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Modelling Asymmetric Dependence Using Copula Functions: An application to Value-at-Risk in the Energy Sector

Author

Listed:
  • Andrea Bastianin

    (Fondazione Eni Enrico Mattei)

Abstract

In this paper I have used copula functions to forecast the Value-at-Risk (VaR) of an equally weighted portfolio comprising a small cap stock index and a large cap stock index for the oil and gas industry. The following empirical questions have been analyzed: (i) are there nonnormalities in the marginals? (ii) are there nonnormalities in the dependence structure? (iii) is it worth modelling these nonnormalities in risk- management applications? (iv) do complicated models perform better than simple models? As for questions (i) and (ii) I have shown that the data do deviate from the null of normality at the univariate, as well as at the multivariate level. When considering the dependence structure of the data I have found that asymmetries show up in their unconditional distribution, as well as in their unconditional copula. The VaR forecasting exercise has shown that models based on Normal marginals and/or with symmetric dependence structure fail to deliver accurate VaR forecasts. These findings confirm the importance of nonnormalities and asymmetries both in-sample and out-of-sample.

Suggested Citation

  • Andrea Bastianin, 2009. "Modelling Asymmetric Dependence Using Copula Functions: An application to Value-at-Risk in the Energy Sector," Working Papers 2009.24, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2009.24
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    Citations

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    Cited by:

    1. Westner, Günther & Madlener, Reinhard, 2012. "Investment in new power generation under uncertainty: Benefits of CHP vs. condensing plants in a copula-based analysis," Energy Economics, Elsevier, vol. 34(1), pages 31-44.
    2. Wanat, Stanisław & Papież, Monika & Śmiech, Sławomir, 2014. "Causality in distribution between European stock markets and commodity prices: Using independence test based on the empirical copula," MPRA Paper 57706, University Library of Munich, Germany.
    3. Wen, Xiaoqian & Wei, Yu & Huang, Dengshi, 2012. "Measuring contagion between energy market and stock market during financial crisis: A copula approach," Energy Economics, Elsevier, vol. 34(5), pages 1435-1446.
    4. Janda, Karel & Krska, Stepan & Prusa, Jan, 2014. "Odhad nákladů na podporu české fotovoltaické energie
      [The Estimation of the Cost of Promotion of the Czech Photovoltaic Energy]
      ," MPRA Paper 54108, University Library of Munich, Germany.
    5. Zhu, Hui-Ming & Li, Rong & Li, Sufang, 2014. "Modelling dynamic dependence between crude oil prices and Asia-Pacific stock market returns," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 208-223.

    More about this item

    Keywords

    Copula functions; Forecasting; Value-At-Risk;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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