High-frequency financial data are characterized by a set of ubiquitous statistical properties that prevail with surprising uniformity. While these 'stylized facts' have been well-known for decades, attempts at their behavioral explanation have remained scarce. However, recently a new branch of...

In this paper we consider daily financial data from various sources (stock market indices, foreign exchange rates and bonds) and analyze their multi-scaling properties by estimating the parameters of a Markov-switching multifractal model (MSM) with Lognormal volatility components. In order to...

This dissertation contains applications of agent-based financial market models and nonlinear econometric methods in financial economics. The first part deals with the analysis of the effectiveness of currency transaction taxes within financial market models with traders with heterogeneous...

Based on a classical financial market model different model variants known from the literature are discussed and analyzed, each focussing on modeling financial markets as a nonlinear dynamic system by introducing the formation of (heterogeneous) beliefs about future asset prices into the model...

A Monte Carlo (MC) experiment is conducted to study the forecasting performance of a variety of volatility models under alternative data generating processes (DGPs). The models included in the MC study are the (Fractionally Integrated) Generalized Autoregressive Conditional Heteroskedasticity...