We specify a class of non-linear and non-Gaussian models for which we estimate and forecast the conditional distributions with daily frequency. We use these forecasts to calculate VaR measures for three different equity markets (US, GB and Japan).(...)

We used neural-network based modelling to generalize the linear econometric return models and compare their out-of-sample predictive ability in terms of different performance measures under three density specifications.(...)

This paper presents a comprehensive review and comparison of five computational methods for Bayesian model selection, based on MCMC simulations from posterior model parameter distributions. We apply these methods to a well-known and important class of models in financial time series analysis,...

A computational economics model of managerial compensation is presented. Risk-averse managers are simulated, and shown to adopt more risk-taking under the influence of stock options. It is also shown that stock options can both help a new entrant compete in an established market; and can help...

The behavior of boundedly rational agents in two interacting markets is investigated. A discrete-time model of coupled financial and consumer markets is described. The integrated model consists of heterogenous consumers, financial traders, and production firms. The production firms operate in...

The behavior of boundedly rational agents in two interacting markets is investigated. A discrete-time model of coupled financial and consumer markets is described. The integrated model is then used to investigate feedback effects between the coupled markets. In particular, the influence of the...

This paper proposes a new model for studying the new product development process inan artificial environment. We show how connectionist models can be used to simulatethe adaptive nature of agents’ learning exhibiting similar behavior as practically experiencedlearning curves. We study the...

One of the central goals in finance is to find better models for pricing and hedging financial derivatives such as call and put options. We present a semi-nonparametric approach to risk-neutral density extraction from option prices which is based on an extension of the concept of mixture density...

Motivated by previous findings that discretization of financial time series can effectively filter the data and reduce the noise, this experimental study compares the trading performance of predictive models based on different modelling paradigms in a realistic setting. Different methods ranging...

In this paper we investigate the potential of the analysis of noisy non-stationary time series by quantizing it into streams of discrete symbols and applying finite-memory symbolic predictors. The main argument is that careful quantization can reduce the noise in the time series to make model...