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Financialization, Crisis and Commodity Correlation Dynamics

Author

Listed:
  • Annastiina Silvennoinen

    (Queensland University of Technology)

  • Susan Thorp

    (University of Sydney)

Abstract

We study bi-variate conditional volatility and correlation dynamics for individual commodity futures and financial assets from May 1990-July 2009 using DSTCC-GARCH (Silvennoinen and Terasvirta 2009). These models allow correlation to vary smoothly between extreme states via transition functions driven by indicators of market conditions. Expected stock volatility and money manager open interest in futures markets are relevant transition variables. Results point to increasing integration between commodities and financial markets. Higher commodity returns volatility is predicted by lower interest rates and corporate bond spreads, US dollar depreciations, higher expected stock volatility and financial traders open positions. We observe higher and more variable correlations between commodity futures and financial asset returns, particularly from mid-sample, often predicted by higher expected stock volatility. For many pairings, we observe a structural break in the conditional correlation processes from the late 1990s.

Suggested Citation

  • Annastiina Silvennoinen & Susan Thorp, 2010. "Financialization, Crisis and Commodity Correlation Dynamics," Research Paper Series 267, Quantitative Finance Research Centre, University of Technology, Sydney.
  • Handle: RePEc:uts:rpaper:267
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    References listed on IDEAS

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    More about this item

    Keywords

    commodity futures; double smooth transition; conditional correlation; financialization;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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