The study aims at simulating and forecasting a company's stock returns and prices by a fundamentalist analysis process based on a Vector Error Correction with Exogenous Variables (VECX) econometric model. To achieve this, we selected relevant fundamentalist indicators and specified a model...

The size of the equity risk premium remains an unanswered question in the accounting and finance literature. This study proposes a new approach to reverse-engineer the equity risk premium, distinct from prior research, in that it does not rely on analysts' forecasts to proxy for the market's...

We re-examine the widely held belief that analysts' earnings per share (EPS) forecasts are superior to random walk (RW) time-series forecasts. We investigate whether analysts' annual EPS forecasts are superior, and if so, under what conditions. Simple RW EPS forecasts are more accurate than...

The size of the equity risk premium remains an unanswered question in the accounting and finance literature. This study proposes a new approach to reverse-engineer the equity risk premium, distinct from prior research, in that it does not rely on analysts’ forecasts to proxy for the market’s...

We use a vector error correction model to study the long-term relationship between aggregate expected default frequency and the macroeconomic development, i.e. CPI, industry production and short-term interest rate. The model is used to forecast the median expected default frequency of the...

We propose a novel econometric model for estimating and forecasting cross-sections of time-varying conditional default probabilities. The model captures the systematic variation in corporate default counts across e.g. rating and industry groups by using dynamic factors from a large panel of...

Outlier detection in high-dimensional datasets poses new challenges that have not been investigated in the literature. In this paper, we present an integrated methodology for the identification of outliers which is suitable for datasets with higher number of variables than observations. Our...

We estimate a large Bayesian time-varying parameter vector autoregressive (TVP-VAR) model of daily stock return volatilities for 35 U.S. and European financial institutions. Based on that model we extract a connectedness index in the spirit of Diebold and Yilmaz (2014) (DYCI). We show that the...