In this paper an alternative non-parametric historical simulation approach, the Mixing Unconditional Disturbances model with constant volatility, where price paths are generated by reshuffling disturbances for S&P 500 Index returns over the period 1950 - 1998, is used to estimate a Generalized...

This paper aims to determine which extreme value copula is best suited to the bivariate relationships between shocks of U.S market with Brazilian, Argentine and Mexican markets. We used prices of S&P500, Ibovespa, Merval and IPC from January, 3, 2009 to December, 31, 2010, totaling 483...

We analyze the properties of multiperiod forecasts which are formulated by a number of companies for a fixed horizon ahead which moves each month one period closer and are collected and diffused each month by some polling agency. Some descriptive evidence and a formal model suggest that knowing...

A dynamic microeconomic model is presented that establishes the price and unit sales evolution of heterogeneous goods consisting of successive homogenous product generations. It suggests that for a fast growing supply the mean price of the generations are governed by a logistic decline towards a...

This is an introductory article which explains the importance of explicit consideration and modeling of causality, contrary to current econometric practice, in order to use data set for extraction of meaningful information. One of the easiest to understand approaches to causality is via...

This paper presents a study of a coordinated production inventory-system. In the proposed model, any echelon considers its successors as part of its inventory system and generates the replenishment order on the basis of operational information of its partners. We show that the coordinated...

Textbooks on Design of Experiments invariably start by explaining why one-factor-at-a-time (OAT) is an inferior method. Here we will show that in a model with all interactions a variant of OAT is extremely efficient, provided that we only have non-negative parameters and that there are only a...

Monte Carlo methods are simulation algorithms to estimate a numerical quantity in a statistical model of a real system. These algorithms are executed by computer programs. Variance reduction techniques (VRT) are needed, even though computer speed has been increasing dramatically, ever since the...

This paper introduces the class of volatility modulated Lévy-driven Volterra (VMLV) processes and their important subclass of Lévy semistationary (LSS) processes as a new framework for modelling energy spot prices. The main modelling idea consists of four principles: First, deseasonalised spot...

Optimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a `robust' methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by...