We report on experimental evidence rationalizing the use of heterogeneous agent models. We provide compelling evidence that subjects in laboratory experiments often behave in ways that depart from the rational choice ideal. Further, these subjects' heuristic approaches often differ from one another in distinct, classifiable ways. It follows that models of heterogeneous, boundedly rational agents can often deliver predictions that are a better fit to the experimental data at both the micro- and the macro-levels of analysis than can rational-choice, single-actor models. Our focus in this chapter is on experimental studies developed to address questions in macroeconomics and finance.
This chapter surveys work dedicated to macroeconomic analysis using an agent-based modeling approach. After a short review of the origins and general characteristics of this approach a systemic comparison of the structure and modeling assumptions of a set of important (families of) agent-based macroeconomic models is provided. The comparison highlights substantial similarities between the different models, thereby identifying what could be considered an emerging common core of macroeconomic agent-based modeling. In the second part of the chapter agent-based macroeconomic research in different domains of economic policy is reviewed.
This chapter surveys the state-of-art of heterogeneous agent models (HAMs) in finance using a jointly theoretical and empirical analysis, combined with numerical and Monte Carlo analysis from the latest development in computational finance. It provides supporting evidence on the explanatory power of HAMs to various stylized facts and market anomalies through model calibration, estimation, and economic mechanisms analysis. It presents a unified framework in continuous time to study the impact of historical price information on price dynamics, profitability and optimality of fundamental and momentum trading. It demonstrates how HAMs can help to understand stock price co-movements and to build evolutionary CAPM. It also introduces a new HAMs perspective on house price dynamics and an integrate approach to study dynamics of limit order markets. The survey provides further insights into the complexity and efficiency of financial markets and policy implications.
This chapter surveys the state-of-art of heterogeneous agent models (HAMs) in finance using a jointly theoretical and empirical analysis, combined with numerical analysis from the latest development in computational finance. It provides supporting evidence on the explanatory power of HAMs to various stylized facts and market anomalies through model calibration, estimation, and economic mechanisms analysis. It presents HAMs with the mainstream finance a unified framework in continuous time to study the impact of historical price information on price dynamics, profitability and optimality of fundamental and momentum trading. It demonstrates how HAMs can help to understand stock price co-movements and evolutionary CAPM. It also introduces a new HAMs perspective on house price dynamics and an integrate approach to study dynamics of limit order markets. The survey provides further insights into the complexity and efficiency of financial markets and policy implications.
The recent global financial crisis has triggered a huge interest in the use of network concepts and network tools to better understand how instabilities can propagate through the financial system. The literature is today quite vast, covering both theoretical and empirical aspects. This review concentrates on empirical work, and associated methodologies, concerned with the evaluation of the fragility and resilience of financial and credit markets. The first part of the review examines the literature on systemic risk that arise from banks mutual exposures. These exposures stem primarily from interbank lending and derivative positions, but also, indirectly, from common holdings of other asset classes, that can lead to common shocks in instances of fire sales, and from widespread non-performing loans to the real sector during period of economic downturns. We survey (a) studies that characterize the structure of national interbank networks, in some cases using a multiplex representations, (b) studies that introduce novel methods to quantify systemic risk and identify systemically important institutions, such as via stress test scenarios, (c) studies that assess which regulatory measures can help mitigate the propagation of contagion and distress in the financial system, and (d) studies that explore which location advantages may arise from holding privileged positions in the interbank network, such as via preferential lending relationships, or because of occupying a more central node, and if such advantages can provide an early indication of the build up of systemic risk. The second part of the review is dedicated to the analysis of indirect networks, specifically (e) proximity based network, i.e. networks obtained starting from a proximity measure sometime filtered with a network filtering methodology, (f) association network, i.e. networks where a link between two financial actors is set if a statistical test again a null hypothesis is rejected, and (g) statistically validated networks, i.e. event or relationship networks where a subset of links is selected according to a statistical validation associated with the rejection of a random null hypothesis. The need for a joint consideration of direct and indirect channels of contagion is briefly discussed.
This chapter surveys heterogeneous agent models with rational expectations that deliver a finite number of heterogeneous agents as an equilibrium outcomes. Instead of having a distribution with infinite support to follow, this class of models endogenously generates a finite number of agents as an equilibrium outcome. As a consequence, many of the additional tools and techniques developed in the DSGE literature with a representative agent can easily be imported in this class of models, allowing these models to be brought to the data with advanced econometric techniques. No-trade, small-heterogeneity and truncation methods are presented. The derivation of optimal policies is presented in these environments. Finally, the chapter discusses the relation with other heterogeneous agent models that don't rely on rational expectations, namely agent-based models.
Financial markets display a host of universal "stylized facts" begging for a scientific explanation: Excess volatility, fat tails, and clustered activity are well known and have been studied for many years. More microstructural stylized facts have recently emerged, for example the long memory of the order flow or the square-root dependence of impact on the volume of metaorders. Agent-based models are attempts to account for these stylized facts in a unified manner. Devising faithful microstructural ABMs would allow one to answer crucial questions, such as those related to market stability. Can large orders destabilize markets? Is HFT activity detrimental? Can destabilizing feedback loops be mitigated by adequate regulation? The present review paper summarizes recent work in that direction. We discuss in particular the Santa-Fe zero-intelligence model, which provides a very interesting benchmark, but suffers from important drawbacks as well, such as strong mean-reversion effects. One can enrich the Santa-Fe model as to reproduce both diffusive prices, and the square-root impact law. The underlying mechanism can be well understood in terms of a generic "reaction–diffusion" model for the dynamics of the liquidity, which can be solved analytically. Finally, we argue that the role of "information" is probably overstated in classical theories, while a picture based on a self-reflexive price-impacting order flow has many merits. The recent accumulation of microstructural stylized facts, allowing one to focus on the price formation mechanism, all but confirm that fundamental information plays a relatively minor role in the dynamics of financial markets, at least on short to medium time scales.
This chapter surveys work dedicated to macroeconomic analysis using an agent-based modeling approach. After a short review of the origins and general characteristics of this approach a systemic comparison of the structure and modeling assumptions of a set of important (families of) agent-based macroeconomic models is provided. The comparison highlights substantial similarities between the different models, thereby identifying what could be considered an emerging common core of macroeconomic agent-based modeling. In the second part of the chapter agent-based macroeconomic research in different domains of economic policy is reviewed.
This paper reviews the literature on heterogeneous agent models of financial stability and their application in stress tests. We open with the observation that the financial system is a complex system, which heterogeneous agent models are well-suited to analyze. The paper then proceeds in two parts. In the first part, we discuss the fundamental drivers of systemic risk in financial systems, and set out how our understanding of them can be informed by heterogeneous agent models. We focus on models of systemic risk resulting from leverage constraints and models of financial contagion due to interconnectedness. In the second part of this review, we discuss how the conceptual insights from leverage and contagion models can be combined to model and understand systemic risk more broadly and to build robust and data-driven stress tests.
This paper addresses the methodological problems of empirical validation in agent-based (AB) models in economics and how these are currently being tackled. We first identify a set of issues that are common to all modelers engaged in empirical validation. We then propose a novel taxonomy, which captures the relevant dimensions along which AB economics models differ. We argue that these dimensions affect the way in which empirical validation is carried out by AB modelers and we critically discuss the main alternative approaches to empirical validation being developed in AB economics. We conclude by focusing on a set of (as yet) unresolved issues for empirical validation that require future research.
