Research

This is a list of publications in peer-reviewed journals. For other contributions like working papers, policy work, invited contributions and expert reports see my cv in the about section and the recent posts on the home page.

This is a review about financial dependencies which merges efforts in econophysics and financial economics during the last few years. We focus on the most relevant contributions to the analysis of asset markets’ dependencies, especially correlational studies, which in our opinion are beneficial for researchers in both fields. In econophysics, these dependencies can be modeled to describe financial markets as evolving complex networks. In particular, we show that a useful way to describe dependencies is by means of information filtering networks that are able to retrieve relevant and meaningful information in complex financial datasets. In financial economics these dependencies can describe asset comovement and spill-overs. In particular, several models are presented that show how network and factor model approaches are related to modeling of multivariate volatility and asset returns, respectively. Finally, we sketch out how these studies can inspire future research and how they contribute to support researchers in both fields to find a better and a stronger common language.

We investigate the appointments of female board members of Japanese corporations together with the corporations’ performance. We relate the presence of female board members to the board and ownership networks of the corresponding firms. We find that firms with female board members often show above average performance. We also find that corporate boards and the corresponding members show homophily with respect to gender in their networks. The observed homophily leads to interdependencies in the appointments of new board members. New appointments of female board members are more likely at firms with ties to other boards with female board members.

We analyze the ties between 4000 Japanese corporations in the time period from 2004 until 2013. We combine data about the board composition with ownership relationships and indicators of corporate profitability. The board network exhibits some clustering, which can partly be explained by ownership relations, and a tendency to form ties to other corporations from the same sector. Connectivity in the board network (corporate board interlocks) and ownership network (shareholdings) does have an influence profitability. Firms that are linked to peers with above average profitability are more profitable than firms in other relationships. Hence, network effects partly explain why board interlocks and ownership ties are not always beneficial.

In this review we discuss advances in the agent-based modeling of economic and social systems. We show the state of the art of the heuristic design of agents and how behavioral economics and laboratory experiments have improved the modeling of agent behavior. We further discuss how economic networks and social systems can be modeled and we discuss novel methodology and data sources. Lastly, we present an overview of estimation techniques to calibrate and validate agent-based models and show avenues for future research.

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 and by introducing a system of recursion formals. The model is based on a decomposition of the conditional covariance matrix into two components and requires only six parameters to be estimated. The first component can be interpreted as the market factor, all remaining components are assumed to be equal. This allow the analytical calculation of the inverse covariance matrix. The factors are dynamic and therefore enable to describe dynamic beta coefficients. We compare the estimated covariances for the S&P500 market with those of other GARCH models and find that they are competitive, despite the low number of parameters. 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.

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.

We examine the bipartite graphs of German corporate boards in 1993, 1999 and 2005, focusing on their projections onto directors (the “personal” network) and onto companies (the “institutional” network). The novel feature here is our focus on the temporal evolution of the two projections. The personal networks exhibit cores of highly central directors who are densely connected among themselves, while the institutional networks show a persistent core of large corporations whose identity remains mostly the same. This results in the persistent presence of a core network of very large corporations, despite substantial turnover in the identity of directors and significant changes in Germany’s corporate governance during the investigated period. Our findings strongly suggest that core persistence originates from the board appointment decisions of the very largest corporations and is largely independent of personal destinies.

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.

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.

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.

We analyze the Italian interbank loan market from 1999 until 2010. The analysis of net trade flows shows a high imbalance caused by a few large net borrowers in the market. The trading volume shows a significant drop starting in 2007, which accelerates with the Lehman default in late 2008. The interbank loan network is very dense. Hence, we try to identify strong links by looking for preferential lending relationships expressed by discounts in the loan rate. Furthermore, we estimate the dynamics of credit spreads for each bank and find that economically significant spreads for the overnight market developed only in 2010. The analysis of preferential loan relationships reveals that in the pre-Lehman era large net borrowers used to borrow at a slight discount. In the post-Lehman era borrowers with large net exposures paid more than the average market rate, which shows that the risk evaluation of market participants has changed considerably.

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.

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. 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. 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.

The high degree of coupling between global financial markets has made the financial village prone to systemic collapses. Our approach is based on meta-correlations (correlations between the intra-market correlations), and a Dependency Network analysis. 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, are highly correlated, the inter-dependencies between these markets and the Eastern markets 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.

The thesis applies methods from network sciences to four economic topics: herding in financial markets; corporate board networks; contagion in global financial markets; the Italian overnight loan market.
V, 144 Bl. : Ill., graph. Darst., Kt. ; 30 cm