Results 1  10
of
44
Herd Behavior and Aggregate Fluctuations in Financial Markets
"... We present a simple model of a stock market where a random communication structure between agents generically gives rise to heavy tails in the distribution of stock price variations in the form of an exponentially truncated powerlaw, similar to distributions observed in recent empirical studies of ..."
Abstract

Cited by 55 (1 self)
 Add to MetaCart
We present a simple model of a stock market where a random communication structure between agents generically gives rise to heavy tails in the distribution of stock price variations in the form of an exponentially truncated powerlaw, similar to distributions observed in recent empirical studies of high frequency market data. Our model provides a link between two wellknown market phenomena: the heavy tails observed in the distribution of stock market returns on one hand and 'herding' behavior in financial markets on the other hand. In particular, our study suggests a relation between the excess kurtosis observed in asset returns, the market order flow and the tendency of market participants to imitate each other. Keywords: heavy tails, financial markets, herd behavior, market organization, intermittency, random graphs, percolation. JEL Classification number: C0, D49, G19 1 R. Cont gratefully acknowledges an AMX fellowship from Ecole Polytechnique (France) and thanks Science & Financ...
A Langevin approach to stock market fluctuations and crashes
 EUROPEAN PHYSICS JOURNAL B
, 1998
"... We propose a non linear Langevin equation as a model for stock market fluctuations and crashes. This equation is based on an identification of the different processes influencing the demand and supply, and their mathematical transcription. We emphasize the importance of feedback e#ects of price va ..."
Abstract

Cited by 37 (4 self)
 Add to MetaCart
We propose a non linear Langevin equation as a model for stock market fluctuations and crashes. This equation is based on an identification of the different processes influencing the demand and supply, and their mathematical transcription. We emphasize the importance of feedback e#ects of price variations onto themselves. Risk aversion, in particular, leads to an "updown" symmetry breaking term which is responsible for crashes, where "panic" is self reinforcing. It is also responsible for the sudden collapse of speculative bubbles. Interestingly, these crashes appear as rare, "activated" events, and have an exponentially small probability of occurence. The model leads to a specific "shape" of the falldown of the price during a crash, which we compare with the October 1987 data. The normal regime, where the stock price exhibits behavior similar to that of a random walk, however reveals non trivial correlations on different time scales, in particular on the time scale over which operators perceive a change of trend.
A microsimulation of traders activity in the stock market: the role of heterogeneity, agents' interaction and . . .
, 2000
"... ..."
The Leverage Effect in Financial Markets: Retarded Volatility and Market Panic
, 2001
"... We investigate quantitatively the socalled leverage effect, which corresponds to a negative correlation between past returns and future volatility. For individual stocks, this... ..."
Abstract

Cited by 25 (1 self)
 Add to MetaCart
We investigate quantitatively the socalled leverage effect, which corresponds to a negative correlation between past returns and future volatility. For individual stocks, this...
Apparent Multifractality in Financial Time Series
, 1999
"... We present a exactly soluble model for financial time series that mimics the long range volatility correlations known to be present in financial data. Although our model is `monofractal' by construction, it shows apparent multiscaling as a result of a slow crossover phenomenon on finite time ..."
Abstract

Cited by 15 (0 self)
 Add to MetaCart
(Show Context)
We present a exactly soluble model for financial time series that mimics the long range volatility correlations known to be present in financial data. Although our model is `monofractal' by construction, it shows apparent multiscaling as a result of a slow crossover phenomenon on finite time scales. Our results suggest that it might be hard to distinguish apparent and true multifractal behavior in financial data. Our model also leads to a new family of stable laws for sums of correlated random variables. Many time series exhibit interesting scaling properties. This means that if x(t) denotes the time series, the probability distribution of the variations ffi T x = x(t + T ) \Gamma x(t), rescaled by a lagdependent factor (T ), can be written as: P (ffi T x; T ) = 1 (T ) F ` ffi T x (T ) ' ; (1) where F(u) is a time independent scaling function. For example, if x(t) is constructed by summing independent identically distributed random variables with finite variance, one h...
Financial Markets as Adaptive Systems
, 1998
"... We show, by studying in detail the market prices of options on liquid markets, that the market has empirically corrected the simple, but inadequate BlackScholes formula to account for two important statistical features of asset fluctuations: "fat tails" and correlations in the scale of fl ..."
Abstract

Cited by 14 (1 self)
 Add to MetaCart
We show, by studying in detail the market prices of options on liquid markets, that the market has empirically corrected the simple, but inadequate BlackScholes formula to account for two important statistical features of asset fluctuations: "fat tails" and correlations in the scale of fluctuations. These aspects, although not included in the pricing models, are very precisely reflected in the price fixed by the market as a whole. Financial markets thus behave as rather efficient adaptive systems.
On the Nature of the Stock Market: Simulation and Experiments
, 2000
"... Over the last few years there has been a surge of activity within the physics community in the emerging field of Econophysics—the study of economic systems from a physicist’s perspective. Physicists tend to take a different view than economists and other social scientists, being interested in such t ..."
Abstract

Cited by 11 (0 self)
 Add to MetaCart
(Show Context)
Over the last few years there has been a surge of activity within the physics community in the emerging field of Econophysics—the study of economic systems from a physicist’s perspective. Physicists tend to take a different view than economists and other social scientists, being interested in such topics as phase transitions and fluctuations. In this dissertation two simple models of stock exchange are developed and simulated numerically. The first is characterized by centralized trading with a market maker. Fluctuations are driven by a stochastic component in the agents ’ forecasts. As the scale of the fluctuations is varied a critical phase transition is discovered. Unfortunately, this model is unable to generate realistic market dynamics. The second model discards the requirement of centralized trading. In this case the stochastic driving force is Gaussiandistributed “news events ” which are public knowledge. Under variation of the control parameter the model exhibits two phase transitions: both a first and a secondorder (critical). The decentralized model is able to capture many of the interesting properties
Modeling Economic Randomness: Statistical Mechanics Of Market Phenomena
 in: M. Batchelor & L.T. Wille (Eds.) Statistical Physics on the eve of the 21st century, Singapore: World Scienti
, 1999
"... Introduction Since the 1980s, the deterioration of the academic job market in physics has been attracting a large number of physicists to investment banks: many of them are now working as \quants", designing sophisticated new derivative products or developing numerically intensive data analysi ..."
Abstract

Cited by 11 (4 self)
 Add to MetaCart
Introduction Since the 1980s, the deterioration of the academic job market in physics has been attracting a large number of physicists to investment banks: many of them are now working as \quants", designing sophisticated new derivative products or developing numerically intensive data analysis techniques for price and volatility forecasting. More recently, several teams of physicists have launched their own rms, oering services in the elds of nancial software design and forecasting. There exists however another set of motivations { scientic ones { which have also been prompting theoretical physicists { especially those with a background in statistical physics { to become interested in nance. Although this phenomenon may seem a bit mysterious to the outsider, we will attempt to convince the reader that it is not: nancial markets may well be considered as objects of high potential interest for researchers in statistical physics. 1.1 Motivations Statist
Statistical Properties of Financial Time Series
, 1999
"... We present a set of stylized empirical facts emerging from the statistical analysis of price variations in various types of financial markets. We first present data sources and discuss the choice of a time scale when constructing financial time series. Various statistical properties of asset returns ..."
Abstract

Cited by 8 (3 self)
 Add to MetaCart
We present a set of stylized empirical facts emerging from the statistical analysis of price variations in various types of financial markets. We first present data sources and discuss the choice of a time scale when constructing financial time series. Various statistical properties of asset returns are then described: distributional properties, tail analysis and extreme fluctuations, linear and nonlinear dependence of returns in time and across stocks. Our description emphasizes properties common to a wide variety of markets and instruments. The last part deals with interest rates: we present some issues encountered in constructing yield curves from empirical data and discuss the statistical properties of the term structure fluctuations.