Microeconomic Models for Long Memory in the Volatility of Financial Time Series

Kirman, Alan; Teyssière, Gilles

doi:10.1162/10811820160130260

Cited by 79 publications

(63 citation statements)

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“…On the contrary, in the presence of a large fraction of fundamentalists only small variations occur. This intermittent dynamics apparently gives rise to time-series properties capturing some of the stylized facts of empirical data The underlying behavioral mechanisms are similar to those of the more involved models of Kirman [19], Lux and Marchesi [25] and Wagner [32] from which our approach draws inspiration.…”

Section: Market Implementationmentioning

confidence: 70%

“…In the lower parts we compare the returns calculated from (18) and from the approximation (19). They behave qualitatively similar which can be taken as a justification of the approximation (19) 5 .…”

Section: Market Implementationmentioning

confidence: 76%

“…His formalization is symmetric with respect to switching probabilities generating a symmetric probability distribution of ants' visiting times at both mangers. In a long chain of subsequent papers this and similar approaches have been applied as models of herding and contagion phenomena in financial markets ( [17], [19], [25]). Costantini and Garibaldi [8] have derived the equilibrium distribution of the related discrete-time stochastic process within the more general theoretical framework of Polya urn processes.…”

Section: The Herding Mechanismmentioning

confidence: 99%

“…Note that our choice of a random development of the noise traders' mood ξ t (and derived from it, stochastic excess demand of this group) is inspired by the modelling of noise traders in the seminal paper by de Long et al [10]. Our noise traders, therefore, are characterized by random misperceptions rather than some heuristic trading rules such as trend following behavior used, for instance, in Kirman's adaptation of the ant model to foreign exchange trading [17,19].…”

Section: Market Implementationmentioning

confidence: 99%

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Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model

2005

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The behavioral origins of the stylized facts of financial returns have been addressed in a growing body of agent-based models of financial markets. While the traditional efficient market viewpoint explains all statistical properties of returns by similar features of the news arrival process, the more recent behavioral finance models explain them as imprints of universal patterns of interaction in these markets. In this paper we contribute to this literature by introducing a very simple agent-based model in which the ubiquitous stylized facts (fat tails, volatility clustering) are emergent properties of the interaction among traders. The simplicity of the model allows us to estimate the underlying parameters, since it is possible to derive a closed form solution for the distribution of returns. We show that the tail shape characterizing the fatness of the unconditional distribution of returns can be directly derived from some structural variables that govern the traders' interactions, namely the herding propensity and the autonomous switching tendency.

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Section: Market Implementationmentioning

confidence: 70%

Section: Market Implementationmentioning

confidence: 76%

Section: The Herding Mechanismmentioning

confidence: 99%

Section: Market Implementationmentioning

confidence: 99%

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Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model

2005

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“…Microeconomic models of financial markets rank in complexity from the simplest models, typically considering the interaction of two main types of agents-the fundamentalists and the chartists [18][19][20][21] either without market information or not caring about the fundamentals, thus creating white noise, while mean reversion effects [22] can be accounted due to the activity of fundamentalists. The first question to be raised is whether a microeconomic approach can be found based on insight about the mechanism of the formation of financial quantities.…”

Section: Introductionmentioning

confidence: 99%

Microeconomic co-evolution model for financial technical analysis signals

Rotundo

Ausloos²

2007

Physica A: Statistical Mechanics and its Applications

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Technical analysis (TA) has been used for a long time before the availability of more sophisticated instruments for financial forecasting in order to suggest decisions on the basis of the occurrence of data patterns. Many mathematical and statistical tools for quantitative analysis of financial markets have experienced a fast and wide growth and have the power for overcoming classical TA methods. This paper aims to give a measure of the reliability of some information used in TA by exploring the probability of their occurrence within a particular microeconomic agent-based model of markets, i.e., the co-evolution Bak-Sneppen model originally invented for describing species population evolutions. After having proved the practical interest of such a model in describing financial index so-called avalanches, in the prebursting bubble time rise, the attention focuses on the occurrence of trend line detection crossing of meaningful barriers, those that give rise to some usual TA strategies. The case of the NASDAQ crash of April 2000 serves as an illustration. r

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Bubbles and Crashes

Kaizoji

Sornette

2010

Encyclopedia of Quantitative Finance

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Episodes of market crashes have fascinated economists for centuries. Although many academics, practitioners and policy makers have studied questions related to collapsing asset price bubbles, there is little consensus yet about their causes and effects. This review and essay evaluates some of the hypotheses offered to explain the market crashes that often follow asset price bubbles. Starting from historical accounts and syntheses of past bubbles and crashes, we put the problem in perspective with respect to the development of the efficient market hypothesis. We then present the models based on heterogeneous agents and the limits to arbitrage that prevent rational agents from bursting bubbles before they inflate. Then, we explore another set of explanations of why rational traders would be led to actually profit from and surf on bubbles, by anticipating the behavior of noise traders or by realizing the difficulties in synchronizing their actions. We then end by discussing a complex system approach of social imitation leading to collective market regimes like herding and the phenomenon of bifurcation (or phase transition) that rationalize what crash can occur in unstable market regimes. The key insight is that diagnosing bubbles may be feasible when taking into account the positive feedback mechanisms that give rise to transient "super-exponential" price growth, the bubbles.

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Microeconomic Models for Long Memory in the Volatility of Financial Time Series

Cited by 79 publications

References 0 publications

Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model

Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model

Microeconomic co-evolution model for financial technical analysis signals

Bubbles and Crashes

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