Vygintas Gontis- Dr.
- Vilnius University
Vygintas Gontis
- Dr.
- Vilnius University
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104
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Introduction
Current institution
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September 1977 - present
Education
September 1972 - June 1977
Publications
Publications (104)
Our recent analysis of empirical limit order flow data in financial markets reveals a power-law distribution in limit order cancellation times. These times are modeled using a discrete probability mass function derived from the Tsallis q-exponential distribution, closely aligned with the second form of the Pareto distribution. We elucidate this dis...
A recent analysis of empirical limit order flow data highlights the necessity for a more refined order flow model that integrates the power-law distribution of limit order cancellation times. These cancellation times follow a discrete probability mass function derived from the Tsallis $q$-exponential distribution, or equivalently, the second form o...
Modeling financial markets based on empirical data poses challenges in selecting the most appropriate models. Despite the abundance of empirical data available, researchers often face difficulties in identifying the best fitting model. Long-range memory and self-similarity estimators, commonly used for this purpose, can yield inconsistent parameter...
Identifying the best possible models based on given empirical data of observed time series is challenging. The financial markets provide us with vast empirical data, but the best model selection is still problematic for researchers. The widely used long-range memory and self-similarity estimators give varying values of the parameters as these estim...
It is a challenging task to identify the best possible models based on given empirical data of observed time series. Though the financial markets provide us with a vast amount of empirical data, the best model selection is still a big challenge for researchers. The widely used long-range memory and self-similarity estimators give varying values of...
In the face of the upcoming 30th anniversary of econophysics, we review our contributions and other related works on the modeling of the long-range memory phenomenon in physical, economic, and other social complex systems. Our group has shown that the long-range memory phenomenon can be reproduced using various Markov processes, such as point proce...
In the face of the upcoming 30th anniversary of econophysics, we review our contributions and other related works on the modeling of the long-range memory phenomenon in physical, economic, and other social complex systems. Our group has shown that the long-range memory phenomenon can be reproduced using various Markov processes, such as point proce...
It is a challenging task to identify the best possible models based on given empirical data of real stochastic time series. Though the financial markets provide us with a vast amount of empirical data, the best model selection is still a big challenge for researchers. The widely used long-range memory and self-similarity estimators give varying val...
It is empirically established that order flow in the financial markets
is positively auto-correlated and can serve as an example of a social system with long-range memory. Nevertheless, widely used long-range memory estimators give varying values of the Hurst exponent. We propose the burst and inter-burst duration statistical analysis as one more t...
It is empirically established that order flow in the financial markets is positively auto-correlated and can serve as an example of a social system with long-range memory. Nevertheless, widely used long-range memory estimators give varying values of the Hurst exponent. We propose the burst and inter-burst duration statistical analysis as one more t...
We consider models of the population or opinion dynamics which result in the non-linear stochastic differential equations (SDEs) exhibiting the spurious long-range memory. In this context, the correspondence between the description of the birth-death processes as the continuous-time Markov chains and the continuous SDEs is of high importance for th...
We propose a general method to obtain approximation of the first passage time distribution for the birth–death processes. We rely on the general properties of birth–death processes, Keilson's theorem and the concept of Riemann sum to obtain closed-form expressions. We apply the method to the three selected birth–death processes and the sophisticate...
We do consider models of the population or opinion dynamics which result in non-linear stochastic differential equations (SDEs) exhibiting spurious long-range memory. In this context, the correspondence between the description of birth-death processes as continuous-time Markov chains and continuous SDEs is of high importance for the alternatives of...
We propose a general method to obtain approximation of the first passage time distribution for the birth-death processes. We rely on the general properties of birth-death processes, Keilson's theorem and the concept of Riemann sum to obtain closed-form expressions. We apply the method to the three selected birth-death processes and the sophisticate...
It is widely accepted that there is strong persistence in financial time series. The origin of the observed persistence, or long-range memory, is still an open problem as the observed phenomenon could be a spurious effect. Earlier we have proposed the consentaneous model of the financial markets based on the non-linear stochastic differential equat...
It is widely accepted that there is strong persistence in the volatility of financial time series. The origin of the observed persistence, or long-range memory, is still an open problem as the observed phenomenon could be a spurious effect. Earlier we have proposed the consentaneous model of the financial markets based on the non-linear stochastic...
The origin of the long-range memory in the non-equilibrium systems is still an open problem as the phenomenon can be reproduced using models based on Markov processes. In these cases a notion of spurious memory is introduced. A good example of Markov processes with spurious memory is stochastic process driven by a non-linear stochastic differential...
The origin of the long-range memory in non-equilibrium systems is still an open problem as the phenomenon can be reproduced using models based on Markov processes. In these cases, the notion of spurious memory is introduced. A good example of Markov processes with spurious memory is a stochastic process driven by a non-linear stochastic differentia...
We address the problem of long-range memory in the financial markets. There are two conceptually different ways to reproduce power-law decay of auto-correlation function: using fractional Brownian motion as well as non-linear stochastic differential equations. In this contribution we address this problem by analyzing empirical return and trading ac...
We address the problem of long-range memory in the financial markets. There are two conceptually different ways to reproduce power-law decay of auto-correlation function: using fractional Brownian motion as well as non-linear stochastic differential equations. In this contribution we address this problem by analyzing empirical return and trading ac...
We address microscopic, agent based, and macroscopic, stochastic, modeling of the financial markets combining it with the exogenous noise. The interplay between the endogenous dynamics of agents and the exogenous noise is the primary mechanism responsible for the observed long-range dependence and statistical properties of high volatility return in...
We investigate the volatility return intervals in the NYSE and FOREX markets.
We explain previous empirical findings using a model based on the interacting
agent hypothesis instead of the widely-used efficient market hypothesis. We
derive macroscopic equations based on the microscopic herding interactions of
agents and find that they are able to re...
We address microscopic, agent based, and macroscopic, stochastic, modeling of the financial markets combining it with the exogenous noise. The interplay between the endogenous dynamics of agents and the exogenous noise is the primary mechanism responsible for the observed long-range dependence and statistical properties of high volatility return in...
A characteristic feature of complex systems in general is a tight coupling between their constituent parts. In complex socio-economic systems this kind of behavior leads to self-organization, which may be both desirable (e.g. social cooperation) and undesirable (e.g. mass panic, financial “bubbles” or “crashes”). Abundance of the empirical data as...
A characteristic feature of complex systems in general is a tight coupling
between their constituent parts. In complex socio-economic systems this kind of
behavior leads to self-organization, which may be both desirable (e.g. social
cooperation) and undesirable (e.g. mass panic, financial "bubbles" or
"crashes"). Abundance of the empirical data as...
We are looking for the agent-based treatment of the financial markets considering necessity to build bridges between microscopic, agent based, and macroscopic, phenomenological modeling. The acknowledgment that agent-based modeling framework, which may provide qualitative and quantitative understanding of the financial markets, is very ambiguous em...
Collective behavior of the complex socio-economic systems is heavily
influenced by the herding, group, behavior of individuals. The importance of
the herding behavior may enable the control of the collective behavior of the
individuals. In this contribution we consider a simple agent-based herding
model modified to include agents with controlled st...
We derive a system of stochastic differential equations simulating the
dynamics of the three agent groups with herding interaction. Proposed approach
can be valuable in the modeling of the complex socio-economic systems with
similar composition of the agents. We demonstrate how the sophisticated
statistical features of the absolute return in the fi...
We propose a Markov jump process with the three-state herding interaction. We
see our approach as an agent-based model for the financial markets. Under
certain assumptions this agent-based model can be related to the stochastic
description exhibiting sophisticated statistical features. Along with power-law
probability density function of the absolu...
We present examples of agent-based and stochastic models of competition and
business processes in economics and finance. We start from as simple as
possible models, which have microscopic, agent-based, versions and macroscopic
treatment in behavior. Microscopic and macroscopic versions of herding model
proposed by Kirman and Bass diffusion of new p...
We present nonlinear stochastic differential equations, generating processes with the q-exponential and q-Gaussian distributions of the observables, i.e. with the long-range power-law autocorrelations and 1/fβ power spectral density. Similarly, the Tsallis q-distributions may be obtained in the superstatistical framework as a superposition of diffe...
We extend Kirman's model by introducing variable event time scale. The proposed flexible time scale is equivalent to the variable trading activity observed in financial markets. Stochastic version of the extended Kirman's agent based model is compared to the nonlinear stochastic models of long-range memory in financial markets. The agent based mode...
We investigate large changes, bursts, of the continuous stochastic signals,
when the exponent of multiplicativity is higher than one. Earlier we have
proposed a general nonlinear stochastic model which can be transformed into
Bessel process with known first hitting (first passage) time statistics. Using
these results we derive PDF of burst duration...
Many complex systems exhibit quiet periods separated by events of rapid evolution. Such systems often produce noise with the power-law characteristics, which can be modeled in terms of avalanches. The universal nature of the power-law behavior suggests that it does not arise as a consequence of particular interaction but it is a characteristic sign...
We provide evidence that for some values of the parameters a simple agent
based model, describing herding behavior, yields signals with 1/f power
spectral density. We derive a non-linear stochastic differential equation for
the ratio of number of agents and show, that it has the form proposed earlier
for modeling of 1/f^beta noise with different ex...
Simulation serves as a third way of doing science, in contrast to both
induction and deduction. The web based modeling may considerably facilitate the
execution of simulations by other people. We present examples of agent-based
and stochastic models of competition and business processes in economics. We
start from as simple as possible models, whic...
In the last sections we introduced a double stochastic process driven by the nonlinear scaled SDE ((49)) reproducing the main statistical properties of the absolute return, observed in the financial markets. Seven parameters of the model enable us to adjust it to the sophisticated power-law statistics of various stocks including long-range behavior....
The dynamics of the {\em generalized} CEV process $dX_t = aX_t^n dt+ bX_t^m
dW_t$ $(gCEV)$ is due to an interplay of two feedback mechanisms:
State-to-Drift and State-to-Diffusion, whose degrees are $n$ and $m$
respectively. We particularly show that the gCEV, in which both feedback
mechanisms are {\sc positive}, i.e. $n,m>1$, admits a stationary p...
We scale and analyze the empirical data of return from New York and Vilnius stock exchanges matching it to the same nonlinear double stochastic model of return in financial market.
Expressions for energy and angular momentum changes of the hydrogen atom due to interaction with the electromagnetic field during the period of the electron motion in the Coulomb field are derived. It is shown that only the energy change for the motion between two subsequent passings of the pericenter is related to the quasiclassical dipole matrix...
We present a nonlinear stochastic differential equation (SDE) which mimics the probability density function (PDF) of the return and the power spectrum of the absolute return in financial markets. Absolute return as a measure of market volatility is considered in the proposed model as a long-range memory stochastic variable. The SDE is obtained from...
Multiplicative processes and multifractals have earned increased popularity in applications ranging from hydrodynamic turbulence to computer network traffic, from image processing to economics. We analyse the multifractality of the recently proposed point process models generating the signals exhibiting 1/f^b noise. The models may be used for model...
It is shown that the Poissonian-like process with slowly diffusing-like time-dependent average interevent time may be represented as the superstatistical one and exhibits 1/ f noise. The distribution of the Poissonian-like interevent time may be expressed as q-exponential distribution of the Nonextensive Statistical Mechanics.
We present and analyze the nonlinear stochastic differential equations
generating scaled signals with the power‐law statistics, including
1/f
β
noise and q‐Gaussian distribution. Numerical analysis reveals that the process exhibits some peaks, bursts or extreme events, characterized by power‐law distributions of the burst statistics and, there...
We present nonlinear stochastic differential equation (SDE) which forms the background for the stochastic modeling of return in the financial markets. SDE is obtained by the analogy with earlier proposed model of trading activity in the financial markets and generalized within the nonextensive statistical mechanics framework. Proposed stochastic mo...
We present a nonlinear stochastic differential equation (SDE) which mimics the probability density function (PDF) of the return and the power spectrum of the absolute return in financial markets. Absolute return as a measure of market volatility is considered in the proposed model as a long-range memory stochastic variable. The SDE is obtained from...
We present generalization of the point process models for the Poissonian-like processes with slowly diffusing mean interevent time and adjust the parameters of the model to the empirical data of trading activity and volatility in the financial markets, reproducing the probability density function (PDF) and the power spectral density
(PSD).
We propose the point process model as the Poissonian-like stochastic sequence with slowly diffusing mean rate and adjust the parameters of the model to the empirical data of trading activity for 26 stocks traded on NYSE. The proposed scaled stochastic differential equation provides the universal description of the trading activities with the same p...
We propose the point process model as the Poissonian-like stochastic sequence with slowly diffusing mean rate and adjust the parameters of the model to the empirical data of trading activity for 26 stocks traded on NYSE. The proposed scaled stochastic differential equation provides the universal description of the trading activities with the same p...
We propose a model of fractal point process driven by the nonlinear stochastic differential equation. The model is adjusted to the empirical data of trading activity in financial markets. This reproduces the probability distribution function and power spectral density of trading activity observed in the stock markets. We present a simple stochastic...
We consider stochastic point processes generating time series exhibiting
power laws of spectrum and distribution density (Phys. Rev. E 71, 051105
(2005)) and apply them for modeling the trading activity in the financial
markets and for the frequencies of word occurrences in the language.
We present analytical and numerical results of modeling of flows represented as correlated non-Poissonian point process and
as Poissonian sequence of pulses of different size. Both models may generate signals with power-law distributions of the intensity
of the flow and power-law spectral density. Furthermore, different distributions of the interev...
Previously we proposed the stochastic point process model, which reproduces
a variety of self-affine time series exhibiting power spectral density
S(f) scaling as a power
of the frequency f
and derived a stochastic differential equation with the same long-range memory properties.
Here we present a stochastic differential equation as a dynamical mo...
We propose a model of fractal point process driven by the nonlinear stochastic differential equation. The model is adjusted to the empirical data of trading activity in financial markets. This reproduces the probability distribution function and power spectral density of trading activity observed in the stock markets. We present a simple stochastic...
Earlier we proposed the stochastic point process model, which reproduces a variety of self-affine time series exhibiting power spectral density S(f) scaling as power of the frequency f and derived a stochastic differential equation with the same long range memory properties. Here we present a stochastic differential equation as a dynamical model of...
Starting from the developed generalized point process model of /f noise [B. Kaulakys et al., Phys. Rev. E 71 (2005) 051105] we derive the nonlinear stochastic differential equations for the signal exhibiting 1/fβ noise and 1/xλ distribution density of the signal intensity with different values of β and λ. The processes with 1/fβ are demonstrated by...
Multiplicative processes and multifractals have earned increased popularity in applications ranging from hydrodynamic turbulence to computer network traffic, from image processing to economics. We analyse the multifractality of the recently proposed point process models generating the signals exhibiting 1/f β noise. The models may be used for model...
We analyze the point processes generated by the autoregressive equation for the interevent time. The correlated point process with the Poissonian-like dis- tribution of the time between the neighboring events results in the 1/f fluctuations and the power-law distribution of the signal. This is in contrast to the white shot noise and Gaussian distri...
We present a simple model reproducing the long-range autocorrelations and the power spectrum of the web traffic. The model assumes the traffic as Poisson flow of files with size distributed according to the power-law. In this model the long-range autocorrelations are independent of the network properties as well as of inter-packet time distribution...
We present a simple model reproducing,the long-range autocorrelations and the power spectrum,of the web traffic. The model assumes,the traffic as Poisson flow of files with size distributed according,to the power-law. In this model the long-range autocorrelations are independent,of the network properties as well as of inter-packet time distribution...
We present a simple point process model of 1/f(beta) noise, covering different values of the exponent beta . The signal of the model consists of pulses or events. The interpulse, interevent, interarrival, recurrence, or waiting times of the signal are described by the general Langevin equation with the multiplicative noise and stochastically diffus...
We introduce the stochastic multiplicative point process modelling trading activity of financial markets. Such a model system exhibits power-law spectral density S(f) ~ 1/f**beta, scaled as power of frequency for various values of beta between 0.5 and 2. Furthermore, we analyze the relation between the power-law autocorrelations and the origin of t...
We introduce the stochastic multiplicative point process modelling trading activity of financial markets. Such a model system exhibits power-law spectral density S(f) ~ 1/f**beta, scaled as power of frequency for various values of beta between 0.5 and 2. Furthermore, we analyze the relation between the power-law autocorrelations and the origin of t...
We introduce the stochastic multiplicative model of time intervals between the events, defining a multiplicative point process and analyze the statistical properties of the signal. Such a model system exhibits power-law spectral density S(f)~1/f, scaled as power of frequency for various values of between 0.5 and 2. We derive explicit expressions fo...
Signals consisting of a sequence of pulses show that inherent origin of the 1/f noise is a Brownian fluctuation of the average interevent time between subsequent pulses of the pulse sequence. In this paper, we generalize the model of interevent time to reproduce a variety of self-affine time series exhibiting power spectral density S(f) scaling as...
Signals consisting of a sequence of pulses show that inherent origin of the 1/f noise is a Brownian fluctuation of the average interevent time between subsequent pulses of the pulse sequence. In this paper we generalize the model of interevent time to reproduce a variety of self-affine time series exhibiting power spectral density S(f) scaling as a...
We introduce the stochastic multiplicative model of time intervals between the events, defining multiplicative point process and analyze the statistical properties of the signal. Such a model system exhibits power-law spectral density S(f)~1/f β , scaled as power of frequency for various values of β, including β=1/2, 1 and 3/2. Explicit expressions...
Stock price change in financial market occurs through transactions in analogy with diffusion in stochastic physical systems. The analysis of price changes in real markets shows that long-range correlations of price fluctuations largely depend on the number of transactions. We introduce the multiplicative stochastic model of time interval between tr...
A simple analytically solvable model exhibiting a 1/f spectrum in an arbitrarily wide frequency range was recently proposed by Kaulakys and Meskauskas (KM). Signals consisting of a sequence of pulses show that inherent origin of the 1/f noise is Brownian fluctuations of the average intervent time between subsequent pulses of the pulse sequence. We...
The motion of an electron of a classical hydrogenic atom in an oscillating electric field is studied theoretically. An analysis is provided, based on the iterative (mapping) forms of the classical equations of motion in perturbation theory and the adiabatic approximation. This greatly facilitates the numerical investigation of stochasticity and the...
Consequences of the deviation from the linear on time quantum transition probabilities leading to the nonexponential decay law and to the so-called Zeno effect are analysed. Main features of the quantum Zeno and quantum anti-Zeno effects for induced transitions are revealed on simple model systems.
Prevention of a quantum system's time evolution by repetitive, frequent
measurements of the system's state has been called the quantum Zeno effect (or
paradox). Here we investigate theoretically and numerically the effect of
repeated measurements on the quantum dynamics of the multilevel systems that
exhibit the quantum localization of the classica...
Theoretical analysis of the recently observed ionization of Rydberg atoms by ultra-short half-cycle single-polarity electromagnetic pulses is presented. It is shown that the observations are consistent with the general classical scaling for the hydrogen atom in a microwave field, but the quantitative explanation of the results is possible only with...
Scale transformations for the classical and quantum dynamics of the hydrogen atom in a harmonic field are introduced which reduce the number of parameters, simplify the analysis of the chaotic dynamics and reveal the functional dependences of the classical and quantum processes.
The non-stationary multistep (diffusion-like) ionisation of Rydberg atoms is considered theoretically. The investigation is based on the time-dependent Fokker-Planck equation and is related to collisional and microwave ionisation of Rydberg atoms. Analytical expressions for the mean time and the higher moments of the distribution of the diffusive i...
![Fig.l. (a) Frequency-dependent excitation spectrum calculated in [14]....](profile/Vygintas-Gontis/publication/235753854/figure/fig1/AS:638820672348163@1529318125777/Figl-a-Frequency-dependent-excitation-spectrum-calculated-in-14-b_Q320.jpg)
The sensitivity of X-ray spectra to a number of typical non-equilibrium
effects occurring in modern tokamaks is examined. Experimental data from
the T-10 and ST Tokamaks are cited to illustrate the degree of deviation
from coronal equilibrium. The analysis exploits recent atomic data for
radiation and autoionization line widths; standard semiempiri...
A new mechanism for the loss of energy by hot ions and fast particles in a plasma is analyzed. This mechanism involves the excitation of multiply charged impurity ions. When deviations from the coronal equilibrium are important, the radiative loss found by including heavy particles in the calculations may be considerably greater than that calculate...
The role of excitation of impurities by heavy particles and their contribution to thermonuclear plasma radiative losses were analyzed. Excitation losses of multiply-charged impurity ions in a hot plasma was found to be a new direct heavy particle energy loss channel. The radiant energy losses due to excitation by heavy particles in a strong nonequi...
Impurity radiative loss can be a direct energy-loss mechanism for plasma ions, equally important as the corresponding mechanism for electrons. Under certain conditions the energy loss due to the excitation of impurities can also amount to a substantial fraction of the energy loss of fast heavy particles in a plasma.
Questions
Question (1)
This text follows up our recent article „Consentaneous Agent-Based and Stochastic Model of the Financial Markets“ published in open access interdisciplinary journal PLoS ONE [1]. This article is a result of the ongoing research at the Institute of Theoretical Physics and Astronomy of Vilnius University implementing the ideas of econophysics. Though our research is mostly related to the modelling of return statistics in financial markets implementing ideas from statistical physics, the concepts behind this work and conclusions are related to the much more extensive interdisciplinary understanding of the social and physical sciences. The desire to extend conventional boundaries and achieve more understanding between researchers of physical and social sciences is a strong motivation for us to deal with econophysics.
The price is a key concept in economics as it enables general quantitative description in economy and theoretical economics. Market price plays a central role as it is assumed to precisely reflect the real exchange values. Therefore a belief that market price is the most objective one lies in the background of mainstream economics, based on the rational expectation and efficient market concepts. These concepts lie in the background of huge financial industry (stock exchange, other securities, derivatives, currency exchange, etc.), making a vast impact on the overall health of the global economy. However, periodically emerging local and global economic crises give rise to the alternative views opposing mainstream concepts of economics.
Econophysics much more often than econometrics criticizes mainstream theory of economics. The observed fluctuations of the market price are larger than it should be accordingto the equilibrium view of the efficient market theory. Alternative views arise even in circles of the economists as behavioral finance and economics receive much more attention. Nobel Prize winner of 2013 professor of Yale University Robert Shiller is an outstanding representative of the behavioral alternative. The decision to award a Nobel price of economics to the most outstanding advocate of efficient market theory prof. E.F. Fama and representative of the alternative view serves as a proof that economics with its concepts is in the crossroad. From our point of view behavioral finance criticism towards econometrics and mathematical finance serves as an obstacle to positively evaluate contribution of econophysics. Nevertheless, the understanding that unstable financial and economic processes have to be considered on the bases of statistical physics is taking place [2].
From the point of view of representatives of behavioral economics and finance the behavior of agents acting in the market is much more like the behavior of realistic personalities with inherent intellectual and psychological bugs than like the behavior of extraordinarily capable individuals with ability to evaluate and account for all of the surrounding circumstances and information. For example, they pay much more attention to the tendency of imitation than to the individual capabilities of agents to make independent decisions. In our work we aim to demonstrate that it is possible to build a consentaneous model of financial markets, where choice of agents between three different trading strategies is based only on the transition probabilities between these choices. From the mathematical point of view these transition probabilities between two of the choices are the same as proposed by Alan Kirman to describe the herding interactions of agents [3]. This way we propose an adaption of the herding model to the financial markets, which can be solved by using method from statistical physics to shape macroscopic description of financial markets by the set of two stochastic differential equations. The main objective of this model is to reproduce general statistical properties of price movements observed in the real stock exchanges from Vilnius to New York.
The detailed simulation of market return statistics, reproducing power law probability density functions, power spectral densities and autocorrelations of absolute return as well as reproducing very details of these statistics, shows that herding of market participants is the most general property dominating their very heterogeneous and less meaningful rationality. We think that rationality is so heterogeneous and so ambiguous, that in the final macroscopic view of the whole agent society only the most general statistically meaningful property – herding –is observed. Rationality as very diverse can be neglected in a same way that physicists neglect trajectories of separate particles in thermodynamic consideration.
Our proposed structure of agent groups is based on a conventional choice considering three opportunities [4]: 1) intrinsic (fundamental) value oriented market traders – fundamentalists, which buy stocks, when market price is lower than fundamental value and sell when market price is higher than fundamental value; 2) speculative traders, who forecast price movement and believe that market price will go up – optimists and 3) speculative traders, who believe that market price will go down, pessimists. Permanent dynamical change of traders’ choices impacts the demand and supply ratio and so forms a long term dynamics of market price. In order to make such agent population dynamics comparable with real financial markets we had to combine it with permanent exogenous impact – external information flow or order flow noise. These are all necessary assumptions to reproduce main general properties of market price dynamics, observed for all markets and all stocks. Though the model proposed has few independent parameters, the same choice of parameter values is appropriate for all markets and all stocks is the main it‘s advantage.
Proposed model provides evidence that price dynamics can be reproduced by the memory-less Markov transitions of traders between possible choices of behavior. From our point of view the proposed model suggests new interpretation of market price, which may exhibit very large deviations from fundamental value. In this new interpretation market price highlights herding based drifts of agent-based societies, neglecting intrinsic (fundamental) understanding of value and surrendering to the imitational waves of collective wandering. Such wandering can be supported by the public tales about unexpected economic opportunities, emerging in the context of new financial, technological, social and political tendencies. As the proposed model is based on the agent opinion dynamics, we ask a question – whether the market price is economic or sociological category? Answering the question we would prefer to assume that fundamental value is more likely to be economic concept and market price is more likely to be sociological concept. To make practical distinction between different market price constituents might be a hard task, nevertheless, the new market price interpretation can be helpful looking for the opportunities to diminish observed huge market price movements, responsible for the local or global economic crises.
From our point of view, the herding as a statistically dominating behavioral property of agents can be used for the stabilization of undesirable market price fluctuations. It appears that only a small number of agents trading exceptionally based only on fundamental values is required to make a considerable influence on other market participants leading to the much more stable movement of the market price [5]. Certainly, it is obvious that for the implementation of such mechanism a new and more comprehensive understanding of fundamental price is needed. It should help to define a new reference point in economics instead of the currently used market price.
References
1. Gontis V, Kononovicius A (2014) Consentaneous Agent-Based and Stochastic Model of the Financial Markets. PLoS ONE 9(7): e102201. doi:10.1371/journal.pone.0102201
2. Castellano C, Fortunato S, Loreto V (2009) Statistical physics of social dynamics. Reviews of Modern Physics 81: 591–646. doi: 10.1103/revmodphys.81.591
3. Kirman AP (1993) Ants, rationality and recruitment. Quarterly Journal of Economics 108: 137–156. doi: 10.2307/2118498
4. Lux T, Westerhoff F (2009) Economic crysis. Nature Physics 5: 2–3. doi: 10.1038/nphys1163
5. Kononovicius A, Gontis V (2014) Control of the socio-economic systems using herding interactions. Physica A 405: 80–84. doi: 10.1016/j.physa.2014.03.003
