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Testing for relevant dependence change in financial data: a CUSUM copula approach

Author

Listed:
  • Tim Kutzker

    (University of Cologne)

  • Florian Stark

    (University of Cologne)

  • Dominik Wied

    (University of Cologne)

Abstract

We propose a new nonparametric test for detecting relevant breaks in copula functions. We assume that the data is driven by two non-equal copulas $$C_1$$ C 1 and $$C_2$$ C 2 . Under the null hypothesis, the copula difference within an appropriate norm is smaller than a certain positive adjustable threshold $$\varDelta $$ Δ . Within the alternative hypothesis, the copula difference exceeds the fixed value $$\varDelta $$ Δ . The test is based on a cumulative sum approach of the empirical copula with sequentially estimated marginals. We propose a bootstrap procedure to compute critical values. The Monte Carlo simulation indicates that the test results in a reasonable sized and powered testing procedure. A real data application of the DAX30 up to cross-sectional dimension $$N=30$$ N = 30 shows the test’s ability to detect relevant break points.

Suggested Citation

  • Tim Kutzker & Florian Stark & Dominik Wied, 2021. "Testing for relevant dependence change in financial data: a CUSUM copula approach," Empirical Economics, Springer, vol. 60(4), pages 1875-1894, April.
    • Handle: RePEc:spr:empeco:v:60:y:2021:i:4:d:10.1007_s00181-019-01811-4
    DOI: 10.1007/s00181-019-01811-4
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    References listed on IDEAS

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    1. Bücher, Axel & Ruppert, Martin, 2013. "Consistent testing for a constant copula under strong mixing based on the tapered block multiplier technique," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 208-229.
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    5. Bücher, Axel & Kojadinovic, Ivan & Rohmer, Tom & Segers, Johan, 2014. "Detecting changes in cross-sectional dependence in multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 111-128.
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    More about this item

    Keywords

    Relevant change; Copula; Break testing; Bootstrap; CUSUM;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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

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