Transitions in the stock markets of the US, UK and Germany

Raddant, Matthias; Wagner, Friedrich

doi:10.1080/14697688.2016.1183812

Cited by 10 publications

(6 citation statements)

References 30 publications

(27 reference statements)

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“…These changes can be analyzed in more detail. In the analysis of Raddant and Wagner (2017) of the US, the UK, and the German stock market the same change has been found in the behavior based on the stock's beta values, derived under the assumption that the covariance matrix of the returns has one large eigenvalue already at a time window sizes of 3 years. In the years 1994-2006 stocks with high trading volume and high beta mainly came from the information technology sector, whereas in 2006-2012 such stocks came mainly from the financial sector.…”

Section: Market Transitionmentioning

confidence: 52%

“…Section 5 contains two applications. By using our β(t) we verify a transitions in the market observed by Raddant and Wagner (2017). As a second application we investigate the correlation of the leverage effect with β.…”

Section: Introductionmentioning

confidence: 82%

“…Figure11: Time dependence of the risk parameter described in equation (27) for the sectors of the S&P market. The dashed line shows the result ofRaddant and Wagner (2017). The solid lines (blue for the information technology and red for the financial sector) use daily βs from the RMG.…”

mentioning

confidence: 99%

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Multivariate Garch with dynamic beta

Raddant,

Wagner

2016

Preprint

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We investigate a solution for the problems related to the application of multivariate Garch models to markets with a large number of stocks by restricting the form of the conditional covariance matrix. The model is based on a factor model and uses only six free Garch parameters. One factor can be interpreted as the market component. The remaining factors are equal and allow the analytical calculation of the inverse covariance matrix. The time-dependence of the factors enables the determination of dynamical beta coefficients. We compare the results from our model with the results of other Garch models for the daily returns from the S&P500 market and find that they are competitive. As applications we use the daily values of beta coefficients to confirm a transition of the market in 2006. Furthermore we discuss the relationship of our model with the leverage effect.

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Section: Market Transitionmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 82%

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Multivariate Garch with dynamic beta

Raddant,

Wagner

2016

Preprint

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“…I. A fundamental change in the standard correlation matrices after the dot-com bubble burst period [36,46,70,71] is visible in Fig. 2.…”

Section: A Dynamics Of the Average Sector Collectivitymentioning

confidence: 93%

New Collectivity Measures for Financial Covariances and Correlations

Heckens,

Guhr

2022

Preprint

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Complex systems are usually non-stationary and their dynamics is often dominated by collective effects. Collectivity, defined as coherent motion of the whole system or of some of its parts, manifests itself in the time-dependent structures of covariance and correlation matrices. The largest eigenvalue corresponds to the collective motion of the system as a whole, while the other large, isolated, eigenvalues indicate collectivity in parts of the system. In the case of finance, these are industrial sectors. By removing the collective motion of the system as a whole, the latter effects are much better revealed. We measure a remaining collectivity to which we refer as average sector collectivity. We identify collective signals around the Lehman Brothers crash and after the dot-com bubble burst. For the Lehman Brother crash, we find a potential precursor. We analyze 213 US stocks over a period of more than 30 years from 1990 to 2021. We plot the average sector collectivity versus the collectivity corresponding to the largest eigenvalue to study the whole market trajectory in a two dimensional space spanned by both collectivities. Therefore, we capture the average sector collectivity in a much more precise way. Additionally, we observe that larger values in the average sector collectivity are often accompanied by trend shifts in the mean covariances and mean correlations. As of 2015/2016 the collectivity in the US stock markets changed fundamentally.

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“…Indeed, stock market indexes are highly integrated among European countries as suggested by Breinlich et al [4]; Raddant [1]; Raddant and Wagner [5]; this means a more volatile FTSE can lead to more volatility in other European stock market indices. To find out what are the outcomes of Brexit for stock markets in EU, this study implements econometric models to measure the effects of Brexit on the volatility of CAC, and DAX, and FTSE.…”

Section: Introductionmentioning

confidence: 99%

Post-Brexit Stock Market Volatility and European Central Bank Reaction Function

Anaraki¹

2018

JFE

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The European stock markets experienced enormous volatility in stock markets post-Brexit era, which wasn't observed before the shock. The volatility in financial markets has disturbed economic fundamentals including capital flow and economic growth in several European countries. To avoid such disturbances the monetary authorities have to implement a monetary reaction function by adopting an appropriate range for short-term interest rate consistent with the volatility of stock markets and depreciation of British pound. The monetary reaction function in this case shouldn't be only based on traditional Taylor's rule but also needs to take into account the volatility of stock markets and depreciation of pound versus major currencies. The goal of this paper is twofold. First, it tries to estimate the effects of Brexit on stock market volatility index for British FTSE, German DAX, and French CAC using econometric models and a dummy variable for post-Brexit. Second, it uses a Vector Auto Regression (VAR) model to stimulate the monetary reaction function of the European Central Bank (ECB) to find out how it may neutralize the adverse effects of stock market volatility on macroeconomic fundamentals. Section 4 discusses the estimated econometric results, and finally section 5 provides findings and conclusions.

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Transitions in the stock markets of the US, UK and Germany

Cited by 10 publications

References 30 publications

Multivariate Garch with dynamic beta

Multivariate Garch with dynamic beta

New Collectivity Measures for Financial Covariances and Correlations

Post-Brexit Stock Market Volatility and European Central Bank Reaction Function

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