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...

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...

The four equity market factors from Fama and French (1993) and Carhart (1997) are perva- sive in academic empirical asset pricing studies and in applied portfolio allocation. However, the joint distributional dynamics of the factors are rarely studied. For investors basing strate- gies on the...

We consider a nonlinear vector model called the logistic vector smooth transition autoregressive model. The bivariate single-transition vector smooth transition regression model of Camacho (2004) is generalised to a multivariate and multitransition one. A modelling strategy consisting of...

We study the strategic disclosure of demand information and product-market strategies of duopolists. In a setting where firms may fail to receive information, we show that firms selectively disclose information in equilibrium in order to influence their competitor’s product-market strategy....

This paper discusses and documents G@RCH 2.0, an Ox package dedicated to the estimation and forecasting of various univariate ARCH-type models including the GARCH, EGARCH, GJR, APARCH, IGARCH, FIGARCH, FIEGARCH and FIAPARCH specifications of the conditional variance and an AR(FI)MA specification...

In this paper we consider the third-moment structure of a class of time series models. It is often argued that the marginal distribution of financial time series such as returns is skewed. Therefore it is of importance to know what properties a model should possess if it is to accommodate...