This paper presents the recurrent estimation of distributions (RED) for modeling real-valued data in a semiparametric fashion. RED models make two novel uses of recurrent neural networks (RNNs) for density estimation of general real-valued data. First, RNNs are used to transform input covariates into a latent space to better capture conditional dependencies in inputs. After, an RNN is used to c...

We introduce an additive partial correlation operator as an extension of partial correlation to the nonlinear setting, and use it to develop a new estimator for nonparametric graphical models. Our graphical models are based on additive conditional independence, a statistical relation that captures the spirit of conditional independence without having to resort to high-dimensional kernels for it...

This thesis mainly focuses on the problem of finding good generative models of natural images. Two main approaches are used to cope with the high dimensional problem of natural images density estimation. The first approach concentrates on developing models for small natural image patches. Mixture of elliptically contoured distributions are used and its performance is compared to some hierarchic...

This paper aims to estimate the Value-at-Risk (VaR) using GARCH type models with improved return distribution. Value at Risk (VaR) is an essential benchmark for measuring the risk of financial markets quantitatively. The parametric method, historical simulation, and Monte Carlo simulation have been proposed in several financial mathematics and engineering studies to calculate VaR, that each of ...

In the paper we compare the modelling ability of discrete-time multivariate Stochastic Volatility models to describe the conditional correlations between stock index returns. We consider four trivariate SV models, which differ in the structure of the conditional covariance matrix. Specifications with zero, constant and time-varying conditional correlations are taken into account. As an example ...

This monograph revisits and extends major empirical findings of the literature on conditional performance evaluation (CPE) for U.S. equity mutual funds. This approach allows expected performance and fund risk to vary over time with the state of the economy. We use an improved and expanded data set, a more extensive list of variables reflecting the state of the economy than in previous work, and...