Trading portfolios at large financial institutions are driven by many variables. These variables are often correlated with each other and exhibit time-varying volatilities. We propose a computationally efficient Value-at-Risk (VaR) methodology based on Dynamic Factor Models (DFM) that can be applied to portfolios with time-varying weights, and that, unlike the popular Historical Simulation (HS) and Filtered Historical Simulation (FHS) methodologies, can handle time-varying volatilities and correlations for a large set of financial variables. We test the DFM-VaR on three stock portfolios that cover the 2007-2009 financial crisis, and find that it reduces the number and average size of back-testing breaches relative to HS-VaR and FHS-VaR. DFM-VaR also outperforms HS-VaR when applied risk measurement of individual stocks that are exposed to systematic risk.

Financial analysts' earnings forecasts are upwards biased with a bias that gets bigger, the longer the forecast horizon. One explanation of this bias is that it reflects asymmetric costs of positive and negative forecast errors: A positive bias may facilitate better access to companies' private information but also compromises the accuracy of analysts' forecasts. This paper proposes a simple theoretical model that relates the bias and accuracy of analysts' forecasts to the forecast horizon and studies its implications empirically.

This paper studies the asymmetric behavior of negative and positive values of analysts' earnings revisions and links it to the conservatism principle of accounting. Using a new three-state mixture of lognormal models that accounts for differences in the magnitude and persistence of positive, negative, and zero revisions, we find evidence that revisions to analysts' earnings expectations can be predicted using publicly available information such as lagged interest rates and past revisions. We also find that our forecasts of revisions to analysts' earnings estimates help to predict the actual earnings figure beyond the information contained in analysts' earnings expectations.