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143
Heterogeneous agent models in economics and finance
 IN HANDBOOK OF COMPUTATIONAL ECONOMICS (EDS
, 2005
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Agentbased computational finance
 in Handbook of Computational Economics, Agentbased Computational Economics
, 2006
"... This paper surveys research on computational agentbased models used in finance. It will concentrate on models where the use of computational tools is critical in the process of crafting models which give insights into the importance and dynamics of investor heterogeneity in many financial settings. ..."
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Cited by 47 (2 self)
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This paper surveys research on computational agentbased models used in finance. It will concentrate on models where the use of computational tools is critical in the process of crafting models which give insights into the importance and dynamics of investor heterogeneity in many financial settings.
A limit theorem for financial markets with inert investors
 Mathematics of Operations Research
, 2003
"... We study the effect of investor inertia on stock price fluctuations with a market microstructure model comprising many small investors who are inactive most of the time. It turns out that semiMarkov processes are tailor made for modeling inert investors. With a suitable scaling, we show that when t ..."
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Cited by 16 (2 self)
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We study the effect of investor inertia on stock price fluctuations with a market microstructure model comprising many small investors who are inactive most of the time. It turns out that semiMarkov processes are tailor made for modeling inert investors. With a suitable scaling, we show that when the price is driven by the market imbalance, the log price process is approximated by a process with long range dependence and nonGaussian returns distributions, driven by a fractional Brownian motion. Consequently, investor inertia may lead to arbitrage opportunities for sophisticated ‘third parties’. The mathematical contributions are a functional central limit theorem for stationary semiMarkov processes, and approximation results for stochastic integrals of continuous semimartingales with respect to fractional Brownian motion.
Leverage causes fat tails and clustered volatility. Preprint. Traders’ collective portfolio optimization with transaction costs
"... We build a simple model of leveraged asset purchases with margin calls. Investment funds use what is perhaps the most basic financial strategy, called “value investing”, i.e. systematically attempting to buy underpriced assets. When funds do not borrow, the price fluctuations of the asset are normal ..."
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Cited by 16 (5 self)
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We build a simple model of leveraged asset purchases with margin calls. Investment funds use what is perhaps the most basic financial strategy, called “value investing”, i.e. systematically attempting to buy underpriced assets. When funds do not borrow, the price fluctuations of the asset are normally distributed and uncorrelated across time. All this changes when the funds are allowed to leverage, i.e. borrow from a bank, to purchase more assets than their wealth would otherwise permit. During good times competition drives investors to funds that use more leverage, because they have higher profits. As leverage increases price fluctuations become heavy tailed and display clustered volatility, similar to what is observed in real markets. Previous explanations of fat tails and clustered volatility depended on “irrational behavior”, such as trend following. Here instead this comes from the fact that leverage limits cause funds to sell into a falling market: A prudent bank makes itself locally safer by putting a limit to leverage, so when a fund exceeds its leverage limit, it must partially repay its loan by selling the asset. Unfortunately this sometimes happens to all the funds simultaneously when the price is already falling. The resulting nonlinear feedback amplifies large downward price movements. At the extreme this causes crashes, but the effect is seen at every time scale, producing a power law of price disturbances. A standard (supposedly more sophisticated) risk control policy in which individual banks base leverage limits on volatility causes leverage to rise during periods of low volatility, and to contract more quickly when volatility gets high, making these extreme fluctuations even worse.
Random walks, liquidity molasses and critical response in financial markets, Quantitative Finance
, 2006
"... Stock prices are observed to be random walks in time despite a strong, long term memory in the signs of trades (buys or sells). Lillo and Farmer have recently suggested that these correlations are compensated by opposite long ranged fluctuations in liquidity, with an otherwise permanent market impac ..."
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Cited by 13 (3 self)
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Stock prices are observed to be random walks in time despite a strong, long term memory in the signs of trades (buys or sells). Lillo and Farmer have recently suggested that these correlations are compensated by opposite long ranged fluctuations in liquidity, with an otherwise permanent market impact, challenging the scenario proposed in Quantitative Finance 4, 176 (2004), where the impact is transient, with a powerlaw decay in time. The exponent of this decay is precisely tuned to a critical value, ensuring simultaneously that prices are diffusive on long time scales and that the response function is nearly constant. We provide new analysis of empirical data that confirm and make more precise our previous claims. We show that the powerlaw decay of the bare impact function comes both from an excess flow of limit order opposite to the market order flow, and to a systematic anticorrelation of the bidask motion between trades, two effects that create a ‘liquidity molasses ’ which dampens market volatility. 1 1
Stock price jumps: news and volume play a minor role. ArXiv eprints
"... In order to understand the origin of stock price jumps, we crosscorrelate highfrequency time series of stock returns with different news feeds. We find that neither idiosyncratic news nor market wide news can explain the frequency and amplitude of price jumps. We find that the volatility patterns a ..."
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Cited by 13 (3 self)
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In order to understand the origin of stock price jumps, we crosscorrelate highfrequency time series of stock returns with different news feeds. We find that neither idiosyncratic news nor market wide news can explain the frequency and amplitude of price jumps. We find that the volatility patterns around jumps and around news are quite different: jumps are followed by increased volatility, whereas news tend on average to be followed by lower volatility levels. The shape of the volatility relaxation is also markedly different in the two cases. Finally, we provide direct evidence that large transaction volumes are not responsible for large price jumps. We conjecture that most price jumps are induced by order flow fluctuations close to the point of vanishing liquidity. Why do stock prices change? The traditional answer, within the theory of efficient markets, is that prices move because some new piece of information becomes
Volatility clustering in financial markets: Empirical facts and agent based models
, 2004
"... Summary. Time series of financial asset returns often exhibit the volatility clustering property: large changes in prices tend to cluster together, resulting in persistence of the amplitudes of price changes. After recalling various methods for quantifying and modeling this phenomenon, we discuss se ..."
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Cited by 12 (0 self)
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Summary. Time series of financial asset returns often exhibit the volatility clustering property: large changes in prices tend to cluster together, resulting in persistence of the amplitudes of price changes. After recalling various methods for quantifying and modeling this phenomenon, we discuss several economic mechanisms which have been proposed to explain the origin of this volatility clustering in terms of behavior of market participants and the news arrival process. A common feature of these models seems to be a switching between low and high activity regimes with heavytailed durations of regimes. Finally, we discuss a simple agentbased model which links such variations in market activity to threshold behavior of market participants and suggests a link between volatility clustering and investor inertia. 1
A Heterogeneous, Endogenous and Coevolutionary GPbased Financial Market
"... Stock markets are very important in modern societies and their behaviour have serious implications in a wide spectrum of the world’s population. Investors, governing bodies and the society as a whole could benefit from better understanding of the behavior of stock markets. The traditional approach t ..."
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Cited by 11 (5 self)
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Stock markets are very important in modern societies and their behaviour have serious implications in a wide spectrum of the world’s population. Investors, governing bodies and the society as a whole could benefit from better understanding of the behavior of stock markets. The traditional approach to analyze such systems is the use of analytical models. However, the complexity of financial markets represents a big challenge to the analytical approach. Most analytical models make simplifying assumptions, such as perfect rationality and homogeneous investors, which threaten the validity of analytical results. This motivates alternative methods. In this work, we developed an artificial financial market and used it to study the behavior of stock markets. In this market, we model technical, fundamental and noise traders. The technical traders are sophisticated genetic programming based agents that coevolve (by means of their fitness function) by predicting investment opportunities in the market using technical analysis as the main tool. With this endogenous artificial market, we identified conditions under which the statistical properties of price series in the artificial market resembles those of the real financial markets. Additionally, we modeled the pressure to beat the market by a behavioral constraint imposed on the agents reflecting the Red Queen principle in evolution. We have demonstrated how evolutionary computation could play a key role in studying stock markets.
t−statistic based correlation and heterogeneity Robust Inference
, 2008
"... We develop a general approach to robust inference about a scalar parameter when the data is potentially heterogeneous and correlated in a largely unknown way. The key ingredient is the following result of Bakirov and Székely (2005) concerning the small sample properties of the standard t−test: For a ..."
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Cited by 10 (0 self)
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We develop a general approach to robust inference about a scalar parameter when the data is potentially heterogeneous and correlated in a largely unknown way. The key ingredient is the following result of Bakirov and Székely (2005) concerning the small sample properties of the standard t−test: For a significance level of 5 % or lower, the t−test remains conservative for underlying observations that are independent and Gaussian with heterogenous variances. One might thus conduct robust large sample inference as follows: partition the data into q ≥ 2 groups, estimate the model for each group and conduct a standard t−test with the resulting q parameter estimators. This results in valid and in some sense efficient inference when the groups are chosen in a way that ensures the parameter estimators to be asymptotically independent, unbiased and Gaussian of possibly different variances. We provide examples of how to apply this approach to time series, panel, clustered and spatially correlated data.
Artificial Financial Markets: An Agent Based . . .
, 2007
"... Stock markets are very important in modern societies and their behaviour have serious implications in a wide spectrum of the world’s population. Investors, governing bodies and the society as a whole could benefit from better understanding of the behaviour of stock markets. The traditional approach ..."
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Cited by 6 (0 self)
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Stock markets are very important in modern societies and their behaviour have serious implications in a wide spectrum of the world’s population. Investors, governing bodies and the society as a whole could benefit from better understanding of the behaviour of stock markets. The traditional approach to analyze such systems is the use of analytical models. However, the complexity of financial markets represents a big challenge to the analytical approach. Most analytical models make simplifying assumptions, such as perfect rationality and homogeneous investors, which threaten the validity of analytical results. This motivates the use of alternative methods. For those reasons, the study of such markets is a fertile field to use the agentbased methodology. In this work, we developed an artificial financial market and used it to study the behaviour of stock markets. In this market, we model technical, fundamental and noise traders. The technical traders are nonsimple genetic programming based agents that coevolve (by means of their fitness function) by predicting investment opportunities in the market using technical analysis as the main tool. Such traders are equipped with