[Un modèle structurel de la dynamique des marchés]
Dans cet article, nous explorons une approche permettant de comprendre les fluctuations de prix au sein d'un marché en tenant compte des dépendances fonctionnelles entre les prix des actifs. Cette approche suggère une classe de modèles d'un type utilisé précédemment pour décrire la dynamique des réseaux neuronaux réels et artificiels. Les approches de la physique statistique s'avèrent appropriées pour l'analyse de leurs propriétés collectives. Dans cet article, nous motivons d'abord la phénoménologie de base et les arguments de modélisation avant de passer à la discussion de certaines questions majeures avec inférence et vérification empirique. En particulier, nous nous concentrons sur la création naturelle d'états de marché par l'inclusion d'interactions et sur la façon dont celles-ci interfèrent ensuite avec l'inférence. Cette question est principalement abordée dans le cadre de données synthétiques. Enfin, nous examinons des données réelles pour vérifier la capacité de notre approche à saisir certaines caractéristiques clés du comportement des marchés financiers.
In this paper, we explore an approach to understanding price fluctuations within a market via considerations of functional dependencies between asset prices. Interestingly, this approach suggests a class of models of a type used earlier to describe the dynamics of real and artificial neural networks. Statistical physics approaches turn out to be suitable for an analysis of their collective properties. In this paper, we first motivate the basic phenomenology and modelling arguments before moving on to discussing some major issues with inference and empirical verification. In particular, we focus on the natural creation of market states through the inclusion of interactions and how these then interfere with inference. This is primarily addressed in a synthetic setting. Finally we investigate real data to test the ability of our approach to capture some key features of the behaviour of financial markets.
Mot clés : Risque de marché, Transitions de phase, Méthode de la fonctionnelle génératrice
Jonathan Khedair 1 ; Reimer Kühn 1
@article{CRPHYS_2019__20_4_336_0, author = {Jonathan Khedair and Reimer K\"uhn}, title = {A structural model of market dynamics, and why it matters}, journal = {Comptes Rendus. Physique}, pages = {336--348}, publisher = {Elsevier}, volume = {20}, number = {4}, year = {2019}, doi = {10.1016/j.crhy.2019.05.013}, language = {en}, }
Jonathan Khedair; Reimer Kühn. A structural model of market dynamics, and why it matters. Comptes Rendus. Physique, Volume 20 (2019) no. 4, pp. 336-348. doi : 10.1016/j.crhy.2019.05.013. https://comptes-rendus.academie-sciences.fr/physique/articles/10.1016/j.crhy.2019.05.013/
[1] Empirical properties of asset returns: stylized facts and statistical issues, Quant. Finance, Volume 1 (2001), pp. 223-236
[2] Theory of Financial Risk and Derivative Pricing: From Statistical Physics to Risk Management, Cambridge University Press, Cambridge, UK, 2006
[3] An Introduction to Econophysics: Correlations and Complexity in Finance, Cambridge University Press, New York, NY, USA, 2000
[4] Financial physics, Rep. Prog. Phys., Volume 66 (2003) no. 10, p. 1611
[5] Physics and financial economics (1776–2014): puzzles, Ising and agent-based models, Rep. Prog. Phys., Volume 77 (2014)
[6] Stochastic process with ultraslow convergence to a Gaussian: the truncated Lévy flight, Phys. Rev. Lett., Volume 73 (1994), pp. 2946-2949
[7] Financial Modelling with Jump Processes, CRC Press, Inc., 2003
[8] Generalized autoregressive conditional heteroscedasticity, J. Econom., Volume 31 (1986), pp. 307-327
[9] A closed-form solution for options with stochastic volatility with applications to bond and currency options, Rev. Financ. Stud., Volume 6 (1993) no. 2, pp. 327-343
[10] Self-organized percolation model for stock market fluctuations, Physica A, Volume 271 (1999), pp. 496-506
[11] Herd behavior and aggregate fluctuations in financial markets, Macroecon. Dyn., Volume 4 (2000), pp. 170-196
[12] Expectation bubbles in a spin model of market intermittency from frustration across scales, Int. J. Mod. Phys. C, Volume 12 (2001), pp. 667-674
[13] Minority Games: Interacting Agents in Financial Markets, Oxford University Press, 2004
[14] The Mathematical Theory of Minority Games — Statistical Mechanics of Interacting Agents, Oxford University Press, Oxford, UK, 2005
[15] Intermittency in a minimal interacting generalisation of the geometric Brownian motion model, J. Phys. A, Volume 41 (2008)
[16] Portfolio selection, J. Finance, Volume 7 (1952), pp. 77-91
[17] A review of two decades of correlations, hierarchies, networks and clustering in financial markets, 2018 | arXiv
[18] Identifying states of a financial market, Sci. Rep., Volume 2 (2012)
[19] Dissecting financial markets: sectors and states, Quant. Finance, Volume 2 (2002), pp. 297-304
[20] Hierarchical structure in financial markets, Eur. Phys. J. B, Volume 11 (1999), pp. 193-197
[21] Correlation, hierarchies, and networks in financial markets, J. Econ. Behav. Organ., Volume 75 (2010), pp. 40-58 (Transdisciplinary Perspectives on Economic Complexity)
[22] Evolution of worldwide stock markets, correlation structure, and correlation-based graphs, Phys. Rev. E, Volume 84 (2011)
[23] Community detection for correlation matrices, Phys. Rev. X, Volume 5 ( Apr 2015 )
[24] Dynamics of cluster structures in a financial market network, Physica A, Volume 413 (2014), pp. 523-533
[25] Dynamics of stock market correlations, Czech Econ. Rev., Volume 4 (2010), pp. 330-341
[26] Dynamic spanning trees in stock market networks: the case of Asia-Pacific, Physica A, Volume 414 (2014), pp. 387-402
[27] Dynamics of market correlations: taxonomy and portfolio analysis, Phys. Rev. E, Volume 68 (2003)
[28] Random matrix approach to cross correlations in financial data, Phys. Rev. E, Volume 65 (2002)
[29]
, Oxford University Press, Oxford (2011), pp. 824-848 (Chapter 40)[30] Cleaning large correlation matrices: tools from random matrix theory, Phys. Rep., Volume 666 (2017), pp. 1-109
[31] A structural model for fluctuations in financial markets, Phys. Rev. E, Volume 97 (2018)
[32] Neurons with graded responses have collective computational properties like those of two-state neurons, Proc. Natl. Acad. Sci. USA, Volume 81 (1984), pp. 3088-3092
[33] Exact mean-field inference in asymmetric kinetic Ising systems, J. Stat. Mech. Theory Exp., Volume 2011 (2011) no. 07
[34] Mean-field theory for the inverse Ising problem at low temperatures, Phys. Rev. Lett., Volume 109 (2012)
[35] Inference and learning in sparse systems with multiple states, Phys. Rev. E, Volume 83 ( May 2011 )
[36] Statistical physics and representations in real and artificial neural networks, Physica A, Volume 504 (2018), pp. 45-76 (Lecture Notes of the 14th International Summer School on Fundamental Problems in Statistical Physics)
[37] Basel Committee on banking supervision. Minimum capital requirements for market risk, 2016 www.bis.org
[38] Noise dressing of financial correlation matrices, Phys. Rev. Lett., Volume 83 (1999), pp. 1467-1470
[39] Quantifying and interpreting collective behavior in financial markets, Phys. Rev. E, Volume 64 ( Aug 2001 )
[40] Universal and nonuniversal properties of cross correlations in financial time series, Phys. Rev. Lett., Volume 83 (1999), pp. 1471-1474
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