Modeling the multivariate dynamic dependence structure of commodity futures portfolios

Aepli, Matthias Daniel; Füss, Roland; Henriksen, Tom Erik Sønsteng; Paraschiv, Florentina

doi:10.1016/j.jcomm.2017.05.002

Cited by 19 publications

(12 citation statements)

References 66 publications

(62 reference statements)

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“…The table indicates that the GARCH(1, 1) is the optimal choice overall, and we will continue with this specification. Similar results are found in Paraschiv et al (2015) and Aepli (2011). Table 5 displays the estimated GARCH-GJR parameters.…”

Section: Application Of the Garch-gjrsupporting

confidence: 78%

“…In McNeil et al (2015) the authors suggested using a combination of GARCH and EVT. This methodology is popular in recent literature, with the largest proportion of new studies focusing on stock markets or single commodities (Ghorbel and Souilmi 2014;Liu 2011;Wang et al 2010;Aepli 2011).…”

Section: Regulatory Requirements For Stress Testingmentioning

confidence: 99%

“…We have shown the deviations from the normal and student t distribution for the returns, especially in the tails. Extreme Value Theory with the Peak over Threshold method has been employed in earlier studies (Aepli 2011;Paraschiv et al 2015;Wang et al 2010) showing a good modeling performance in shaping heavy tails. This is in line with the regulatory requirements for stress testing that point out the need to give a special attention to tail risk in asset returns.…”

Section: Motivating the Choice Of Modeling Approachmentioning

confidence: 99%

“…Due to the common bust and boom cycles and co-integration of commodity markets, is important to model the dependence structure between returns in a realistic way. In Aepli et al (2017) the authors show the importance of modelling time-variation and asymmetries in the dependence structure between the risk factors of a portfolio of commodity futures. In support of Basel III critics on over-reliance on historical correlations, the authors introduce multivariate dynamic copula models as a superior alternative.…”

Section: Motivating the Choice Of Modeling Approachmentioning

confidence: 99%

“…There exist a numerous amount of copulas to choose from, and the best choice is dependent on the aim of the analysis and the data. Analysis in Paraschiv et al 2015, Aepli (2011) and McNeil et al (2015) find the t copula to be superior over the Gaussian copula in the context of modelling multivariate financial return data. For our purpose, we therefore prefer a t copula over the more common Gaussian copula.…”

Section: Motivating the Choice Of Modeling Approachmentioning

confidence: 99%

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Portfolio stress testing applied to commodity futures

2020

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In this article, we construct a portfolio of commodity futures which mimics the Dow Jones Commodity Index and perform an extensive stress testing exercise with a focus on hybrid scenarios. The increased volume of investments in commodities as financial instruments over the last decades underline the importance of a more thorough framework for stress testing of related portfolios. Our study is the first to show comparatively the marginal impact of the model choice for portfolio components versus the marginal role of tail dependence on the portfolio profit and loss in stress testing exercises. We model the distribution of returns of portfolio components with an asymmetric AR-GARCH model combined with Extreme Value Theory for extreme tails, and employ multivariate copula functions to model the time-varying joint dependence structure. Our study reveals that indeed, for a realistic stress test, a special attention should be given to the tail risk in individual commodity returns as well as to tail correlations. We also draw conclusions about parameter risk persistent in stress testing exercises. Finally yet importantly, in line with Basel IIIb, the study highlights the importance of using forward-looking hybrid and hypothetical scenarios over historical scenarios. Keywords Stress testing • Commodity futures • Risk measures • Extreme value theory • Copula functions Research funded by Adolf Øiens Donasjonsfond, "Energizing new Computional Frontiers" and by the

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Section: Application Of the Garch-gjrsupporting

confidence: 78%

Section: Regulatory Requirements For Stress Testingmentioning

confidence: 99%

Section: Motivating the Choice Of Modeling Approachmentioning

confidence: 99%

Section: Motivating the Choice Of Modeling Approachmentioning

confidence: 99%

Section: Motivating the Choice Of Modeling Approachmentioning

confidence: 99%

See 3 more Smart Citations

Portfolio stress testing applied to commodity futures

2020

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A seasonal copula mixture for hedging the clean spark spread with wind power futures

2019

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The recently introduced German wind power futures have brought the opportunity to address the problem of volume risk in wind power generation directly. In this paper, we study the hedging benefits of these instruments in the context of peak gas-fired power plants, by employing a strategy that allows trading in the day-ahead clean spark spread and wind power futures. To facilitate hedging decisions, we propose a seasonal copula mixture for the joint behavior of the day-ahead clean spark spread and the daily wind index. The model describes the data surprisingly well, both in terms of the marginals and the dependence structure, while being straightforward and easy to implement. Based on Monte Carlo simulations from the proposed model, the results indicate that significant benefits can be achieved by using wind power futures. Moreover, a comparison study shows that accounting for asymmetry, tail dependence, and seasonality in the dependence structure is especially important in the context of risk management.

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