Senin, 28 November 2011

Econophysics

Econophysics is an interdisciplinary research field, applying theories and methods originally developed by physicists in order to solve problems in economics, usually those including uncertainty or stochastic processes and nonlinear dynamics. Its application to the study of financial markets has also been termed statistical finance referring to its roots in statistical physics.

History

Physicists’ interest in the social sciences is not new; Daniel Bernoulli, as an example, was the originator of utility-based preferences. One of the founders of neoclassical economic theory, former Yale University Professor of Economics Irving Fisher, was originally trained under the renowned Yale physicist, Josiah Willard Gibbs.[1] Likewise, Jan Tinbergen, who won the first Nobel Prize in economics in 1969 for having developed and applied dynamic models for the analysis of economic processes, studied physics with Paul Ehrenfest at Leiden University.

Econophysics was started in the mid-1990s by several physicists working in the subfield of statistical mechanics. Unsatisfied with the traditional explanations and approaches of economists - which usually prioritized simplified approaches for the sake of soluble theoretical models over agreement with empirical data - they applied tools and methods from physics, first to try to match financial data sets, and then to explain more general economic phenomena.

One driving force behind econophysics arising at this time was the sudden availability of large amounts of financial data, starting in the 1980s. It became apparent that traditional methods of analysis were insufficient - standard economic methods dealt with homogeneous agents and equilibrium, while many of the more interesting phenomena in financial markets fundamentally depended on heterogeneous agents and far-from-equilibrium situations.

The term “econophysics” was coined by H. Eugene Stanley in the mid 1990s, to describe the large number of papers written by physicists in the problems of (stock and other) markets, and first appeared in a conference on statistical physics in Calcutta in 1995 and its following publications. The inaugural meeting on Econophysics was organised 1998 in Budapest by János Kertész and Imre Kondor.

Currently, the almost regular meeting series on the topic include: Econophysics Colloquium, ESHIA/ WEHIA, ECONOPHYS-KOLKATA, APFA

If "econophysics" is taken to denote the principle of applying statistical mechanics to economic analysis, as opposed to a particular literature or network, priority of innovation is probably due to Farjoun and Machover (1983). Their book Laws of Chaos: A Probabilistic Approach to Political Economy proposes dissolving (their words) the transformation problem in Marx's political economy by re-conceptualising the relevant quantities as random variables.

If, on the other side, "econophysics" is taken to denote the application of physics to economics, one can already consider the works of Léon Walras and Vilfredo Pareto as part of it. Indeed, as shown by Ingrao and Israel, general equilibrium theory in economics is based on the physical concept of mechanical equilibrium.

Econophysics has nothing to do with the "physical quantities approach" to economics, advocated by Ian Steedman and others associated with Neo-Ricardianism. Notable econophysicists are Jean-Philippe Bouchaud, Bikas K Chakrabarti, Dirk Helbing, János Kertész, Matteo Marsili, Joseph L. McCauley, Enrico Scalas, Didier Sornette, H. Eugene Stanley, Victor Yakovenko and Yi-Cheng Zhang.


Basic tools

Basic tools of econophysics are probabilistic and statistical methods often taken from statistical physics.

Physics models that have been applied in economics include percolation models, chaotic models developed to study cardiac arrest, and models with self-organizing criticality as well as other models developed for earthquake prediction.[2] Moreover, there have been attempts to use the mathematical theory of complexity and information theory, as developed by many scientists among whom are Murray Gell-Mann and Claude E. Shannon, respectively.

Since economic phenomena are the result of the interaction among many heterogeneous agents, there is an analogy with statistical mechanics, where many particles interact; but it must be taken into account that the properties of human beings and particles significantly differ.

Another good example is Random Matrix Theory, which can be used to identify the noise in financial correlation matrices. It has been shown that this technique can significantly improve the performance of portfolios, e.g., in applied in Portfolio Optimization[3] . Thus, this practice is commonly encountered in the praxis of quantitative finance.

There are, however, various other tools from physics that have so far been used with mixed success, such as fluid dynamics, classical mechanics and quantum mechanics (including so-called classical economy, quantum economy and quantum finance), and the path integral formulation of statistical mechanics.

There are also analogies between finance theory and diffusion theory. For instance, the Black-Scholes equation for option pricing is a diffusion-advection equation.


Impact on mainstream economics and finance

Papers on econophysics have been published primarily in journals devoted to physics and statistical mechanics, rather than in leading economics journals. Mainstream economists have generally been unimpressed by this work.[4] Some Heterodox economists, including Mauro Gallegati, Steve Keen and Paul Ormerod, have shown more interest, but also criticized trends in econophysics.

In contrast, econophysics is having some impact on the more applied field of quantitative finance, whose scope and aims significantly differ from those of economic theory. Various econophysicists have introduced models for price fluctuations in financial markets or original points of view on established models.[5][6] Also several scaling laws have been found in various economic data.[7][8][9]

See also

Stylised Lithium Atom.svg Physics portal

References

  1. ^ Yale Economic Review, Retrieved October-25-09
  2. ^ Didier Sornette (2003). Why Stock Markets Crash?. Princeton University Press.
  3. ^ Vasiliki Plerou, Parameswaran Gopikrishnan, Bernd Rosenow, Luis Amaral, Thomas Guhr and H. Eugene Stanley (2002). "Random matrix approach to cross correlations in financial data". Physical Review E 65 (6): 066126. doi:10.1103/PhysRevE.65.066126.
  4. ^ Philip Ball (2006). "Econophysics: Culture Crash". Nature 441 (7094): 686–688. Bibcode 2006Natur.441..686B. doi:10.1038/441686a. PMID 16760949.
  5. ^ Jean-Philippe Bouchaud, Marc Potters (2003). Theory of Financial Risk and Derivative Pricing. Cambridge University Press.
  6. ^ Enrico Scalas (2006). "The application of continuous-time random walks in finance and economics". Physica A 362 (2): 225–239. Bibcode 2006PhyA..362..225S. doi:10.1016/j.physa.2005.11.024.
  7. ^ Y. Liu, P. Gopikrishnan, P. Cizeau, M. Meyer, C.-K. Peng, and H. E. Stanley (1999). "Statistical properties of the volatility of price fluctuations". Physical Review E 60 (2): 1390. doi:10.1103/PhysRevE.60.1390.
  8. ^ M. H. R. Stanley, L. A. N. Amaral, S. V. Buldyrev, S. Havlin, H. Leschhorn, P. Maass, M. A. Salinger, H. E. Stanley (1996). "Scaling behaviour in the growth of companies". Nature 379 (6568): 804. doi:10.1038/379804a0. http://havlin.biu.ac.il/Publications.php?keyword=Scaling+behaviour+in+the+growth+of+companies&year=*&match=all.
  9. ^ K. Yamasaki, L. Muchnik, S. Havlin, A. Bunde, and H.E. Stanley (2005). "Scaling and memory in volatility return intervals in financial markets". PNAS 102 (26): 9424–8. doi:10.1073/pnas.0502613102. PMC 1166612. PMID 15980152. http://havlin.biu.ac.il/Publications.php?keyword=Scaling+and+memory+in+volatility+return+intervals+in+financial+markets&year=*&match=all.

Further reading

Textbooks

Journal articles, PhD theses



Lectures

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