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Copulas and Portfolios in the Electric Vehicle Sector

Author

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  • Andrej Stenšin

    (School of Economics and Business, Norwegian University of Life Sciences, Christian Magnus Falsens Road 18, 1433 Aas, Norway)

  • Daumantas Bloznelis

    (School of Economics and Business, Norwegian University of Life Sciences, Christian Magnus Falsens Road 18, 1433 Aas, Norway)

Abstract

How can investors unlock the returns on the electric vehicle industry? Available investment choices range from individual stocks to exchange traded funds. We select six representative assets and characterize the time-varying joint distribution of their returns by copula-GARCH models. They facilitate portfolio optimization targeted at a chosen combination of risk and reward. With daily data from 2012 to 2020, we illustrate the models’ applicability by building a minimum expected shortfall portfolio and comparing its performance to that of an equally weighted benchmark. Our results should be of interest to investors and risk managers seeking or facing exposure to the electric vehicle sector.

Suggested Citation

  • Andrej Stenšin & Daumantas Bloznelis, 2022. "Copulas and Portfolios in the Electric Vehicle Sector," JRFM, MDPI, vol. 15(3), pages 1-20, March.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:3:p:132-:d:768240
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    References listed on IDEAS

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    1. Peter Christoffersen, 2004. "Backtesting Value-at-Risk: A Duration-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 84-108.
    2. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    5. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    7. Dißmann, J. & Brechmann, E.C. & Czado, C. & Kurowicka, D., 2013. "Selecting and estimating regular vine copulae and application to financial returns," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 52-69.
    8. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    9. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
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