A data-driven mixture kernel for count data classification using support vector machines
In this paper, we investigate the problem of training support vector machines (SVMs) on count data. Multinomial generalized Dirichlet mixture models allow us to model efficiently count data. On the other hand, SVMs permit good discrimination. We propose, then, a hybrid model that appropriately combines their advantages. Finite mixture models are introduced, as an SVM kernel, to incorporate prior knowledge about the nature of data involved in the problem at hand. In the context of this model, we compare different kernels. Through an application involving image database categorization, we find that our data-driven kernel performs better.
Abstract This study contributes to the literature on socially responsible investing by examining the diversification potential of commodities, specifically oil, gold and clean energy together with the Brazilian Corporate Sustainability Index (ISE). Multivariate GARCH models are used to model volatility spillovers and conditional correlation in pairs of stocks containing ISE. Specifically, A-BEKK and A-DCC models with spillovers are estimated. The models’ results are used to compute and analyze the optimal weights and hedge ratios for stock portfolio holdings. The greatest benefit from diversification is obtained through the acquisition of gold and then OVX.