4,369 research outputs found
Crises and collective socio-economic phenomena: simple models and challenges
Financial and economic history is strewn with bubbles and crashes, booms and
busts, crises and upheavals of all sorts. Understanding the origin of these
events is arguably one of the most important problems in economic theory. In
this paper, we review recent efforts to include heterogeneities and
interactions in models of decision. We argue that the Random Field Ising model
(RFIM) indeed provides a unifying framework to account for many collective
socio-economic phenomena that lead to sudden ruptures and crises. We discuss
different models that can capture potentially destabilising self-referential
feedback loops, induced either by herding, i.e. reference to peers, or
trending, i.e. reference to the past, and account for some of the phenomenology
missing in the standard models. We discuss some empirically testable
predictions of these models, for example robust signatures of RFIM-like herding
effects, or the logarithmic decay of spatial correlations of voting patterns.
One of the most striking result, inspired by statistical physics methods, is
that Adam Smith's invisible hand can badly fail at solving simple coordination
problems. We also insist on the issue of time-scales, that can be extremely
long in some cases, and prevent socially optimal equilibria to be reached. As a
theoretical challenge, the study of so-called "detailed-balance" violating
decision rules is needed to decide whether conclusions based on current models
(that all assume detailed-balance) are indeed robust and generic.Comment: Review paper accepted for a special issue of J Stat Phys; several
minor improvements along reviewers' comment
Hysteresis and economics - taking the economic past into account
The goal of this article is to discuss the rationale underlying the application of hysteresis to economic models. In particular, we explain why many aspects of real economic systems are hysteretic is plausible. The aim is to be explicit about the difficulties encountered when trying to incorporate hysteretic effects into models that can be validated and then used as possible tools for macroeconomic control. The growing appreciation of the ways that memory effects influence the functioning of economic systems is a significant advance in economic thought and, by removing distortions that result from oversimplifying specifications of input-output relations in economics, has the potential to narrow the gap between economic modeling and economic reality
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
Why a simple herding model may generate the stylized facts of daily returns: Explanation and estimation
The paper proposes an elementary agent-based asset pricing model that, invoking the two trader types of fundamentalists and chartists, comprises four features: (i) price determination by excess demand; (ii) a herding mechanism that gives rise to a macroscopic adjustment equation for the market fractions of the two groups; (iii) a rush towards fundamentalism when the price misalignment becomes too large; and (iv) a stronger noise component in the demand per chartist trader than in the demand per fundamentalist trader, which implies a structural stochastic volatility in the returns. Combining analytical and numerical methods, the interaction between these elements is studied in the phase plane of the price and a majority index. In addition, the model is estimated by the method of simulated moments, where the choice of the moments reflects the basic stylized facts of the daily returns of a stock market index. A (parametric) bootstrap procedure serves to set up an econometric test to evaluate the model's goodness-of-fit, which proves to be highly satisfactory. The bootstrap also makes sure that the estimated structural parameters are well identified. --structural stochastic volatility,method of simulated moments,autocorrelation pattern,fat tails,bootstrapped p-values
Turnover, account value and diversification of real traders: evidence of collective portfolio optimizing behavior
Despite the availability of very detailed data on financial market,
agent-based modeling is hindered by the lack of information about real trader
behavior. This makes it impossible to validate agent-based models, which are
thus reverse-engineering attempts. This work is a contribution to the building
of a set of stylized facts about the traders themselves. Using the client
database of Swissquote Bank SA, the largest on-line Swiss broker, we find
empirical relationships between turnover, account values and the number of
assets in which a trader is invested. A theory based on simple mean-variance
portfolio optimization that crucially includes variable transaction costs is
able to reproduce faithfully the observed behaviors. We finally argue that our
results bring into light the collective ability of a population to construct a
mean-variance portfolio that takes into account the structure of transaction
costsComment: 26 pages, 9 figures, Fig. 8 fixe
Critical Market Crashes
This review is a partial synthesis of the book ``Why stock market crash''
(Princeton University Press, January 2003), which presents a general theory of
financial crashes and of stock market instabilities that his co-workers and the
author have developed over the past seven years. The study of the frequency
distribution of drawdowns, or runs of successive losses shows that large
financial crashes are ``outliers'': they form a class of their own as can be
seen from their statistical signatures. If large financial crashes are
``outliers'', they are special and thus require a special explanation, a
specific model, a theory of their own. In addition, their special properties
may perhaps be used for their prediction. The main mechanisms leading to
positive feedbacks, i.e., self-reinforcement, such as imitative behavior and
herding between investors are reviewed with many references provided to the
relevant literature outside the confine of Physics. Positive feedbacks provide
the fuel for the development of speculative bubbles, preparing the instability
for a major crash. We demonstrate several detailed mathematical models of
speculative bubbles and crashes. The most important message is the discovery of
robust and universal signatures of the approach to crashes. These precursory
patterns have been documented for essentially all crashes on developed as well
as emergent stock markets, on currency markets, on company stocks, and so on.
The concept of an ``anti-bubble'' is also summarized, with two forward
predictions on the Japanese stock market starting in 1999 and on the USA stock
market still running. We conclude by presenting our view of the organization of
financial markets.Comment: Latex 89 pages and 38 figures, in press in Physics Report
Stochastic resonance in the recovery of signal from agent price expectations
Contributions that noise can make to the objective of detecting signal in agent expectations for price in financial markets are examined. Although contrary to most assumptions on exogenous noise in financial markets as increasing both risk and uncertainty in the detection of signal, a basis for the contribution that noise can have to agent objectives in signal detection through stochastic resonance (SR) is well-documented across disciplines. After reviewing foundations for the micro-processing of expectations, a multi-component model of networked agents that includes a component of bounded rational processing and a component that has been cited as generating “herding” behavior in financial markets is offered. The signal-to-noise ratios in the proposed models provide a basis to investigate SR in an application to financial markets. Results with both deterministic and stochastic forms of the proposed model support SR as a process in which randomness can contribute to the recovery of signal in agent expectation. Additionally, predictive models that indicate the sensitivity of the occurrence of SR to the parameters of the models of agent expectations were estimated and cross-validated. The discriminative ability of the models is reported through Area Under the Receiver Operating Curve (AUROC) methodology. These results extend the cross-discipline demonstrations of SR to models of price in financial markets.</p
Complex network analysis and nonlinear dynamics
This chapter aims at reviewing complex network and nonlinear dynamical
models and methods that were either developed for or applied to socioeconomic
issues, and pertinent to the theme of New Economic Geography. After an introduction
to the foundations of the field of complex networks, the present summary
introduces some applications of complex networks to economics, finance, epidemic
spreading of innovations, and regional trade and developments. The chapter also
reviews results involving applications of complex networks to other relevant
socioeconomic issue
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