Selection of Value at Risk Models for Energy Commodities

Laporta, Alessandro G.; Merlo, Luca; Petrella, Lea

doi:10.1016/j.eneco.2018.07.009

Cited by 71 publications

(55 citation statements)

References 34 publications

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“…Following the banking and industry standard, the investment risk of commodity futures has typically been quantified by the value at risk (VaR) and the main objective of most studies has been to identify the most suitable VaR estimation technique (see Aloui & Mabrouk, 2010; Füss et al, 2010; Laporta et al, 2018). This, however, is problematic.…”

Section: Introductionmentioning

confidence: 99%

Time‐varying dynamics of expected shortfall in commodity futures markets

Mehlitz

Auer

2021

Journal of Futures Markets

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Motivated by the growing interest of investors in commodities and by advances in risk measurement, we present a full‐scale analysis of expected shortfall (ES) in commodity futures markets. Besides illustrating the dynamics of historic ES, we evaluate whether popular estimators are suitable for forecasting future ES. By implementing a new backtest, we find that the performance of estimators hinges on market stability. Estimators tend to fail when markets are in turmoil and accurate forecasts are urgently needed. Even though a kernel method performs best on average, our results advise against the use of established estimators for risk (and margin) prediction.

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Section: Introductionmentioning

confidence: 99%

Time‐varying dynamics of expected shortfall in commodity futures markets

Mehlitz

Auer

2021

Journal of Futures Markets

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“…Also fitted is the GARCH (1, 1) model with the AST and GHYP distributions chosen as the innovation distributions. These two distributions allow for asymmetry of the volatility, which has been noted in the literature for cryptocurrency and energy data sets [ 37 , 71 , 72 ]. We have chosen GARCH (1, 1) as a baseline model, because it is the most simple and accessible model available in the R packages and for fitting GARCH type models.…”

Section: Estimation Results and Discussionmentioning

confidence: 99%

Compound distributions for financial returns

et al. 2020

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In this paper, we propose six Student's t based compound distributions where the scale parameter is randomized using functional forms of the half normal, Fré chet, Lomax, Burr III, inverse gamma and generalized gamma distributions. For each of the proposed distribution, we give expressions for the probability density function, cumulative distribution function, moments and characteristic function. GARCH models with innovations taken to follow the compound distributions are fitted to the data using the method of maximum likelihood. For the sample data considered, we see that all but two of the proposed distributions perform better than two popular distributions. Finally, we perform a simulation study to examine the accuracy of the best performing model.

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“…The third model is also mean reverting but with less restrictions in the sense that it permits asymmetric response to both positive and negative past returns. The asymmetric CAViaR specification has become the most popular one for practitioners due to its consideration of the skewness and kurtosis properties of financial series [29,39,42]. In this paper, asymmetric CAViaR is also employed for measurement of the risk of cryptocurrency returns, which is verified through test statistics (see also [43] for a cryptocurrency risk measurement study).…”

Section: Caviar Modelmentioning

confidence: 93%

Risk Connectedness Heterogeneity in the Cryptocurrency Markets

Wang

Huang

2020

Front. Phys.

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This paper examines the risk connectedness across seven cryptocurrencies, Bitcoin, Ethereum, Ripple, Litecoin, Stellar, Monero, and Dash, which have large capitalizations in the cryptocurrency market. The data sample is from August 7, 2015, to February 15, 2020. We measure the return risks of the cryptocurrencies by using the CAViaR model, showing that they have similar risk tendencies, with volatility clusterings from the beginning of 2017 to the end of 2018. The net pairwise spillover index developed by Diebold and Yilmaz [1] is used as the measure of the risk connectedness among the cryptocurrencies. We find that the risk spillover directions are highly correlative with the market capitalizations of the cryptocurrencies. Cryptocurrencies with small market capitalization transmit risks to those with large market capitalization. When there is a downward risk tendency, the risk spillover levels among the cryptocurrencies are stronger than when there is an upward risk tendency, while the spillover directions remain the same under both risk tendencies, except for the cryptocurrency Monero, the particularity of which may be due to the difference in its trading volume compared to the others. We use generalized forecast error variance decomposition for the spillover index and explore the risk connectedness across the cryptocurrencies at different timescales, namely, the short term (0-4 days), medium term (4-30 days) and long term (30-300 days). The risk spillovers can be neglected at the short-term frequency, which implies a delayed effect. The risk spillovers at medium-term frequency are mostly stronger than those at long-term frequency. The dynamic connectedness results show that the means of risk spillover at a long-term frequency are larger than those at medium-term frequency. An inverse result holds for the ranges of risk spillover. The fluctuations of risk spillover at long-term and medium-term frequencies admit the same comparison result with the means of risk spillover in these two frequencies. The findings in this paper provide some suggestions for regulators controlling market stability and cryptocurrency investors generating investment strategies.

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Selection of Value at Risk Models for Energy Commodities

Cited by 71 publications

References 34 publications

Time‐varying dynamics of expected shortfall in commodity futures markets

Time‐varying dynamics of expected shortfall in commodity futures markets

Compound distributions for financial returns

Risk Connectedness Heterogeneity in the Cryptocurrency Markets

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