Simone Alfarano

A NOISE TRADER MODEL AS A GENERATOR OF APPARENT FINANCIAL POWER LAWS AND LONG MEMORY

In various agent-based models the stylized facts of financial markets (unit-roots, fat tails and volatility clustering) have been shown to emerge from the interactions of agents. However, the complexity of these models often limits their analytical accessibility. In this paper we show that even a very simple model of a financial market with heterogeneous interacting agents is capable of reproducing these ubiquitous statistical properties. The simplicity of our approach permits to derive some analytical insights using concepts from statistical mechanics. In our model, traders are divided into two groups: fundamentalists and chartists, and their interactions are based on a variant of the herding mechanism introduced by Kirman [1993]. The statistical analysis of simulated data points toward long-term dependence in the auto-correlations of squared and absolute returns and hyperbolic decay in the tail of the distribution of raw returns, both with estimated decay parameters in the same range like those of empirical data. Theoretical analysis, however, excludes the possibility of ‘true’ scaling behavior because of the Markovian nature of the underlying process and the boundedness of returns. The model, therefore, only mimics power law behavior. Similarly as with the phenomenological volatility models analyzed in LeBaron [2001], the usual statistical tests are not able to distinguish between true or pseudo-scaling laws in the dynamics of our artificial market

The small core of the German corporate board network

We consider the bipartite graph of German corporate boards and identify a small core of directors who are highly central in the entire network while being densely connected among themselves. To identify the core, we compare the actual number of board memberships to a random benchmark, focusing on deviations from the benchmark that span several orders of magnitude. The board appointment decisions of largely capitalized companies appear to be the driving force behind the existence of a core in Germany’s corporate network.

A Minimal Noise Trader Model with Realistic Time Series Properties

Simulations of agent-based models have shown that the stylized facts (unit-root, fat tails and volatility clustering) of financial markets have a possible explanation in the interactions among agents. However, the complexity, originating from the presence of non-linearity and interactions, often limits the analytical approach to the dynamics of these models. In this paper we show that even a very simple model of a financial market with heterogeneous interacting agents is capable of reproducing realistic statistical properties of returns, in close quantitative accordance with the empirical analysis. The simplicity of the system also permits some analytical insights using concepts from statistical mechanics and physics. In our model, the traders are divided into two groups : fundamentalists and chartists, and their interactions are based on a variant of the herding mechanism introduced by Kirman [22]. The statistical analysis of our simulated data shows long-term dependence in the auto-correlations of squared and absolute returns and hyperbolic decay in the tail of the distribution of the raw returns, both with estimated decay parameters in the same range like empirical data. Theoretical analysis, however, excludes the possibility of ’true’ scaling behavior because of the Markovian nature of the underlying process and the finite set of possible realized returns. The model, therefore, only mimics power law behavior. Similarly as with the phenomenological volatility models analyzed in LeBaron [25], the usual statistical tests are not able to distinguish between true or pseudo-scaling laws in the dynamics of our artificial market.

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.

Identification of Interaction Effects in Survey Expectations: A Cautionary Note

Abstract A growing body of literature reports evidence of social interaction effects in survey expectations. In this note, we argue that evidence in favor of social interaction effects should be treated with caution, or could even be spurious. Utilizing a parsimonious stochastic model of expectation formation and dynamics, we show that the existing sample sizes of survey expectations are about two orders of magnitude too small to reasonably distinguish between noise and interaction effects. Moreover, we argue that the problem is compounded by the fact that highly correlated responses among agents might not be caused by interaction effects at all, but instead by model-consistent beliefs. Ultimately, these results suggest that existing survey data cannot facilitate our understanding of the process of expectations formation.

A nonparametric approach tothe noise density in stochastic volatility models

We propose a nonparametric method to determine the functional form of the noise density in discrete-time stochastic volatility models of financial returns. Our approach suggests that the assumption of Gaussian noise is often adequate, but we do observe deviations from Gaussian noise for some assets, for instance gold.

The small core of the German corporate board network: New evidence from 2010

Milakovic, Alfarano and Lux (2010) have identified a small core of directors who are both highly central to the entire network of German corporate boards as well as closely connected among themselves. While their analysis has been based on data for the management and supervisory boards of a sample of 287 publicly traded companies with high market capitalization as of May 2008, a subsequent study by Milakovic, Raddant and Birg (2010) using somewhat smaller samples from the years 1993, 1999, and 2005 has confirmed that this closely connected core is a persistent stylized fact for the German corporate sector. In this note, we provide an update of our previous results using the composition of management and supervisory boards as of December, 2010. Again, almost all qualitative properties of previous samples are confirmed despite considerable turnover within the group of persons constituting the network core.

Long-run expectations in a learning-to-forecast experiment

ABSTRACT We conduct a Learning to Forecast Experiment using a novel setting in which we elicit subjects’ short- and long-run expectations on the future price of an asset. We find that: (i) the rational expectations equilibrium is not a meaningful description for the whole time spectrum of subjects’ expectations; (ii) they are, instead, better described by an anchor-and-adjustment learning scheme; (iii) subjects exhibit a higher degree of heterogeneity in their long-run expectations vis-à-vis short-run expectations.

A spectral perspective on excess volatility

We perform a careful spectral analysis of the correlation structures observed in real and financial returns for a large pool of long-lived US corporations and find that financial returns are characterized by strong collective fluctuations that are absent from real returns. Once the excessive comovement is subtracted from individual financial time series, the behaviour of real and financial returns is virtually identical in both the cross-sectional and time series domains, thereby demonstrating the inherently collective nature of excessive fluctuations. Put differently, if excess volatility is to be reduced, then one would do well to inhibit excess comovement first. At any rate, the excessive behaviour in volatility and comovement should no longer be studied in isolation of each other.

Der dichte Kern des Netzwerks deutscher Aufsichtsräte und Unternehmensvorstände@@@The Small Core of the German Corporate Board Network: The 2010 Update: Verflechtungsstrukturen im Jahr 2010

ZusammenfassungFür die sogenannte Deutschland AG war eine enge Verflechtung von Personen in Aufsichtsräten und Vorständen typisch. Diese Strukturen haben sich über die Zeit kaum geändert. Dies zeigen die Autoren mit Hilfe der Netzwerkanalyse — einer Methode, die aus den Naturwissenschaften kommend immer häufiger auch für ökonomische Zusammenhänge genutzt wird.AbstractIn a previous study the authors have identified a small core of directors who are both highly central to the entire network of German corporate boards as well as closely connected among themselves. In this note the authors provide an update of their previous results using the composition of management and supervisory boards as of December 2010. Almost all qualitative properties of previous samples are confirmed despite considerable turnover within the group of people constituting the network core.