We assess the bias and the efficiency of state-of-the-art dynamic panel data estimators by means of model-based Monte Carlo simulations. The underlying data-generating process consists of a standard theoretical growth model of income convergence based on capital accumulation. While we impose a...

In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap proach which is easily implemented in empirical...

The recently developed rough Bergomi (rBergomi) model is a rough fractional stochastic volatility (RFSV) model which can generate more realistic term structure of at-the-money volatility skews compared with other RFSV models. However, its non-Markovianity brings mathematical and computational...

In this paper we investigate the forecast performance of nonlinear error-correction models with regime switching. In particular, we focus on threshold and Markov-switching error-correction models where adjustment towards long-run equilibrium is nonlinear and discontinuous. Our simulation study...

This paper examines continuous-time models for the S&P 100 index and its constituents. We find that the jump process of the typical stock looks significantly different than that of the index. Most importantly, the average size of a jumps in the returns of the typical stock is positive, while it...

We demonstrate the estimation biases that arise when stock returns from 12 month prior and 2 month prior are included within intermediate and recent past momentum profits. These biases lead to an overestimation of intermediate past momentum but an underestimation of recent past momentum in the...