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Stock market returns and oil price shocks: A CoVaR analysis based on dynamic vine copula models

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  • Julia Kielmann

    (Technical University of Munich)

  • Hans Manner

    (University of Graz)

  • Aleksey Min

    (Technical University of Munich)

Abstract

Crude oil plays a significant role in economic developments in the world. Understanding the relationship between oil price changes and stock market returns helps to improve portfolio strategies and risk positions. Kilian (Am Econ Rev 99(3): 1053–1069, 2009) proposes to decompose the oil price into three types of oil price shocks by using a structural vector autoregression model. This paper investigates the dynamic, nonlinear dependence and risk spillover effects between BRICS stock returns and the different types of oil price shocks using an appropriate multivariate and dynamic copula model. Risk is measured using the conditional value at risk, conditioning on one or more simultaneous oil and stock market shocks. For this purpose, a D-vine-based quantile regression model and the GAS copula model are combined. Our results show, inter alia, that the early stages of the Covid-19 crisis lead to increasing risk levels in the BRICS stock markets except for the Chinese one, which has recovered quickly and therefore shows no changes in the risk level.

Suggested Citation

  • Julia Kielmann & Hans Manner & Aleksey Min, 2022. "Stock market returns and oil price shocks: A CoVaR analysis based on dynamic vine copula models," Empirical Economics, Springer, vol. 62(4), pages 1543-1574, April.
    • Handle: RePEc:spr:empeco:v:62:y:2022:i:4:d:10.1007_s00181-021-02073-9
    DOI: 10.1007/s00181-021-02073-9
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    Cited by:

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    2. Mbarki, Imen & Khan, Muhammad Arif & Karim, Sitara & Paltrinieri, Andrea & Lucey, Brian M., 2023. "Unveiling commodities-financial markets intersections from a bibliometric perspective," Resources Policy, Elsevier, vol. 83(C).
    3. Khan, Nasir & Saleem, Asima & Ozkan, Oktay, 2023. "Do geopolitical oil price risk influence stock market returns and volatility of Pakistan: Evidence from novel non-parametric quantile causality approach," Resources Policy, Elsevier, vol. 81(C).
    4. Si Mohammed, Kamel & Tedeschi, Marco & Mallek, Sabrine & Tarczyńska-Łuniewska, Małgorzata & Zhang, Anqi, 2023. "Realized semi variance quantile connectedness between oil prices and stock market: Spillover from Russian-Ukraine clash," Resources Policy, Elsevier, vol. 85(PA).
    5. Wang, Xiangning & Huang, Qian & Zhang, Shuguang, 2023. "Effects of macroeconomic factors on stock prices for BRICS using the variational mode decomposition and quantile method," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    6. Li, Jiang-Cheng & Xu, Ming-Zhe & Han, Xu & Tao, Chen, 2022. "Dynamic risk resonance between crude oil and stock market by econophysics and machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    7. Liu, Feng & Xu, Jie & Ai, Chunrong, 2023. "Heterogeneous impacts of oil prices on China's stock market: Based on a new decomposition method," Energy, Elsevier, vol. 268(C).

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    More about this item

    Keywords

    Oil prices; Risk management; Time-varying copula; D-vine copula; CoVaR;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: 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
    • 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

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