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Monitoring multivariate variance changes

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  • Pape, Katharina
  • Wied, Dominik
  • Galeano, Pedro

Abstract

We propose a model-independent multivariate sequential procedure to monitor changes in the vector of componentwise unconditional variances in a sequence of p-variate random vectors. The asymptotic behavior of the detector is derived and consistency of the procedure stated. A detailed simulation study illustrates the performance of the procedure confronted with different types of data generating processes. We conclude with an application to the log returns of a group of DAX listed assets.

Suggested Citation

  • Pape, Katharina & Wied, Dominik & Galeano, Pedro, 2016. "Monitoring multivariate variance changes," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 54-68.
    • Handle: RePEc:eee:empfin:v:39:y:2016:i:pa:p:54-68
    DOI: 10.1016/j.jempfin.2016.08.007
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    3. Castrillón-Candás, Julio E. & Kon, Mark, 2022. "Anomaly detection: A functional analysis perspective," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    4. Golosnoy, Vasyl & Schmid, Wolfgang & Seifert, Miriam Isabel & Lazariv, Taras, 2020. "Statistical inferences for realized portfolio weights," Econometrics and Statistics, Elsevier, vol. 14(C), pages 49-62.
    5. Josua Gösmann & Tobias Kley & Holger Dette, 2021. "A new approach for open‐end sequential change point monitoring," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(1), pages 63-84, January.
    6. Fang Duan & Dominik Wied, 2018. "A residual-based multivariate constant correlation test," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(6), pages 653-687, August.

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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