The daily term structure of interest rates is filtered to reduce the influence of cross-correlations and autocorrelations on its factors. A three-factor model is fitted to the filtered data. We perform statistical tests, finding that factor loadings are unstable through time for daily data. This...

The article presents a Bayesian nonparametric approach to model the Pricing Kernel (PK), defined as the present value of the ratio between the risk neutral density, q, and a modified physical density, p*. The risk neutral density is estimated from option data and the modified physical density is...

The problem of market predictability can be decomposed into two parts: predictive models and predictors. At first, we show how the joint employment of model selection and machine learning models can dramatically increase our capability to forecast the equity premium out-of-sample. Secondly, we...

We present a multivariate, non-parametric technique for constructing reliable daily VaR predictions for individual assets belonging to a common equity market segment, which takes also into account the possible dependence structure between the assets and is still computationally feasible in large...

We propose a simple class of multivariate GARCH models, allowing for time-varying conditional correlations. Estimates for time-varying conditional correlations are constructed by means of a convex combination of averaged correlations (across all series) and dynamic realized (historical)...