We develop a novel machine learning method to estimate large dimensional time-varying GMM models via our newly designed ridge fusion regularization scheme. Our method is a one-step procedure and allows for abrupt, smooth and dual type time variation with a fast rate of convergence. It...

This paper combines a term structure model of credit default swaps (CDS) with weak-identification robust methods to jointly estimate the probability of default and the loss given default of the underlying firm. The model is not globally identified because it forgoes parametric time series...

Knowing the entire universe of possible outcomes, even if not knowing what particular outcome has occurred, is a situation of known unknowns, and is the domain of risk, whereas not knowing the entire universe of possible outcomes is a situation of unknown unknowns, and is the domain of...

We propose a new approach to the definition of stress scenarios for volatilities and correlations. Correlations and volatilities depend on a common market factor, which is the key to stressing them in a consistent and intuitive way. Our approach is based on a new asset price model where...

We propose a new approach to the definition of stress scenarios for volatilities and correlations. Correlations and volatilities depend on a common market factor, which is the key to stressing them in a consistent and intuitive way. Our approach is based on a new asset price model where...

The influence of past stock price movements on volatilities and correlations is essential for understanding diversification and contagion in financial markets. We develop a model that makes the influence of past returns on volatilities and correlations explicit. Employing information about...

Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized measures to the latent conditional variance. A...

This study investigates the impact of the macroeconomic environment on South African industrial sector returns. Using standardized coefficients, we find that global influences are the most important drivers of returns and that industrial sectors are highly integrated with the global economy. We...

Dynamic average correlations of stock returns are predicted by the volatility of the market excess return and moving average returns of value, size and momentum portfolios. While the influence of market volatility on average correlation is well-known, the role of value, size and momentum appears...

Using filtering techniques, spectral analysis, and Markov chain models, I document trends and cycles of factors have significantly changed over the period to December 2000 compared to the period post-January 2001. The recent weaker performance of value in the 21st century, including the value...