We develop a conditional factor model for the term structure of treasury bonds, which unifies non parametric curve estimation with cross-sectional asset pricing. Our factors correspond to the optimal non-parametric basis functions spanning the discount curve. They are investable portfolios...

We model the dynamics of asset prices and associated derivatives by consideration of the dynamics of the conditional probability density process for the value of an asset at some specified time in the future. In the case where the asset is driven by Brownian motion, an associated "master...

This study deals with the dynamic hedging of single-tranche collateralized debt obligations (STCDOs). As a first step, we specify a top-down affine factor model in which a catastrophic risk component is incorporated in order to capture the dynamics of super-senior tranches. Next, we derive the...

We propose an affi ne two-factor model for the pricing of single-tranche collateralized debt obligations by following the general top-down framework introduced in Filipovic et al. [2011]. Apart from being analytically tractable, this model has the feature that it incorporates a catastrophic risk...

We introduce closed-form transition density expansions for multivariate affine jump-diffusion processes. The expansions rely on a general approximation theory which we develop in weighted Hilbert spaces for random variables which possess all polynomial moments. We establish parametric conditions...

Polynomial processes have the property that expectations of polynomial functions (of degree n, say) of the future state of the process conditional on the current state are given by polynomials (of degree n) of the current state. Here we explore the application of polynomial processes in the...

We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and...