In this paper, we perform a robust analysis of the determinants of US swap spreads using a wide range of theoretically motivated candidate factors. We conduct an analysis in the frequency domain to see how the impacts of the candidate factors on the swap spread differ between different horizons....

In this paper, we perform a robust analysis of the determinants of US swap spreads using a wide range of theoretically motivated candidate factors. We conduct an analysis in the frequency domain to see how the impacts of the candidate factors on the swap spread differ between different horizons....

In this paper, we perform a robust analysis of the determinants of US swap spreads using a wide range of theoretically motivated candidate factors. We conduct an analysis in the frequency domain to see how the impacts of the candidate factors on the swap spread differ between different horizons....

We propose a bivariate component GARCH-MIDAS model to estimate the long- and short-run components of the variances and covariances. The advantage of our model to the existing DCC-based models is that it uses the same form for both the variances and covariances and that it estimates these moments...

This paper analyses the ability of beta and other factors, like firm size and book-to-market, to explain cross-sectional variation in average stock returns on the Swedish stock market for the period 1980-1990. We correct for errors in variables problem of the estimated market beta. Since this...

Our aim is to give a comparative analysis of ability of different factor mimicking portfolios in representing the background factors. Our analysis contains a cross-sectional regression approach, a time-series regression approach and a portfolio approach for constructing factor mimicking...

We investigate the long-run stock-bond correlation using a novel model that combines the dynamic conditional correlation model with the mixed-data sampling approach. The long-run correlation is affected by both macro-finance variables (historical and forecasts) and the lagged realized...

Current models for predicting volatility do not incorporate information flow and are solely based on historical volatilities. We suggest a method to quantify the semantic content of words in news articles about a company and use this as a predictor of its stock volatility. The results show that...