Econophysics: Empirical facts and agent-based models

By Anirban Chakraborti, Ioane Muni Toke, Marco Patriarca, Frédéric Abergel. 2009

244 citations recorded by the source corpus.

This article aims at reviewing recent empirical and theoretical developments usually grouped under the term Econophysics. Since its name was coined in 1995 by merging the words Economics and Physics, this new interdisciplinary field has grown in various directions: theoretical macroeconomics (wealth distributions), microstructure of financial markets (order book modelling), econometrics of financial bubbles and crashes, etc. In the first part of the review, we discuss on the emergence of Econophysics. Then we present empirical studies revealing statistical properties of financial time series. We begin the presentation with the widely acknowledged stylized facts which describe the returns of financial assets- fat tails, volatility clustering, autocorrelation, etc.- and recall that some of these properties are directly linked to the way time is taken into account. We continue with the statistical properties observed on order books in financial markets. For the sake of illustrating this review, (nearly) all the stated facts are reproduced using our own high-frequency financial database. Finally, contributions to the study of correlations of assets such as random matrix theory and graph theory are presented. In the second part of the review, we deal with models in Econophysics through the point of view of agent-based modelling. Amongst a large number of multi-agent-based models, we have identified three representative areas. First, using previous work originally presented in the fields of behavioural finance and market microstructure theory, econophysicists have developed agent-based models of order-driven markets that are extensively presented here. Second, kinetic theory models designed to explain some empirical facts on wealth distribution are reviewed. Third, we briefly summarize game theory models by reviewing the now classic minority game and related problems.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/7b4b935c33c8644213151c0a7495e5d70ff1e820

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Citations in the corpus

Quantifying China's Regional Economic Complexity (2017, background): “uate online reputation in socioeconomic systems [30, 31] and to build better recommender systems in e-commerce [32, 33]. More recent works on econophysics and complexityare summarizedby review papers [34, 35, 36] and books [37, 38]. Towards quantifying the complexity of a country’s economy, the pion”
Limit order books (2010, methodology): “Heavy tails have been observed on Euronext (Chakraborti et al. 2011b), the Paris Bourse (Plerou and Stanley 2008), the S&P 500 index (Gallant et al. 1992, Gopikrishnan et al. 1999, Cont 2001), FX markets (Guillaume et al. 1997), the NYSE (Gopikrishnan et al. 1998), the American Stock Exchange…”
Recent advances in reinforcement learning in finance (2021, background): “…of the BSMmodel are not realistic since: (1) transaction costs due to commissions, market impact, and nonzero bid–ask spread exist in the real market; (2) the volatility is not constant; (3) short-term returns typically have a heavy-tailed distribution (Cont, 2001; Chakraborti et al., 2011).”
Price jump prediction in a limit order book (2012, background): “he center of interest, and the price changes are but a by-product of the more complicated set of changes on limit orders, market orders, cancellation of orders, ... see e.g. Chakraborti et al. (2009) [6], Abergel et al. (2011) [1] for the latest developments in the econophysics of order-driven marke”
The role of volume in order book dynamics: a multivariate Hawkes process analysis (2016, background): “e volume) and the price. Several are the challenges for a mathematical description of this system. First, the arrival times of the orders are not well described by a Poisson process (see for example (Chakraborti et al., 2011) and references therein). In fact, the time durations between events are no”