We estimate a unit root bilinear process using the Maximum Likelihood method with log-likelihood function constructed by means of the Kalman filter, and evaluate the finite sample properties of this estimator. One hundred and six world-wide price series are tested for unit root bilinearity...

A popular argument states that most of the diversification in a portfolio can be obtained with a rather small number of securities. In this paper we present three algorithms to approach the underlying NP-hard problem of portfolio optimization with a cardinality constraint. All three of these...

We propose an agent-based model to investigate the effects of policies against poverty – income support, workfare policies and active labor market programs within different economic and institutional setting

Some researchers and many practitioners have move from the classic mean-variance (Markowitz, 1959) portfolio theory to a new portfolio optimization framework based on downside-risk measures that are more appropriate to the investor’s preferences. Moreover, several studies (Friedman and...

This paper considers the behavior of the exchange rate in a very simple artificial currency market with two currencies and artificial agents who evolve their forecast rules over time via a genetic algorithm. I consider two simple forecast rules, one linear and the other non-linear. Under the...

In impulse response analysis the construction of intervals for the response at a particular time is a familiar topic. This paper considers the construction of confidence bands for the path of reponses. It investigates the feasibility of procedures based on heuristic optimisation methods for...

Price forecasting and trading strategies modelling are examined with major international stock indexes under different time horizons. Results demonstrate that an accurate prediction is equally important as a stable saving rate for long-term survivability. The best economic performances are...

This paper is a step towards the econometric foundation of computational intelligence in finance. Financial time series modeling and forecasting are addressed with an artificial neural network, examining issues of its topology dependency. Structural dependency of results is viewed not as a...