We introduce an asset-allocation framework based on the active control of the value-at- risk of the portfolio. Within this framework, we compare two paradigms for making the allocation using neural networks. The first one uses the network to make a forecast of asset behavior, in conjunction with...

To deal with the overfitting problems that occur when there are not enough examples compared to the number of input variables in supervised learning, traditional approaches are weight decay and greedy variable selection. An alternative that has recently started to attract attention is to keep...

Metric-based methods, which use unlabeled data to detect gross differences in behavior away from the training points, have recently been introduced for model selection, often yielding very significant improvements over alternatives (including cross-validation). We introduce extensions that take...

We provide a formulation of stochastic volatility (SV) based on Gaussian process regression (GPR). Forecasting volatility out-of-sample, both simulation and empirical analyses show that our GPR-based stochastic volatility (GPSV) model clearly outperforms SV and GARCH benchmarks, especially at...

Conducted for the Treasury Board of Québec, this study seeks to present recent contributions to the evolution, within an enterprise architecture context, of de jure and de facto standards by various actors in the milieu, industrial consortia, and international standardization committees active...

The Government of Québec is slowly but surely turning its attention to the issue of free and open source software in response to the interest shown by Québec's software industry and the attention paid to the phenomenon by governments around the world. This openness is easy to understand given...