Using unobservable conditional variance as measure, latentvariable approaches, such as GARCH and stochasticvolatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of highfrequency financial market data modeling realized...

The use of GARCH models with stable Paretian innovations in financial modeling has been recently suggested in the literature. This class of processes is attractive because it allows for conditional skewness and leptokurtosis of financial returns without ruling out normality. This contribution...

We examine how regularly scheduled macroeconomic announcements for the U.S., Germany and the euro area affect the German stock market, using high–frequency, minute–by–minute DAX data. Our study extends the literature on high–frequency announcement effects in several ways. First, we...

This paper introduces a Banking-Macro Model and estimates the linkages through a Multi-Regime VAR (MRVAR). We first introduce a dynamic model which is akin to the Brunnermeier and Sannikov (BS) model (2010). The banking sector borrows from capital markets, issues liabilities, accumulates assets,...

Assumptions about the dynamic and distributional behavior of risk factors are crucial for the construction of optimal portfolios and for risk assessment. Although asset returns are generally characterized by conditionally varying volatilities and fat tails, the normal distribution with constant...

Alternative strategies for predicting stock market volatility are examined. In out-of-sample forecasting experiments implied-volatility information, derived from contemporaneously observed option prices or history-based volatility predictors, such as GARCH models, are investigated, to determine...