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In this paper we extend the original heterogeneous agent model by introducing smart traders and changes in agents' sentiment. The idea of smart traders is based on the endeavor of market agents to estimate future price movements. By adding smart traders and changes in sentiment we try to improve the original heterogeneous agents model so that it provides a(More)
In this paper, we show how the sampling properties of Hurst exponent methods of estimation change with the presence of heavy tails. We run extensive Monte Carlo simulations to find out how rescaled range analysis (R/S), multifractal detrended fluctuation analysis (M F − DF A), detrending moving average (DM A) and generalized Hurst exponent approach (GHE)(More)
In this paper we propose a new approach to estimation of the tail exponent in financial stock markets. We begin the study with the finite sample behavior of the Hill estimator under α−stable distributions. Using large Monte Carlo simulations we show that the Hill estimator overestimates the true tail exponent and can hardly be used on samples with small(More)
Extended Abstract Traditional prediction methods for time series often restrict on linear regression analysis, exponential smoothing, and ARMA. These methods generally produce reasonable prediction results for stationary random time series of linear systems. In the recent decades, development in econometrics resulted also in methods which are capable of(More)
Declaration: Hereby I claim that I elaborated this diploma thesis on my own, and that the only literature and sources I used are those listed in references. ABSTRACT In this thesis we apply neural networks as nonparametric and nonlinear methods to the Central European stock markets returns (Czech, Polish, Hungarian and German) modelling. In the first two(More)
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