Matthias Raddant

Evolvement of uniformity and volatility in the stressed global financial village

Background In the current era of strong worldwide market couplings the global financial village became highly prone to systemic collapses, events that can rapidly sweep throughout the entire village. Methodology/Principal Findings We present a new methodology to assess and quantify inter-market relations. The approach is based on the correlations between the market index, the index volatility, the market Index Cohesive Force and the meta-correlations (correlations between the intra-correlations.) We investigated the relations between six important world markets—U.S., U.K., Germany, Japan, China and India—from January 2000 until December 2010. We found that while the developed “western” markets (U.S., U.K., Germany) are highly correlated, the interdependencies between these markets and the developing “eastern” markets (India and China) are volatile and with noticeable maxima at times of global world events. The Japanese market switches “identity”—it switches between periods of high meta-correlations with the “western” markets and periods when it behaves more similarly to the “eastern” markets. Conclusions/Significance The methodological framework presented here provides a way to quantify the evolvement of interdependencies in the global market, evaluate a world financial network and quantify changes in the world inter market relations. Such changes can be used as precursors to the agitation of the global financial village. Hence, the new approach can help to develop a sensitive “financial seismograph” to detect early signs of global financial crises so they can be treated before they develop into worldwide events.

CORRELATIONS AND DEPENDENCIES IN THE GLOBAL FINANCIAL VILLAGE

The high degree of coupling between global financial markets has made the financial village prone to systemic collapses. Here we present a new methodology to assess and quantify inter-market relations. The approach is based on meta-correlations (correlations between the intra-market correlations), and a Dependency Network analysis approach. We investigated the relations between six important world markets — U.S., U.K., Germany, Japan, China and India from January 2000 until December 2010. Our findings show that while the developed Western markets (U.S., U.K., Germany), are highly correlated, the inter-dependencies between these markets and the Eastern markets (India and China) are very volatile and with noticeable maxima at times of global world events. Finally, using the Dependency network approach, we quantify the flow of information between the different markets, and how markets affect each other. We observe that German and U.K. stocks show a large amount of coupling, while other markets are more segmented. These and additional reported findings illustrate that this methodological framework provides a way to quantify interdependencies in the global market and their evolvement, to evaluate the world financial network, and quantify changes in inter-market relations. Such changes can be used as precursors to the agitation of the global financial village.

A note on institutional hierarchy and volatility in financial markets

From a statistical point of view, the prevalence of non-Gaussian distributions in financial returns and their volatilities shows that the Central Limit Theorem (CLT) often does not apply in financial markets. In this article, we take the position that the independence assumption of the CLT is violated by herding tendencies among market participants, and investigate whether a generic probabilistic herding model can reproduce non-Gaussian statistics in systems with a large number of agents. It is well known that the presence of a herding mechanism in the model is not sufficient for non-Gaussian properties, which crucially depend on the details of the communication network among agents. The main contribution of this article is to show that certain hierarchical networks, which portray the institutional structure of fund investment, warrant non-Gaussian properties for any system size and even lead to an increase in system-wide volatility. Viewed from this perspective, the mere existence of financial institutions with socially interacting managers contributes considerably to financial volatility.

Cascades in Real Interbank Markets

We analyze cascades of defaults in an interbank loan market. The novel feature of this study is that the network structure and the size distribution of banks are derived from empirical data. We find that the ability of a defaulted institution to start a cascade depends on an interplay of shock size and connectivity. Further results indicate that the interbank loan network is structurally less stable after the financial crisis than it was before. To evaluate the influence of the network structure on market stability, we compare simulated cascades from the empirical network with results from different network models. The results show that the empirical network has non-random features, which cannot be captured by randomized networks. The analysis also reveals that simulations that assume homogeneity for banks and loan size tend to overestimate the fragility of the interbank market.

Phase transition in the S&P stock market

We analyze the returns of stocks contained in the Standard & Poor’s 500 index from 1987 until 2011. We use covariance matrices of the firms’ returns determined in a time windows of several years. We find that the eigenvector belonging to the leading eigenvalue (the market) exhibits a phase transition. The market is in an ordered state from 1995 to 2005 and in a disordered state after 2005. We can relate this transition to an order parameter derived from the stocks’ beta and the trading volume. This order parameter can also be interpreted within an agent-based model.

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

In an analysis of the US, the UK and German stock market, we find a change in the behaviour based on the stocks’ beta values. In the years 1995–2006, trades of stocks with high beta and large volume were concentrated in the IT and technology sector, whereas in 2006–2012 those trades are dominated by stocks from the financial sector. We show that an agent-based model can reproduce such a transition. We further show that the initial impulse for the transition might stem from the increase of high-frequency trading at that time.

Interconnectedness in the Global Financial Market

The global financial system is highly complex, with cross-border interconnections and interdependencies. In this highly interconnected environment, local financial shocks and events can be easily amplified and turned into global events. This paper analyzes the dependencies among nearly 4,000 stocks from 15 countries. The returns are normalized by the estimated volatility using a GARCH model and a robust regression process estimates pairwise statistical relationships between stocks from different markets. The estimation results are used as a measure of statistical interconnectedness, and to derive network representations, both by country and by sector. The results show that countries like the United States and Germany are in the core of the global stock market. The energy, materials, and financial sectors play an important role in connecting markets, and this role has increased over time for the energy and materials sectors. Our results confirm the role of global sectoral factors in stock market dependence. Moreover, our results show that the dependencies are rather volatile and that heterogeneity among stocks is a non-negligible aspect of this volatility.

Hierarchy in Germany's Corporate Network

We examine the bipartite graphs of German corporate boards in 1993, 1999 and 2005, where we observe the persistence of a core in the corporate network, in spite of substantial turnover among core directors and changes incorporate governance and in the tail distribution of multiple board membership. Our analysis suggests that core persistence originates from selective board appointment decisions.