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Conditional Correlations in the Returns on Oil Companies Stock Prices and Their Determinants

Author

Listed:
  • Matteo Manera

    (Department of Statistics, University of Milan-Bicocca and Fondazione Eni Enrico Mattei)

  • Massimo Giovannini

    (Department of Economics, Boston College)

  • Margherita Grasso

    (Fondazione Eni Enrico Mattei)

  • Alessandro Lanza

    (Eni S.p.A. and Fondazione Eni Enrico Mattei)

Abstract

The identification of the forces that drive stock returns and the dynamics of their associated volatilities is a major concern in empirical economics and finance. This analysis is particularly relevant for determining optimal hedging strategies based on whether shocks to the volatilities of returns of oil companies stock prices, relevant stock market indexes and oil spot and futures prices are high or low, and positively or negatively correlated. This paper investigates the correlations of volatilities in the stock price returns and their determinants for the most important integrated oil companies, namely Bp (BP), Chevron-Texaco (CVX), Eni (ENI), Exxon-Mobil (XOM), Royal Dutch (RD) and Total-Fina Elf (TFE). We measure the actual co-risk in stock returns and their determinants “within” and “between” the different oil companies, using multivariate cointegration techniques in modelling the conditional mean, as well as multivariate GARCH models for the conditional variances. We focus first on the determinants of the market value of each company using the cointegrated VAR/VECM methodology. Then we specifiy the conditional variances of VECM residuals with the Constant Conditional Correlation (CCC) multivariate GARCH model of Bollerslev (1990) and the Dynamic Conditional Correlation (DCC) multivariate GARCH model of Engle (2002). The “within” and “between” DCC indicate low to high/extreme interdependence between the volatilities of companies’ stock returns and the relevant stock market indexes or Brent oil prices.

Suggested Citation

  • Matteo Manera & Massimo Giovannini & Margherita Grasso & Alessandro Lanza, 2004. "Conditional Correlations in the Returns on Oil Companies Stock Prices and Their Determinants," Working Papers 2004.71, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2004.71
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    References listed on IDEAS

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    Cited by:

    1. Bianconi, Marcelo & Yoshino, Joe A., 2014. "Risk factors and value at risk in publicly traded companies of the nonrenewable energy sector," Energy Economics, Elsevier, vol. 45(C), pages 19-32.
    2. Kang, Wensheng & Perez de Gracia, Fernando & Ratti, Ronald A., 2017. "Oil price shocks, policy uncertainty, and stock returns of oil and gas corporations," Journal of International Money and Finance, Elsevier, vol. 70(C), pages 344-359.
    3. Cardona, Laura & Gutiérrez, Marcela & Agudelo, Diego A., 2017. "Volatility transmission between US and Latin American stock markets: Testing the decoupling hypothesis," Research in International Business and Finance, Elsevier, pages 115-127.
    4. Diaz, Elena Maria & de Gracia, Fernando Perez, 2017. "Oil price shocks and stock returns of oil and gas corporations," Finance Research Letters, Elsevier, vol. 20(C), pages 75-80.
    5. Kourouvakalis, Stylianos, 2008. "Méthodes numériques pour la valorisation d'options swings et autres problèmes sur les matières premières," Economics Thesis from University Paris Dauphine, Paris Dauphine University, number 123456789/116 edited by Geman, Hélyette.
    6. Frank Venmans, 2015. "Capital market response to emission allowance prices: a multivariate GARCH approach," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 17(4), pages 577-620, October.

    More about this item

    Keywords

    Constant conditional correlations; Dynamic conditional correlations; Multivariate GARCH models; Stock price indexes; Brent oil prices; Spot and futures prices; Multivariate cointegration; VECM;

    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
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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