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Applications of statistical physics in finance and economics

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  • Lux, Thomas
Abstract
This chapter reviews recent research adopting methods from statistical physics in theoretical or empirical work in economics and finance. The bulk of what has recently become known as 'econophysics' in broader circles draws its motivation from observed scaling laws in financial markets and the abundance of data available from the economy's financial sphere. Sec. 2 of this review presents the robust power laws encountered in financial economics and discusses potential explanations for scaling in finance derived from models of stochastic interactions of traders. Sec. 3 provides an overview over other applications of statistical physics methodology in finance and attempts to evaluate the impact they have had so far on financial economics. With the following section, the review turns to recent work on the emergence of wealth and income heterogeneity and the recent inception of new strands of research on this topic, both within econophysics and the neoclassical economics tradition. Sec. 5 reviews the new stylized facts that have been identified in cross-sectional data of firm characteristics and agent-based approaches to industrial organization and macroeconomic dynamics that have been motivated by these findings. We conclude with an assessment of the major methodological contributions of this new strand of research.

Suggested Citation

  • Lux, Thomas, 2008. "Applications of statistical physics in finance and economics," Kiel Working Papers 1425, Kiel Institute for the World Economy (IfW Kiel).
    • Handle: RePEc:zbw:ifwkwp:1425
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    3. Pierre Gosselin & Aïleen Lotz & Marc Wambst, 2020. "A path integral approach to business cycle models with large number of agents," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(4), pages 899-942, October.
    4. Hongli Niu & Jun Wang, 2014. "Phase and multifractality analyses of random price time series by finite-range interacting biased voter system," Computational Statistics, Springer, vol. 29(5), pages 1045-1063, October.
    5. Maximilian Beikirch & Simon Cramer & Martin Frank & Philipp Otte & Emma Pabich & Torsten Trimborn, 2018. "Simulation of Stylized Facts in Agent-Based Computational Economic Market Models," Papers 1812.02726, arXiv.org, revised Nov 2019.
    6. Mikhail Goykhman, 2017. "Wealth dynamics in a sentiment-driven market," Papers 1705.07092, arXiv.org.
    7. Vladik Kreinovich & Monchaya Chiangpradit & Wararit Panichkitkosolkul, 2012. "Efficient algorithms for heavy-tail analysis under interval uncertainty," Annals of Operations Research, Springer, vol. 195(1), pages 73-96, May.
    8. Didier SORNETTE, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based Models," Swiss Finance Institute Research Paper Series 14-25, Swiss Finance Institute.
    9. Gregor Semieniuk & Ellis Scharfenaker, 2014. "A Bayesian Latent Variable Mixture Model for Filtering Firm Profit Rate," SCEPA working paper series. 2014-1, Schwartz Center for Economic Policy Analysis (SCEPA), The New School.
    10. AlShelahi, Abdullah & Saigal, Romesh, 2018. "Insights into the macroscopic behavior of equity markets: Theory and application," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 778-793.
    11. Shu-Heng Chen & Sai-Ping Li, 2011. "Econophysics: Bridges over a Turbulent Current," Papers 1107.5373, arXiv.org.
    12. Sandro Claudio Lera & Didier Sornette, 2015. "Currency target zone modeling: An interplay between physics and economics," Papers 1508.04754, arXiv.org, revised Oct 2015.
    13. LI, XI HAO & Gallegati, Mauro, 2015. "Stock-Flow Dynamic Projection," MPRA Paper 62047, University Library of Munich, Germany.
    14. Schinckus, Christophe, 2018. "Ising model, econophysics and analogies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 95-103.
    15. Goykhman, Mikhail, 2017. "Wealth dynamics in a sentiment-driven market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 488(C), pages 132-148.

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    Keywords

    Power laws; Agent-based models; Econophysics; Stylized facts;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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