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On the characteristics of dynamic correlations between asset pairs

Author

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  • Jacobs, Michael
  • Karagozoglu, Ahmet K.

Abstract

Recent research provides considerable evidence that correlations between assets change significantly over time and diversification benefits of correlations may vary substantially based on the time-varying measure of correlation used for different asset types. Our study evaluates and compares alternative time-series correlation modeling techniques according to both statistical and economic metrics, focusing specifically on individual asset pairs. We identify the moving correlation structure that best tracks the dynamic conditional correlation estimates using a large set of different financial time series encompassing 467 asset pairs in nine different asset classes. Results from our direct, statistical loss function based, and indirect, portfolio mean-variance based, forecast evaluations provide optimal window-length ranges for 36 asset-class pairs which should help in portfolio construction as well as risk management. Furthermore for robustness tests, we implement the model confidence set approach which, without a benchmark specification, produces a set of models constructed to contain the best models with a given level of confidence among competing forecast evaluations.

Suggested Citation

  • Jacobs, Michael & Karagozoglu, Ahmet K., 2014. "On the characteristics of dynamic correlations between asset pairs," Research in International Business and Finance, Elsevier, vol. 32(C), pages 60-82.
    • Handle: RePEc:eee:riibaf:v:32:y:2014:i:c:p:60-82
    DOI: 10.1016/j.ribaf.2014.03.004
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    Cited by:

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    2. Miralles-Quirós, José Luis & Miralles-Quirós, María del Mar, 2017. "The Copula ADCC-GARCH model can help PIIGS to fly," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 50(C), pages 1-12.
    3. Miralles-Quirós, José Luis & Daza-Izquierdo, Julio, 2015. "Do DOW returns really influence the intraday Spanish stock market behavior?," Research in International Business and Finance, Elsevier, vol. 33(C), pages 99-126.
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    6. Jiaying Peng & Zhenghui Li & Benjamin M. Drakeford, 2020. "Dynamic Characteristics of Crude Oil Price Fluctuation—From the Perspective of Crude Oil Price Influence Mechanism," Energies, MDPI, vol. 13(17), pages 1-19, August.

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    More about this item

    Keywords

    Correlation forecasting; Dynamic conditional correlation; GARCH; Risk management; Hedging;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G19 - Financial Economics - - General Financial Markets - - - Other

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