We adapt the continuous-time random walk formalism to describe asset price evolution. We expand the idea proposed by Rachev and Rűschendorf who analyzed the binomial pricing model in the discrete time with randomization of the number of price changes. As a result, in the framework of the proposed model we obtain a mixture of the Gaussian and a generalized arcsine laws as the limiting distribution of log-returns. Moreover, we derive an European-call-option price that is an extension of the Black-Scholes formula. We apply the obtained theoretical results to model actual financial data and try to show that the continuous-time random walk offers alternative tools to deal with several complex issues of financial markets.
A new model is formulated of the sociological effect of the spiral of silence, introduced by Elisabeth Noelle-Neumann in 1974. The probability that a new opinion is openly expressed decreases with the difference between this new opinion and the perceived opinion of the majority. We also assume that the system is open, i.e. some people enter and some leave during the process of the opinion formation. An influence of a leader is simulated by a comparison of two runs of the simulation, where the leader has different opinion in each run. The difference of the mean expressed opinions in these two runs persists long after the leader's leave.
Two models of a queue are proposed: a human queue and two lines of vehicles before a narrowing. In both models, a queuer tries to evaluate his waiting time, taking into account the delay caused by intruders who jump to the queue front. As the collected statistics of such events is very limited, the evaluation can give very long times. The results provide an example, when direct observations should be supplemented by an inference from the context.
Student's t-distribution is compared to a solution of superdiffusion equation. This t-distribution is a continuous probability distribution that arises in the problem of estimating the mean of a normally distributed population when the sample size is small. Formally it can written in the form similar to the Gaussian distribution, in which, however, instead of usual exponential function, the so called K-exponential - a form of binomial distribution - appears. Similar binomial form has the Zeldovich-Kompaneets solution of nonlinear diffusion-like problems. A superdiffusion process, similar to a Zeldovich-Kompaneets heat conduction process, is defined by a nonlinear diffusion equation in which the diffusion coefficient takes the form $D=a(t)(1/f)^n$, where a=a(t) is an external time modulation, n is a positive constant, and f=f(x,t) is a solution to the nonlinear diffusion equation. It is also shown that a Zeldovich-Kompaneets solution still satisfies the superdiffusion equation if a=a(t) is replaced by the mean value of a. A solution to the superdiffusion equation is given. This may be useful in description of social, financial, and biological processes. In particular, the solution possesses a fat tail character that is similar to probability distributions observed at stock markets. The limitation of the analogy with the Student distribution is also indicated.
Simple model of share price evolution, which is an extension of Kehr-Kutner-Binder one and Montero-Masoliver models, is presented. The market empirical data inspired the assumptions of the model. The model seems to be the reference one for the study of the short-range correlations in financial data as it considers the observed correlation over two successive jumps of the financial ant.
In this work we empirically verify the generic breaking of the Central Limit Theorem on the financial and commodity markets. We analysed the distributions of log-returns for typical indices and price of gold, for increasing time horizons. We considered Random Coarse Graining Transformation of the Continuous-Time Random Walk model, which can represent the non-Gaussian price dynamics of underlying assets and the corresponding derivatives, e.g., various options or future contracts. We confirmed that empirical data and predictions of the model quite well agree.
We study crash dynamics of the Warsaw Stock Exchange by using minimal spanning tree networks. We identify the transition of the complex network during its evolution from a (hierarchical) power law minimal spanning tree network - representing the stable state of Warsaw Stock Exchange before the recent worldwide financial crash, to a superstar-like (or superhub) minimal spanning tree network of the market decorated by a hierarchy of trees - an unstable, intermediate market state. Subsequently, we observe a transition from this complex tree to the topology of the (hierarchical) power law minimal spanning tree network decorated by several star-like trees or hubs - this structure and topology represent the Warsaw Stock Exchange after the worldwide financial crash, and can be considered to be an aftershock. Our results can serve as an empirical foundation for a future theory of dynamic structural and topological phase transitions on financial markets.
We examine deviations from Boltzmann-Gibbs statistics for a certain class of partially equilibrated systems of finite size. We find that such systems are characterized by the Lévy distribution whose non-extensivity parameter is related to the number of internally equilibrated subsystems and to correlations among them. This concept is applicable to relativistic heavy ion collisions.
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