We analyse the decline in output volatility in Germany. A lower level of variance in an autoregressive model of output growth can be either due to a change in the structure of the economy (a change in the propagation mechanism) or a reduced error term variance (reduced impulses). In Germany the...

This paper applies volatility measures and VAR spectral analytic techniques to give a thorough description of the salient business cycle characteristics of central NIPA aggregates for the G7. Furthermore, their role in contributing to the supranational G7 and EURO15 cyclic dynamics is...

We estimate a general microstructure model of the transitory and permanent impact of order flow on stock prices. Jumps are detected in both the transaction price (observation equation) and fundamental value (state equation). The model's parameters and variances are updated in real time. Prices...

This paper introduces a new class of stochastic volatility models which allows for stochastic volatility of volatility (SVV): Volatility modulated non-Gaussian Ornstein-Uhlenbeck (VMOU) processes. Various probabilistic properties of (integrated) VMOU processes are presented. Further we study the...

We propose a new semiparametric observation-driven volatility model where the form of the error density directly influences the volatility dynamics. This feature distinguishes our model from standard semiparametric GARCH models. The link between the estimated error density and the volatility...

The asset allocation decision often relies upon correlation estimates arising from short-run data. Short-run correlation estimates may, however, be distorted by frictions. In this paper, we introduce a long-run wavelet-based correlation estimator, distinguishing between long-run common behavior...

While attention is a predictor for digital asset prices, and jumps in Bitcoin prices are well-known, we know little about its alternatives. Studying high frequency crypto data gives us the unique possibility to confirm that cross market digital asset returns are driven by high frequency jumps...

This paper proposes a Skewed Stochastic Volatility (SSV) model to model time varying, asymmetric forecast distributions to estimate Growth at Risk as introduced in Adrian et al. (2019) seminal paper "Vulnerable Growth". In contrary to their semi-parametric approach, the SSV model enables...

This papers develops a dynamic factor models with regime switching to account for the decreasing volatility of the U.S. economy observed since the mid-1980s. Apart from the Markov switching capturing the cyclical fluctuations, an additional type of regime switching is introduced to allow...

This paper proposes a Skewed Stochastic Volatility (SSV) model to model time varying, asymmetric forecast distributions to estimate Growth at Risk as introduced in Adrian, Boyarchenko, and Giannone's (2019) seminal paper "Vulnerable Growth". In contrary to their semi-parametric approach, the SSV...