impact

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.

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Quantile-based GARCH-MIDAS: Estimating value-at-risk using mixed-frequency information

Finance Research Letters ◽

10.1016/j.frl.2021.101965 ◽

2021 ◽

pp. 101965

Author(s):

Yan Xu ◽

Xinyu Wang ◽

Hening Liu

Keyword(s):

At Risk ◽

Value At Risk ◽

Mixed Frequency ◽

Frequency Information

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Forecasts of Value-at-Risk and Expected Shortfall in the Crude Oil Market: A Wavelet-Based Semiparametric Approach

Energies ◽

10.3390/en13143700 ◽

2020 ◽

Vol 13 (14) ◽

pp. 3700

Author(s):

Lu Yang ◽

Shigeyuki Hamori

Keyword(s):

At Risk ◽

Crude Oil ◽

Time Scales ◽

Hybrid Model ◽

Value At Risk ◽

Expected Shortfall ◽

Semiparametric Models ◽

Short Term ◽

Oil Market

We propose the use of wavelet-based semiparametric models for forecasting the value-at-risk (VaR) and expected shortfall (ES) in the crude oil market. We compared the forecast outcomes across different time scales for three semiparametric models, three nonparametric, distribution-based, generalized, autoregressive, conditional, heteroskedasticity (GARCH) models, and three rolling-window models. We found that the GARCH model estimated by the Fissler and Ziegel (FZ) zero loss minimization (GARCH-FZ) model performs the best at forecasting the VaR and ES in the short term, whereas the hybrid model performs the best for mid- and long-term time scales. Thus, long-term investors should consider the hybrid model and short-term investors should employ the GARCH-FZ model in their risk management processes. Overall, our proposed wavelet-based semiparametric models outperform the other models tested for all time scales and market conditions. As such, we suggest that these models are considered for the management of crude oil price risk and in the development of energy policy.

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