The assessment of models of financial market behaviour requires evaluation tools. When complexity hinders a direct estimation approach, e.g., for agent based microsimulation models or multifractal models, simulation based estimators might provide an alternative. In order to apply such...

We continue our research into the Quasi Monte Carlo simulation method and its application in macroeconometrics. Prior research indicated Quasi Monte Carlo simulation to be superior to traditional Monte Carlo methods. Now we address additional important issues including the robustness of the...

It is common practice in econometrics to base a model to be applied to data on pure theory, and yet to replace the variables of the pure theory by aggregates of them. But if one must aggregate, there are many alternative ways of doing so; we present an approach using heuristic optimization for...

The purpose of this paper is to compare the use of quasi-Monte Carlo methods, in particular the so--called $(t,m,s)-nets$ technique, versus classical Monte Carlo approaches for the simulation of econometric time series models. Some theoretic results indicate the superiority of quasi-Monte Carlo...

For modelling economic and financial time series, multivariate linear and nonlinear systems of equations have become a standard tool. These models can also be applied to non-stationary processes. However, the resulting finite-sample estimates may depend strongly on the specification of the model...

Agent based models take into account limited rational behaviour of individuals acting on financial markets. Explicit simulation of this behaviour and the resulting interaction of individuals provide a description of aggregate financial market time series. At least for some parameter settings,...