Econophysics Research in Victor Yakovenko's group
Collaborators

Qin Liu (2013present), graduate student

Scott Lawrence (2013present), undergraduate student

J. Barkley Rosser, Jr. (20082009),
Professor of Economics and Kirby L. Kramer Jr. Chair of
Business Administration (James Madison University, Harrisonburg, Virginia),
Honorary Editor of the
Journal of Economic Behavior and Organization,
EditorinChief of the journal Review of Behavioral Economics (2013present)

Anand Banerjee (20052008), graduate student, Ph.D. 2008,
now a postdoctoral fellow at NIH in Bethesda

Justin Chen (2007 summer), undergraduate student from Caltech,
developed computer animation of money exchange models

Richard Prange (20022008), Professor Emeritus of Physics,
deceased

A. Christian Silva (20022005), graduate student, Ph.D. 2005,
now with the EvnineVaughan Associates

Adrian Dragulescu (19972002), graduate student, Ph.D. 2002,
now a risk analyst at the
Constellation Energy Group in Baltimore
Research Grants
Papers
1. Statistical Mechanics of Money, Income, and Wealth

[1.1] "Statistical mechanics of money" by
A. A. Dragulescu and V. M. Yakovenko

Published:
The European Physical Journal B, v. 17, pp. 723729
(2000), PDF

Preprint:
condmat/0001432,
PDF.
Viewgraphs:
PDF

Computer Animation Video by Justin Chen

Computer Simulations in Mathematica by Ian Wright

Abstract:
In a closed economic system, money is conserved. Thus, by analogy with energy, the equilibrium probability distribution of money must follow the exponential Gibbs law characterized by an effective temperature equal to the average amount of money per economic agent. We demonstrate how the Gibbs distribution emerges in computer simulations of economic models. Then we consider a thermal machine, in which the difference of temperatures allows one to extract a monetary profit. We also discuss the role of debt, and models with broken timereversal symmetry for which the Gibbs law does not hold.

[1.2] "Evidence for the exponential distribution of income in the USA"
by A. A. Dragulescu and V. M. Yakovenko

Published:
The European Physical Journal B, v. 20, pp. 585589 (2001),
PDF

Preprint:
condmat/0008305,
PDF.
Viewgraphs:
PDF

Abstract:
Using tax and census data, we demonstrate that the distribution of individual income in the USA is exponential. Our calculated Lorenz curve without fitting parameters and Gini coefficient 1/2=50% agree well with the data. From the individual income distribution, we derive the distribution function of income for families with two earners and show that it also agrees well with the data. The family data for the period 19471994 fit the Lorenz curve and Gini coefficient 3/8=37.5% calculated for twoearners families.

[1.3] "Exponential and powerlaw probability distributions of wealth
and income in the United Kingdom and the United States" by
A. A. Dragulescu and V. M. Yakovenko

Published:
Physica A, v. 299,
pp. 213221 (2001),
PDF

Preprint:
condmat/0103544,
PDF.
Viewgraphs:
PDF

Abstract:
We present the data on wealth and income distributions in the United Kingdom, as well as on the income distributions in the individual states of the USA. In all of these data, we find that the great majority of population is described by an exponential distribution, whereas the highend tail follows a power law. The distributions are characterized by a dimensional scale analogous to temperature. The values of temperature are determined for the UK and the USA, as well as for the individual states of the USA.

[1.4] "Statistical Mechanics of Money, Income, and
Wealth: A Short Survey" by A. A. Dragulescu and
V. M. Yakovenko

Published:
Modeling of Complex Systems: Seventh Granada Lectures,
AIP Conference Proceedings 661, New York, 2003,
pp. 180183, PDF

Preprint:
condmat/0211175,
PDF.
Viewgraphs:
PDF

Abstract:
In this short paper, we overview and extend the results of our papers
condmat/0001432,
condmat/0008305, and
condmat/0103544, where we use an analogy with statistical physics to describe probability distributions of money, income, and wealth in society. By making a detailed quantitative comparison with the available statistical data, we show that these distributions are described by simple exponential and powerlaw functions.

[1.5] "Temporal evolution of the `thermal' and `superthermal'
income classes in the USA during 19832001" by
A. C. Silva and V. M. Yakovenko

Published:
Europhysics Letters, v. 69, pp. 304310 (2005),
PDF

Preprint:
condmat/0406385,
PDF.
Presentation:
Viewgraphs,
Video, and Audio online.

Abstract:
Personal income distribution in the USA has a welldefined twoclass structure. The majority of population (9799%) belongs to the lower class characterized by the exponential BoltzmannGibbs ("thermal") distribution, whereas the upper class
(13% of population) has a Pareto powerlaw ("superthermal") distribution. By analyzing income data for 19832001, we show that the "thermal" part is stationary in time, save for a gradual increase of the effective temperature, whereas the "superthermal" tail swells and shrinks following the stock market. We discuss the concept of equilibrium inequality in a society, based on the principle of maximal entropy, and quantitatively show that it applies to the majority of population.

[1.6] "Twoclass structure of income
distribution in the USA: Exponential bulk and powerlaw tail" by
V. M. Yakovenko and A. C. Silva

Published: In the book "Econophysics of Wealth
Distributions", edited by A. Chatterjee, S. Yarlagadda, and
B. K. Chakrabarti (2005, Springer series "New Economic Windows",
ISBN 8847003296), pp. 1523

Abstract: Conference proceedings paper based on
[1.5].

[1.7] "A study of the personal income distribution in Australia" by
A. Banerjee, V. M. Yakovenko, and T. Di Matteo

Published:
Physica A, v. 370, pp.
5459 (2006), PDF

Preprint:
physics/0601176,
PDF.

Abstract:
We analyze the data on personal income distribution from the Australian Bureau of Statistics. We compare fits of the data to the exponential, lognormal, and gamma distributions. The exponential function gives a good (albeit not perfect) description of 98% of the population in the lower part of the distribution. The lognormal and gamma functions do not improve the fit significantly, despite having more parameters, and mimic the exponential function. We find that the probability density at zero income is not zero, which contradicts the lognormal and gamma distributions, but is consistent with the exponential one. The highresolution histogram of the probability density shows a very sharp and narrow peak at low incomes, which we interpret as the result of a government policy on income redistribution.

[1.8] "Universal patterns of inequality"
by A. Banerjee and V. M. Yakovenko

Published: New
Journal of Physics 12, 075032 (2010),
PDF

Preprint:
arXiv:0912.4898,
PDF.

Abstract:
We study probability distributions of money, income, and energy consumption per capita for ensembles of economic agents. Following the principle of entropy maximization for partitioning of a limited resource, we find exponential distributions for the investigated variables. We also discuss fluxes of money and population between two systems with different money temperatures. For income distribution, we study a stochastic process with additive and multiplicative components. The resultant income distribution interpolates between exponential at the low end and powerlaw at the high end, in agreement with the empirical data for USA. We discuss how the increase of income inequality in USA in 19832007 results from dramatic increase of the income fraction going to the upper tail and exceeding 20% of the total income. Analyzing the data from the World Resources Institute, we find that the distribution of energy consumption per capita around the world is reasonably well described by the exponential function. Comparing the data for 1990, 2000, and 2005, we discuss the effects of globalization on the inequality of energy consumption.

[1.9] "Global inequality in energy consumption from 1980 to 2010"
by S. Lawrence, Q. Liu, and V. M. Yakovenko

Published: Entropy 15, 55655579 (2013),
PDF

Preprint:
arXiv:1312.6443,
PDF.

Abstract:
We study the global probability distribution of energy consumption per capita around the world using data from the U.S. Energy Information Administration (EIA) for 19802010. We find that the Lorenz curves have moved up during this time period, and the Gini coefficient G has decreased from 0.66 in 1980 to 0.55 in 2010, indicating a decrease in inequality. The global probability distribution of energy consumption per capita in 2010 is close to the exponential distribution with G=0.5. We attribute this result to the globalization of the world economy, which mixes the world and brings it closer to the state of maximal entropy. We argue that global energy production is a limited resource that is partitioned among the world population. The most probable partition is the one that maximizes entropy, thus resulting in the exponential distribution function. A consequence of the latter is the law of 1/3: the top 1/3 of the world population consumes 2/3 of produced energy. We also find similar results for the global probability distribution of CO_{2} emissions per capita.
2. Stochastic Volatility Models for StockPrice Fluctuations

[2.1] "Probability distribution of returns in the Heston model with stochastic
volatility" by A. A. Dragulescu and V. M. Yakovenko

Published:
Quantitative Finance, v. 2, pp. 443453 (2002),
PDF.
Erratum:
Quantitative Finance, v. 3, p. C15 (2003),
PDF

Preprint:
condmat/0203046,
PDF.
Viewgraphs:
vertical.pdf,
horizontal.pdf,

Abstract:
We study the Heston model, where the stock price dynamics is governed by a geometrical (multiplicative) Brownian motion with stochastic variance. We solve the corresponding FokkerPlanck equation exactly and, after integrating out the variance, find an analytic formula for the timedependent probability distribution of stock price changes (returns). The formula is in excellent agreement with the DowJones index for time lags from 1 to 250 trading days. For large returns, the distribution is exponential in logreturns with a timedependent exponent, whereas for small returns it is Gaussian. For time lags longer than the relaxation time of variance, the probability distribution can be expressed in a scaling form using a Bessel function. The DowJones data for 1982–2001 follow the scaling function for seven orders of magnitude.

[2.2] "Comparison between the probability distribution of returns in
the Heston model and empirical data for stock indexes" by
A. C. Silva and V. M. Yakovenko

Published:
Physica A 324, 303310 (2003),
PDF

Preprint:
condmat/0211050,
PDF.
Viewgraphs: pdf

Abstract:
We compare the probability distribution of returns for the three major stockmarket indexes (Nasdaq, S&P500, and DowJones) with an analytical formula recently derived by
Dragulescu and Yakovenko for the Heston model with stochastic variance. For the period of 19821999, we find a very good agreement between the theory and the data for a wide range of time lags from 1 to 250 days. On the other hand, deviations start to appear when the data for 20002002 are included. We interpret this as a statistical evidence of the major change in the market from a positive growth rate in 1980s and 1990s to a negative rate in 2000s.

[2.3] "Exponential distribution of financial
returns at mesoscopic time lags: a new stylized fact"
by A. C. Silva, R. E. Prange, and V. M. Yakovenko

Published:
Physica A 344, 227235 (2004),
PDF

Preprint:
condmat/0401225,
PDF.
Presentation:
PPT.

Abstract:
We study the probability distribution of stock returns at mesoscopic time lags (return horizons) ranging from about an hour to about a month. While at shorter microscopic time lags the distribution has powerlaw tails, for mesoscopic times the bulk of the distribution (more than 99% of the probability) follows an exponential law. The slope of the exponential function is determined by the variance of returns, which increases proportionally to the time lag. At longer times, the exponential law continuously evolves into Gaussian distribution. The exponentialtoGaussian crossover is well described by the analytical solution of the Heston model with stochastic volatility.

[2.4] "Stochastic volatility of financial markets as the fluctuating rate of
trading: an empirical study" by A. C. Silva, and V. M. Yakovenko

Published:
Physica A 382,
278–285 (2007), PDF

Preprint:
physics/0608299,
PDF.
Presentation:
PPT.

Abstract: We present an empirical study of the subordination hypothesis for a stochastic time series of a stock price. The fluctuating rate of trading is identified with the stochastic variance of the stock price, as in the continuoustime random walk (CTRW) framework. The probability distribution of the stock price changes (logreturns) for a given number of trades N is found to be approximately Gaussian. The probability distribution of N for a given time interval Dt is nonPoissonian and has an exponential tail for large N and a sharp cutoff for small N. Combining these two distributions produces a nontrivial distribution of logreturns for a given time interval Dt, which has exponential tails and a Gaussian central part, in agreement with empirical observations.
3. Reviews Papers and Books on Econophysics

[3.1] "Applications of physics to economics and finance: Money, income, wealth,
and the stock market" by A. A. Dragulescu

Posted: (2003)
condmat/0307341,
PDF.

Abstract: Ph.D. thesis in physics defended on May 15, 2002 at the
University of Maryland. It covers the papers [1.11.4, 2.1] listed above
and contains extra material. (30 pages, 30 figures)

[3.2] "Research in econophysics" by V. M. Yakovenko

Posted: (2003)
condmat/0302270,
PDF.

Abstract:
Review of econophysics research in the group of Victor Yakovenko written for the online newspaper published by the Department of Physics, University of Maryland:
The Photon, Issue 24, JanuaryFebruary 2003

[3.3] "Applications of physics to finance and economics: returns,
trading activity and income" by A. Christian Silva

Posted: (2005)
physics/0507022,
PDF.

Abstract: Ph.D. thesis in physics defended on May 10, 2005 at the University of Maryland. It covers the papers [2.22.3, 1.5] listed above and contains much additional material. (24 pages, 45 figures)

[3.4] "Econophysics, Statistical Mechanics Approach to" by V. M.
Yakovenko

Posted: (2007)
arXiv:0709.3662,
PDF.

Published: in
Encyclopedia of Complexity and System Science, edited by R. A. Meyers,
ISBN 9780387758886, Springer (2009)

Abstract: This invited review article surveys statistical models for money, wealth, and income distributions developed in the econophysics literature since late 1990s.
(24 pages, 11 figures, 144 citations)

[3.5] Book "Classical Econophysics" by A. F. Cottrell, P. Cockshott, G. J. Michaelson, I. P. Wright, and V. M.
Yakovenko

Published: Routledge (2009), ISBN 9780415478489, series Advances in Experimental and Computable Economics.

Abstract:
This monograph examines the domain of classical political economy using the methodologies developed in recent years both by the new discipline of econophysics and by computing science. This approach is used to reexamine the classical subdivisions of political economy: production, exchange, distribution and finance. Covering a combination of techniques drawn from three areas, classical political economy, theoretical computer science and econophysics, to produce models that deepen our understanding of economic reality, this new title will be of interest to higher level doctoral and research students, as well as scientists working in the field of econophysics. (384 pages)

[3.6] "Colloquium: Statistical mechanics of money, wealth, and income" by V. M.
Yakovenko and J. B. Rosser, Jr.

Published:
Reviews of Modern Physics
81, 1703 (2009),
PDF

Preprint:
arXiv:0905.1518,
PDF.
Presentation:
Viewgraphs,
Video, Audio, and Animation online.

Abstract:
The paper reviews statistical models for money, wealth, and income distributions developed in the econophysics literature since the late 1990s. By analogy with the BoltzmannGibbs distribution of energy in physics, it is shown that the probability distribution of money is exponential for certain classes of models with interacting economic agents. Alternative scenarios are also reviewed. Data analysis of the empirical distributions of wealth and income reveals a twoclass distribution. The majority of the population belongs to the lower class, characterized by the exponential ("thermal") distribution, whereas a small fraction of the population in the upper class is characterized by the powerlaw ("superthermal") distribution. The lower part is very stable, stationary in time, whereas the upper part is highly dynamical and out of equilibrium.

[3.7] "Statistical mechanics of money, debt, and energy consumption" by V. M. Yakovenko

Published: Science and Culture 76 (910), 430436 (2010),
invited paper to the
Special Issue on Econophysics.

Preprint:
arXiv:1008.2179,
PDF.

Abstract:
We briefly review statistical models for the probability distribution of money developed in the econophysics literature since the late 1990s. In these models, economic transactions are modeled as random transfers of money between the agents in payment for goods and services. We focus on conceptual foundations for this approach, on the issues of money conservation and debt, and present new results for the energy consumption distribution around the world.

[3.8] "Statistical mechanics approach to the probability distribution of money" by V. M. Yakovenko

Published: Chapter 7 in the book New Approaches to Monetary Theory: Interdisciplinary Perspectives, edited by Heiner Ganssmann, ISBN 9780415595254, Routledge (2011), pages 104123, Routledge series International Studies in Money and Banking, proceedings of the workshop Money  Interdisciplinary Perspectives, Department of Sociology, Free University of Berlin, 2527 June 2009

Preprint:
arXiv:1007.5074,
PDF.

Abstract:
This invited Chapter reviews statistical models for the probability distribution of money developed in the econophysics literature since the late 1990s. In these models, economic transactions are modeled as random transfers of money between the agents in payment for goods and services. Starting from the initially equal distribution of money, the system spontaneously develops a highly unequal distribution of money analogous to the BoltzmannGibbs distribution of energy in physics. Boundary conditions are crucial for achieving a stationary distribution. When debt is permitted, it destabilizes the system, unless some sort of limit is imposed on maximal debt.

[3.9] "Applications of statistical mechanics to economics: Entropic origin of the probability distributions of money, income, and energy consumption" by V. M. Yakovenko

Published: Chapter 4 in the book Social Fairness and Economics: Economic essays in the spirit of Duncan Foley, edited by Lance Taylor, Armon Rezai, and Thomas Michl, ISBN 9780415538190, Routledge (2013), pages 5382, Routledge series Frontiers of Political Economy, proceedings of the symposium in honor of Duncan K. Foley on occasion of his 70th birthday at the Department of Economics, New School for Social Research, New York, 2021 April 2012.

Preprint:
arXiv:1204.6483,
PDF.

Abstract:
This Chapter is written for the Festschrift celebrating the 70th birthday of the distinguished economist Duncan Foley from the New School for Social Research in New York. This Chapter reviews applications of statistical physics methods, such as the principle of entropy maximization, to the probability distributions of money, income, and global energy consumption per capita. The exponential probability distribution of wages, predicted by the statistical equilibrium theory of a labor market developed by Foley in 1996, is supported by empirical data on income distribution in the USA for the majority (about 97%) of population. In addition, the upper tail of income distribution (about 3% of population) follows a power law and expands dramatically during financial bubbles, which results in a significant increase of the overall income inequality. A mathematical analysis of the empirical data clearly demonstrates the twoclass structure of a society. Empirical data for the energy consumption per capita around the world are close to an exponential distribution, which can be also explained by the entropy maximization principle.
4. Invited Book Reviews

[4.1] Review of the book The Physics of Wall Street: A Brief History of Predicting the Unpredictable (2013) by James Owen Weatherall

Published in Physics Today 66, August 2013, p. 50

[4.2] Review of the book Econophysics of Income and Wealth Distributions (2013) by Bikas K. Chakrabarti, Anirban Chakraborti, Satya R. Chakravarty, and Arnab Chatterjee

To be published in International Review of Economics and Finance, in preparation
Presentations
 At conferences:

Applications of Physics in
Financial Analysis, 1517 July 1999, Dublin, Ireland
Proceedings:
International Journal of Theoretical and Applied Finance, Vol. 3, No. 3 (July
2000)

Applications of Physics in Financial Analysis 2, 1315 July
2000, Liege, Belgium
Proceedings:
The European Physical Journal B, Vol. 20, No. 4 (April II 2001)

NATO
Advanced Research Workshop on Application of Physics in Economic
Modelling, 810 February 2001, Prague, Czech Republic
Proceedings:
Physica A, Vol. 299, No. 12 (1 October 2001)

Scaling
Concepts and Complex Systems, 914 July 2001, Merida, Yucatan,
Mexico

21st
International Conference on Statistical Physics, 1521 July
2001, Cancún, México

Horizons in
Complex Systems, 58 December 2001, Messina, Italy

Applications of Physics in Financial
Analysis 3, 57 December 2001, London, England

Workshop on Economics and Heterogeneous Interacting Agents, 29 May  2
June 2002, the Abdus Salam International Centre for Theoretical
Physics, Trieste, Italy

International
Conference "Computing in Economics and Finance", 2630 June
2002, AixenProvence, France

International Econophysics Conference, 2831 August 2002, Bali,
Indonesia
Proceedings:
Physica A, Vol. 324, No. 12 (1 June 2003)

7th Granada
Seminar on Computational and Statistical Physics, 27
September 2002, Granada, Spain
Proceedings: Modeling of Complex
Systems: Seventh Granada Lectures,
AIP Conference Proceedings 661, New York, 2003.

Applications of Physics
in Financial Analysis 4, 1315 November 2003, Warsaw,
Poland
Proceedings:
Physica A, Vol. 344, No. 12 (1 December 2004)

9th Workshop on
Economics and Heterogeneous Interacting Agents (WEHIA2004),
2729 May 2004, Kyoto University, Japan

North American Association for Computational Social and Organizational
Science, NAACSOS Conference 2004, 2729 June 2004, Carnegie
Mellon University, Pittsburgh, PA, USA

Volatility of Financial Markets: Theoretical Models, Forecasting and
Trading, 1829 October 2004, Lorentz Center, Leiden
University, The Netherlands
Yakovenko's talk: "Thermal" and "superthermal" twoclass structure of
the personal income distribution
Silva's talk: Exponential distribution of financial returns at
mesoscopic time lags: a new stylized fact
Prange's talk: Volatility of the forecasted drift: Stocks and Options

Econophysics of Wealth Distributions, 1519 March 2005, Saha
Institute of Nuclear Physics, Kolkata, India.
Talk 1: Twoclass structure of income distribution in the USA:
exponential bulk and powerlaw tail
Talk 2: Statistical mechanics of money, income, and wealth:
foundations and applications
Proceedings: "Econophysics of Wealth Distributions", edited by
A. Chatterjee, S. Yarlagadda, and B. K. Chakrabarti (2005, Springer
series "New Economic Windows",
ISBN 8847003296).
 Interdisciplinary workshop Emergence
at the Pacific Institute of Theoretical Physics, University of British
Columbia, Vancouver, 1518 May 2005.
 Symposium on Understanding Complex
Systems, 1619 May 2005, Department of Physics, University of
Illinois at UrbanaChampaign.
Talk: "Statistical Mechanics of Money, Income, and Wealth",
slides,
audio.

11th Conference on Computing in Economics and Finance of the
Society for Computational Economics, Washington, DC, 2325 June
2005.

Econophysics Conference, Australian National University, Canberra,
Australia, 1418 November 2005.

75th Annual Meeting of the Southern Economic Association,
Washington, DC, 1820 November 2005.

International Workshop
Topological Aspects of Critical and Network Systems, Sapporo, Japan, 1314 February 2006.

Focus Session on Econophysics, March Meeting of the American Physical Society,
Baltimore, Maryland, 13 March 2006.

5th International Conference
Applications of Physics in Financial Analysis (APFA5), Turin, Italy,
29 June  1 July, 2006
Proceedings:
Physica A, Vol. 382, Issue 1, Pages 1358 (1 August 2007) and
The European Physical Journal B, Vol. 57, No. 2, Pages 121224 (May II
2007)

Conference on Fat
Tails from Finance to Fluids, Dublin, Ireland, 21  27 May 2007

ESHIA/WEHIA Conference, Center
for Social Complexity, George Mason University, Fairfax, Virginia, 18  19
June 2007

Winter Meeting on
Statistical Physics, Taxco (Guerrero), Mexico, 8  11 January 2008

Conference on Data in
Complex Systems, Palermo, Italy, 6  9 April 2008

Chairing a session at the conference Transdisciplinary Perspectives on
Economic Complexity, James Madison University, Harrisonburg, Virginia, 17 May 2008

Conference on
Probabilistic Political Economy, Kingston University, London, 14  17 July 2008

Working group Universal Diversity Patterns Across the Sciences, Santa Fe Institute, New Mexico, 24 February 2009

Econophys  Kolkata IV conference, Indian Statistical Institute, Kolkata, 9  13 March 2009
 International Symposium on Neural Networks and Econophysics: from superconducting junctions to financial markets, Department
of Physics and Business School, Loughborough University, UK,
14 June 2009

Workshop Money  Interdisciplinary Perspectives, Free University of Berlin, Germany, 25  27 June 2009

Annual Meeting of AAAS,
invited talk in the session
"What
Went Wrong with the Global Economy?", San Diego, 19 February 2010

March Meeting of the American Physical Society, Portland, Oregon, 16 March 2010

Annual Meeting of the Pacific Sociological Association, Oakland, California, 8 April 2010

Statistical Mechanics Conference, Rutgers University, Piscataway, New Jersey, 9 May 2010

General Conference of the International Association for Research in Income and Wealth (IARIW), St.Gallen, Switzerland, 26 August 2010

Mediterranean School on NanoPhysics, Marrakech, Morocco, sponsored by ICTP, Trieste, 11 December 2010

InfoMetrics across the Sciences, InfoMetrics Institute, Department of Economics, American University, Washington DC, 2 May 2011

8th International Conference on
Complex Systems, New England Complex Systems Institute, Boston, 29 June 2011

Dynamic Days Conference, Baltimore, 6 January 2012

Symposium in honor of Duncan K. Foley, Department of Economics, New School for Social Research, New York, 20 April 2012

General Conference of IARIW, Boston,
9 August 2012

Econophysics Colloquium, ETH Zurich, 11 September 2012

Annual Conference of the Society of Government Economists, George Washington University, Washington DC, 6 November 2012

Econophysics Colloquium and
Asia Pacific Econophysics Conference, Asia Pacific Center for Theoretical Physics, Pohang University of Science and Technology, Korea, 30 July 2013

Models from Statistical Mechanics in Applied Sciences, Mathematics Institute, University of Warwick, UK, 9 September 2013

Statistical modeling, financial data analysis and applications, Palazzo Franchetti, Venice, Italy, 13 September 2013
 Seminars:

University of Maryland, Condensed Matter Physics Seminar, September
1999

Oxford University, Theoretical Condensed Matter Physics Seminar, September 1999

Princeton University, Condensed Matter Physics Seminar, April
2000

University of Maryland, Seminar on Interdisciplinary Problems in
Physics and Chemistry, October 2000

Laboratoire de Physique Théorique et Modèles Statistiques, Orsay,
France, February 2001

Boston University, Condensed Matter Physics Seminar, March 2001

University of Maryland, "Foundations and Frontiers of Physics" seminar
for graduate students, April 2001

University of Maryland, Statistics Seminar, Mathematics Department,
September 2001

Santa Fe Institute, October 2001

University of Maryland,
Physics Colloquium, January 2002

University of Maryland, Statistics Seminar, Mathematics Department,
April 2002

University of Maryland, Informal Statistical
Physics Seminar, IPST, April 2002

University of Maryland, Ph. D. Defense of Adrian Dragulescu, Physics
Department, May 2002
 University of Maryland, Department of Finance, 13 September
2002

JHU Applied Physics Laboratory (Maryland),
Colloquium,
10 January 2003

George Mason University, Fairfax VA, School of Computational Sciences,
General Colloquium, 16 October 2003

Naval Research Laboratory, Washington DC, Sigma Xi Colloquium, 7
January 2004

University of Maryland, "Foundations and Frontiers of Physics" seminar
for graduate students, 9 February 2004

Monterrey Institute of Technology, Mexico, 6th International Symposium
of Physics, 26 February 2004

Kavli Institute for Theoretical Physics, University of California at
Santa Barbara,
Colloquium
(Viewgraphs, Video, and Audio online), 2 June 2004

Instituto de Fisica Teorica, Universidade Estadual Paulista (UNESP),
Sao Paulo, Brasil, Colloquium, 6 August 2004

NASA's Goddard Space Flight Center, Laboratory for Solar and Space Physics,
Greenbelt, Maryland, 16 December 2005

The Brookings Institution,
joint seminar of the Center on Social and Economic Dynamics and the
Globalization and Inequality Group, Washington, DC,
17 January 2006

Department of Economics, New School for Social Research,
New York, 17 April 2006

University of Maryland, "Foundations and Frontiers of Physics" seminar
for graduate students, 24 April 2006

University of Maryland, Third Feynman Festival,
29 August 2006

Georgetown University, Department of Physics
Colloquium,
19 September 2006

Center for Social Complexity,
George Mason University, Fairfax, Virginia, 23 March 2007

Department of Physics,
George Mason University, Fairfax, Virginia, 20 March 2008

Department of Economics, New School for Social Research, New York, 5 May 2008

Laboratoire de Physique Théorique et Modèles Statistiques, Orsay,
France, 16 October 2008

Keynote talk at the celebration the 60th anniversary of the Economics Department, Università Cattolica del Sacro Cuore, Milan, Italy, 3 November 2008

Santa Fe Institute, SFI Seminar, 15 January 2009

Center for Nonlinear Studies (CNLS) colloquium,
Los Alamos National Laboratory, 2 February 2009

Kavli Institute for Theoretical Physics, University of California at
Santa Barbara,
Colloquium
(Viewgraphs, Video, Audio, and Animation online), 13 May 2009

Seminar at the Chair of Quantitative Finance, Ecole Centrale Paris, France, 2 July 2009

Janelia Farm Research Campus of the Howard Hughes Medical Institute, Northern Virginia, 4 January 2010

University of Maryland, Physics Research Seminar for undergraduate physics majors, 6 January 2010

Indiana University at Bloomington, Seminar at the Biocomplexity Institute, 12 January 2010

Stanford University, Physics Department
Colloquium, 19 January 2010

University of Maryland, Physics
Colloquium (Viewgraphs, Video, and Audio online), 26 January 2010

Howard University, Colloquium at the Department of Physics and Astronomy, Washington DC, 31 March 2010

Johns Hopkins University, Colloquium at the Department of Physics and Astronomy, Baltimore, Maryland, 29 April 2010

Nasdaq QMX, Seminar at the Economics Research Division, Rockville, Maryland, 14 June 2010

University of North Carolina, Colloquium at the Department of Physics and Astronomy, Chapel Hill, 10 January 2011

American University, Economics Department and InfoMetrics Institute, Washington, DC, 14 September 2011

Syracuse University, Physics Department Colloquium, 19 January 2012

University of Maryland, Department of Mathematics, seminar series Aspects of Statistical Mechanics with Applications, 6 February 2012

American Center for Physics, APS MidAtlantic Senior Physicists Group, College Park, Maryland, 22 February 2012

New York University, Physics Department Colloquium, 19 April 2012

Washington University in St. Louis, Physics Department Colloquium, 2 May 2012

Johns Hopkins University, Economics and Finance Club, 26 September 2012

George Mason University, Department of Mathematical Sciences,
Applied and Computational Mathematics seminar, 19 October 2012

Catholic University of America, Physics Department Colloquium, 31 October 2012

University of California at Los Angeles (UCLA), Physics Department Colloquium, 10 January 2013

University of Florida, Gainesville, Physics Department Colloquium, 17 January 2013

University of Tennessee, Knoxville, Physics Department Colloquium, 28 January 2013

University of Maryland, Foundations and Frontiers of Physics seminar
for graduate students, 13 February 2013

Niels Bohr Institute, Copenhagen, Niels Bohr Lecture, 29 May 2013

Institute for Futures Studies, Stockholm, Research Seminar, 31 May 2013

University of Illinois at UrbanaChampaign, Physics Department Colloquium, 28 August 2013

University of Maryland, CSCAMM/KINet Seminar, 4 September 2013
Coverage in the Media

Brian Hayes, "Follow the Money",
American Scientist, v. 90, pp. 400405 (2002),
pdf

Greg Price, oped column
"Lies and
Statistics" in Australian Financial
Review newspaper, 1 March 2003, p. 51 (text)

Chapter 8.4 "The Heston model: a model with volatility fluctuations
and skew" in the book
"Theory of Financial Risk and Derivative Pricing: from Statistical Physics to
Risk Management" by JeanPhilippe Bouchaud and Marc Potters
(Cambridge University Press, 2nd edition, 2003) summarizes our paper
[2.1].

Chapter 20.3 "Origin of Heavy Tails" in the book
"Path Integrals
in Quantum Mechanics, Statistics, Polymer Physics, and Financial
Markets" by Hagen Kleinert (World Scientific, 3rd edition, 2004)
reproduces our paper [2.1].

Jenny Hogan,
"Why it is hard to share the wealth",
New Scientist, issue 2490, 12 March 2005, page 6

Christopher Shea,
"Econophysics", in a special issue "The Year in Ideas" of
The New York Times Magazine, 11 December 2005, page 67

Greg Price, oped column
"Lies and
Statistics" in Australian Financial
Review newspaper, 7 January 2006, p. 63 (text)

Steven Brush,
"Economics+Physics=Econophysics!",
The Faculty Voice, an independent faculty newspaper, University of Maryland,
Vol. 21, No. 3, March 2008, p. 5

J. R. van Meter,
"On the distribution of money and ability",
Science 2.0 Blog, 28 August 2009

Christine EvansPughe,
"We should treat money like energy...",
Engineering and Technology Magazine, volume 6, issue 6, 13 June 2011, pages 4143, publication of the UK Institution of Engineering and Technology

Phillip Schewe,
"Inequality and Investment Bubbles: A Clearer Link is Established", press release of the Joint Quantum Institute, 29 April 2012
Links

Econophysics Forum at
the Department of Physics, University of Fribourg, Switzerland.
Maintains listing of preprints in the field and has many useful links

MoneyScience portal by Jacob
Bettany, formerly the editor of "Quantitative Finance", England

Focus Sessions on Econophysics at the March Meetings of the American Physical Society:
Last updated
December 23, 2013
Home page
of Victor Yakovenko