Testing for Structural Breaks in Correlations: Does it Improve Value-at-Risk Forecasting?

Berens, Tobias; Weiß, Gregor N. F.; Wied, Dominik

doi:10.2139/ssrn.2265488

Cited by 6 publications

(5 citation statements)

References 29 publications

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“…First, Xing et al (2012) demonstrated that the credit rating transitions' structural breaks can be well captured by their proposed model, and they showed that structural breaks in credit rating dynamics were different by industries. Berens et al (2015) showed that the performances of correlation models such as the constant conditional correlation (CCC) model and the dynamic conditional correlation (DCC) model can be improved by taking into consideration structural breaks in asset comovements.…”

Section: Recent Literature Reviewmentioning

confidence: 99%

A Multivariate Analysis of the Effects of Structural Breaks on Stock Return Volatility Persistence: The Case of the US and Japan

Tsuji¹

2019

IJBA

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This paper quantitatively inspects the effects of structural breaks in stock returns on their volatility persistence by using the stock return data of the US and Japan. More concretely, applying the diagonal BEKK-MGARCH model with and without structural break dummies to the returns of S&P 500 and TOPIX, we reveal the following interesting findings. (1) First, we clarify that for both the US and Japanese stock returns, the values of the GARCH parameters, namely, the values of the volatility persistence parameters in the diagonal BEKK-MGARCH models decrease when we include the structural break dummies in the models. (2) Second, we further find that interestingly, during the Lehman crisis in 2008, the estimated time-varying volatilities from the diagonal BEKK-MGARCH model with structural break dummies are slightly higher than those from the no structural break dummy model. (3) Third, we furthermore reveal that also very interestingly, the estimated time-varying correlations from the diagonal BEKK-MGARCH model with no structural break dummy are slightly higher than those from the structural break dummy model.

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Section: Recent Literature Reviewmentioning

confidence: 99%

A Multivariate Analysis of the Effects of Structural Breaks on Stock Return Volatility Persistence: The Case of the US and Japan

Tsuji¹

2019

IJBA

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“…In an application of this test to Value-at-Risk forecasts (Berens et al, 2013) it is seen that this proposed test might indeed be useful in practical situations. That is, it might be a promising approach to combine the well-known CCC (constant conditional correlation) and DCC (dynamic conditional correlation) model with this test for structural breaks in correlations.…”

Section: Introductionmentioning

confidence: 97%

A Nonparametric Test for a Constant Correlation Matrix

Wied

2013

SSRN Journal

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We propose a nonparametric procedure to test for changes in correlation matrices at an unknown point in time. The new test requires constant expectations and variances, but only mild assumptions on the serial dependence structure and has considerable power in finite samples. We derive the asymptotic distribution under the null hypothesis of no change as well as local power results and apply the test to stock returns.

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“…While our focus here is on a specific application, the problem has wide applicability. For example, Stoehr et al (2020) examine changes in the covariance structure of functional Magnetic Resonance Imaging (fMRI) data, where a failure to satisfy stationarity assumptions can significantly contaminate any analysis, while Wied et al (2013) and Berens et al (2015) examine how changes in the covariance of financial data can be used to improve stock portfolio optimisation.…”

Section: Introductionmentioning

confidence: 99%

Detecting changes in covariance via random matrix theory

Ryan¹,

Killick²

2021

Preprint

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A novel method is proposed for detecting changes in the covariance structure of moderate dimensional time series. This non-linear test statistic has a number of useful properties. Most importantly, it is independent of the underlying structure of the covariance matrix. We discuss how results from Random Matrix Theory, can be used to study the behaviour of our test statistic in a moderate dimensional setting (i.e. the number of variables is comparable to the length of the data). In particular, we demonstrate that the test statistic converges point wise to a normal distribution under the null hypothesis. We evaluate the performance of the proposed approach on a range of simulated datasets and find that it outperforms a range of alternative recently proposed methods. Finally, we use our approach to study changes in the amount of water on the surface of a plot of soil which feeds into model development for degradation of surface piping.

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Testing for Structural Breaks in Correlations: Does it Improve Value-at-Risk Forecasting?

Cited by 6 publications

References 29 publications

A Multivariate Analysis of the Effects of Structural Breaks on Stock Return Volatility Persistence: The Case of the US and Japan

A Multivariate Analysis of the Effects of Structural Breaks on Stock Return Volatility Persistence: The Case of the US and Japan

A Nonparametric Test for a Constant Correlation Matrix

Detecting changes in covariance via random matrix theory

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