The paper analyses the ability of a nonlinear asset pricing model suggested by Dittmar (2002) to explain the returns on international value and growth portfolios. For comparison we use some competing pricing models; such as the ICAPM, the exchange rate risk augmented ICAPM and the international...

The paper tests the hypothesis that highly leveraged firms lose market shares to their less leveraged rivals in an industry downturn. Both parametric and semiparametric regression methods are applied to analyse the relationships between firm performance and leverage. It is found that the highly...

In this paper we show that the long-run stock and bond volatility and the long-run stock-bond correlation depend on macroeconomic uncertainty. We use the mixed data sampling (MIDAS) econometric approach. The findings are in accordance with the flight-to-quality phenomenon when macroeconomic...

This paper applies the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long-term components of the return variance. A principal component analysis is used to incorporate the information contained in...

We employ spatial econometrics techniques to investigate to what extentcountries’ economic and geographical relations affect their stock market comovements.We propose an econometric model that is particularly suitable for financial data, where common time trends prevail. In general, among the...

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...

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...