This paper documents that factors extracted from a large set of macroeconomic variables bear useful information for predicting monthly US excess stock returns and volatility over the period 1980-2005. Factor-augmented predictive regression models improve upon both benchmark models that only...

This paper investigates the determinants of credit spread changes on bonds denominated in euro. The analysis is carried out using a panel data on euro bonds. We try to asses the relative importance of market and idiosyncratic factors in explaining the movements in credit spread. Because credit...

When the yield curve is modelled using an affine factor model, residuals may still contain relevant information and do not adhere to the familiar white noise assumption.This paper proposes a pragmatic way to improve out of sample performance for yield curve forecasting. The proposed adjustment...

Most multivariate variance or volatility models suffer from a common problem, the “curse of dimensionality”. For this reason, most are fitted under strong parametric restrictions that reduce the interpretation and flexibility of the models. Recently, the literature has focused on...

We propose a new model for volatility forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the GARCH model. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the common and the idiosyncratic part of each series of returns....

In this paper, we address the question whether the impact of default risk on equity returns depends on the financial system firms operate in. Using an implementation of Merton's option-pricing model for the value of equity to estimate firms' default risk, we construct a factor that measures the...

We propose several connectedness measures built from pieces of variance decompositions, and we argue that they provide natural and insightful measures of connectedness among financial asset returns and volatilities. We also show that variance decompositions define weighted, directed networks, so...

We develop likelihood-based tests for autocorrelation and predictability in a first order non-Gaussian and noninvertible ARMA model. Tests based on a special case of the general model, referred to as an all-pass model, are also obtained. Data generated by an all-pass process are uncorrelated...

This paper examines properties of mean-variance inefficient proxies with respect to producing a linear relation between expected returns and betas. The numerical results of a Monte Carlo simulation show that in the CAPM slightly inefficient, positively weighted proxies cause an almost perfect...