Identification of Interaction Effects in Survey Expectations: A Cautionary Note

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 eects 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.Survey expectations; model-consistent beliefs; social inter- action; networks.

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 --Herd Behavior,Speculative Dynamics,Fat Tails,Volatility Clustering

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. --Herd Behavior , Speculative Dynamics , Fat Tails , Volatility Clustering

Extreme Value Theory as a Theoretical Background for Power Law Behavior

Power law behavior has been recognized to be a pervasive feature of many phenomena in natural and social sciences. While immense research efforts have been devoted to the analysis of behavioral mechanisms responsible for the ubiquity of power-law scaling, the strong theoretical foundation of power laws as a very general type of limiting behavior of large realizations of stochastic processes is less well known. In this chapter, we briefly present some of the key results of extreme value theory, which provide a statistical justification for the emergence of power laws as limiting behavior for extreme fluctuations. The remarkable generality of the theory allows to abstract from the details of the system under investigation, and therefore allows its application in many diverse fields. Moreover, this theory offers new powerful techniques for the estimation of the Pareto index, detailed in the second part of this chapter.Extreme Value Theory; Power Laws; Tail index

Time-variation of higher moments in a financial market with heterogeneous agents: An analytical approach

A growing body of recent literature allows for heterogenous trading strategies and limited rationality of agents in behavioral models of financial markets. More and more, this literature has been concerned with the explanation of some of the stylized facts of financial markets. It now seems that some previously mysterious time-series characteristics like fat tails of returns and temporal dependence of volatility can be observed in many of these models as macroscopic patterns resulting from the assumed interaction of speculative traders. However, most of the available evidence stems from simulation studies of relatively complicated models which do not allow for analytical solutions. In this paper, this line of research is supplemented by analytical solutions of a simple variant of the seminal herding model introduced by Kirman (1993). Embedding the herding framework into a simple equilibrium asset pricing framework, we are able to derive closed-form solutions for the time-variation of higher moments as well as related quantities of interest enabling us to spell out under what circumstances the model gives rise to realistic behavior of the resulting time series. --

A Note on institutional hierarchy and volatility in financial markets

From a statistical point of view, the prevalence of non-Gaussian distributions in nancial returns and their volatilities shows that the Central Limit Theorem (CLT) often does not apply in nancial markets. In this paper 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 sucient for non-Gaussian properties, which crucially depend on the details of the communication network among agents. The main contribution of this paper 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 nancial institutions with socially interacting managers contributes considerably to nancial volatility.Herding; financial volatility; networks; core-perifery

Time-variation of higher moments in a financial market with heterogeneous agents: An analytical approach

A growing body of recent literature allows for heterogenous trading strategies and limited rationality of agents in behavioral models of financial markets. More and more, this literature has been concerned with the explanation of some of the stylized facts of financial markets. It now seems that some previously mysterious time-series characteristics like fat tails of returns and temporal dependence of volatility can be observed in many of these models as macroscopic patterns resulting from the interaction among different groups of speculative traders. However, most of the available evidence stems from simulation studies of relatively complicated models which do not allow for analytical solutions. In this paper, this line of research is supplemented by analytical solutions of a simple variant of the seminal herding model introduced by Kirman [1993]. Embedding the herding framework into a simple equilibrium asset pricing model, we are able to derive closed-form solutions for the time-variation of higher moments as well as related quantities of interest enabling us to spell out under what circumstances the model gives rise to realistic behavior of the resulting time series --

Estimation of a simple genetic algorithm applied to a laboratory experiment

The aim of our contribution relies on studying the possibility of implementing a genetic algorithm in order to reproduce some characteristics of a simple laboratory experiment with human subjects. The novelty of our paper regards the estimation of the key-parameters of the algorithm, and the analysis of the characteristics of the estimator.Estimation, genetic algoritms, experimenst

Excess Volatility and Herding in an Artificial Financial Market: Analytical Approach and Estimation

Several agent-based models have been proposed in the economic literature to explain the key stylized facts of financial data: heteroscedasticity, fat tails of returns and long-range dependence of volatility. Agentbased models view these empirical regularities as emerging properties of interacting groups of boundedly rational agents in financial markets. The complexity of these interacting agent models has largely constrained their analytical treatment, limiting their analysis mainly to Monte Carlo simulations. In order to overcome this limitation, we introduce a ‘minimalist’ model of an artificial financial market, along the lines of our previous contributions, based on herding behavior among two types of traders. The simplicity of the model allows for an almost complete analytical characterization of both conditional and unconditional statistical properties of prices and returns. Moreover, the underlying parameters of the model can be estimated directly, which permits an assessment of its goodness-of-fit for empirical data. While the performance of the model for domestic stock markets has been the focus of a previous contribution, in this paper we report results for selected exchange rates against the US dollar.Herd Behavior; Speculative Dynamics; Fat Tails; Volatility Clustering.

A simple asymmetric herding model to distinguish between stock and foreign exchange markets

Drawing on previous work of one of the authors, the paper takes an asymmetric variant of Kirman’s ant model and combines it with an elementary asset pricing mechanism. The closed-form solution of the equilibrium probability distribution allows the specification of a tractable likelihood function for daily returns, which is then employed to estimate the model’s behavioural parameters for a large pool of Japanese stocks. By way of Monte Carlo simulations it is found that most of these markets belong to the same class, which is characterized by a dominance of the stylized noise traders. In contrast, the model assigns a number of major foreign exchange markets to a different class, where on average the majority of agents follows the fundamentalist trading rule. Implications for the tail index are also worked out