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Modeling different kinds of spatial dependence in stock returns

Author

Listed:
  • Matthias Arnold
  • Sebastian Stahlberg
  • Dominik Wied

Abstract

The paper modifies previously suggested GMM approaches to spatial autoregression in stock returns. Our model incorporates global dependencies, dependencies inside industrial branches and local dependencies. As can be seen from Euro Stoxx 50 returns, this combination of spatial modeling and finance allows for superior risk forecasts in portfolio management. Copyright Springer-Verlag 2013

Suggested Citation

  • Matthias Arnold & Sebastian Stahlberg & Dominik Wied, 2013. "Modeling different kinds of spatial dependence in stock returns," Empirical Economics, Springer, vol. 44(2), pages 761-774, April.
    • Handle: RePEc:spr:empeco:v:44:y:2013:i:2:p:761-774
    DOI: 10.1007/s00181-011-0528-2
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    References listed on IDEAS

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    More about this item

    Keywords

    GMM estimation; Heteroscedasticity; Spatial dependence; Stock returns; Value at Risk; C13; C51; G12;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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