Model-based and empirical analyses of stochastic fluctuations in economy and finance

TL;DR

This paper explores the complexity and stochastic behavior of economic and financial systems using statistical mechanics and information theory, focusing on modeling, empirical analysis, and probability distribution functions.

Contribution

It introduces new methods for deriving probability distribution functions and analyzing stochastic fluctuations in economic and financial systems.

Findings

Analysis of stochastic fluctuations in finance and economy

Development of models for probability distribution functions

Empirical validation of theoretical approaches

Abstract

The objective of this work is the investigation of complexity, asymmetry, stochasticity and non-linearity of the financial and economic systems by using the tools of statistical mechanics and information theory. More precisely, this thesis concerns statistical-based modeling and empirical analyses with applications in finance, forecasting, production processes and game theory. In these areas the time dependence of probability distributions is of prime interest and can be measured or exactly calculated for model systems. The correlation coefficients and moments are among the useful quantities to describe the dynamics and the correlations between random variables. However, the full investigation can only be achieved if the probability distribution function of the variable is known; its derivation is one of the main focuses of the present work.