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Are benefits from oil-stocks diversification gone? New evidence from a dynamic copula and high frequency data

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  • Avdulaj, Krenar
  • Barunik, Jozef

Abstract

Oil is perceived as a good diversification tool for stock markets. To fully understand this potential, we propose a new empirical methodology that combines generalized autoregressive score copula functions with high frequency data and allows us to capture and forecast the conditional time-varying joint distribution of the oil-stocks pair accurately. Our realized GARCH with time-varying copula yields statistically better forecasts of the dependence and quantiles of the distribution relative to competing models. Employing a recently proposed conditional diversification benefits measure that considers higher-order moments and nonlinear dependence from tail events, we document decreasing benefits from diversification over the past ten years. The diversification benefits implied by our empirical model are, moreover, strongly varied over time. These findings have important implications for asset allocation, as the benefits of including oil in stock portfolios may not be as large as perceived.

Suggested Citation

  • Avdulaj, Krenar & Barunik, Jozef, 2015. "Are benefits from oil-stocks diversification gone? New evidence from a dynamic copula and high frequency data," FinMaP-Working Papers 32, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
  • Handle: RePEc:zbw:fmpwps:32
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    Cited by:

    1. Wen, Xiaoqian & Bouri, Elie & Roubaud, David, 2017. "Can energy commodity futures add to the value of carbon assets?," Economic Modelling, Elsevier, vol. 62(C), pages 194-206.
    2. repec:eee:ememar:v:35:y:2018:i:c:p:69-90 is not listed on IDEAS
    3. Murad A. BEIN & Mehmet AGA, 2016. "On the Linkage between the International Crude Oil Price and Stock Markets: Evidence from the Nordic and Other European Oil Importing and Oil Exporting Countries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 115-134, December.
    4. Kayalar, Derya Ezgi & Küçüközmen, C. Coşkun & Selcuk-Kestel, A. Sevtap, 2017. "The impact of crude oil prices on financial market indicators: copula approach," Energy Economics, Elsevier, vol. 61(C), pages 162-173.
    5. Ito, R., 2016. "Spline-DCS for Forecasting Trade Volume in High-Frequency Finance," Cambridge Working Papers in Economics 1606, Faculty of Economics, University of Cambridge.
    6. repec:eee:eneeco:v:66:y:2017:i:c:p:559-570 is not listed on IDEAS
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    8. Krenar AVDULAJ & Jozef BARUNIK, 2013. "Can We Still Benefit from International Diversification? The Case of the Czech and German Stock Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(5), pages 425-442, November.
    9. repec:eee:eneeco:v:68:y:2017:i:c:p:283-302 is not listed on IDEAS
    10. repec:prg:jnlpol:v:2018:y:2018:i:2:id:1185:p:218-239 is not listed on IDEAS
    11. Ryoko Ito, 2016. "Asymptotic Theory for Beta-t-GARCH," Cambridge Working Papers in Economics 1607, Faculty of Economics, University of Cambridge.
    12. repec:oup:jfinec:v:16:y:2018:i:1:p:63-117. is not listed on IDEAS
    13. Pircalabu, A. & Hvolby, T. & Jung, J. & Høg, E., 2017. "Joint price and volumetric risk in wind power trading: A copula approach," Energy Economics, Elsevier, vol. 62(C), pages 139-154.
    14. Georgios Bampinas & Theodore Panagiotidis, 2017. "Oil and stock markets before and after financial crises: A local Gaussian correlation approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(12), pages 1179-1204, December.

    More about this item

    Keywords

    portfolio diversification; dynamic correlations; high frequency data time-varying copulas; commodities;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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