By decomposing analysts' forecast errors into common and idiosyncratic components, we develop a simple model aimed at explaining the relationship between forecast uncertainty and analyst dispersion. Under this framework, we propose a new measure of earnings forecast uncertainty as the sum of...

In this paper, Value-at-Risk (VaR) models that account for intraday-jumps are developed. The VaR is modeled directly as a quantile, the respective model-parameters are estimated by using quantile-regression. In order to analyze the dynamics of the impact of intraday-jumps on the forecasts,...

The Financial Risk Meter (FRM) is an established mechanism that, based on conditional Value at Risk (VaR) ideas, yields insight into the dynamics of network risk. Originally, the FRM has been composed via Lasso based quantile regression, but we here extend it by incorporating the idea of...

In this paper, we propose a general data-driven framework that unifies the valuation and risk measurement of financial derivatives, which is especially useful in markets with thinly-traded derivatives. We first extract the empirical characteristic function from market-observable time series for...

This paper develops an approach based on Gram-Charlier-like expansions for modeling financial series to take in due account features such as leptokurtosis. A Gram-Charlier-like expansion adjusts the moments of interest of a given distribution via its own orthogonal polynomials. This approach,...

Redenomination and default risk are key issues for European sovereign bond markets. Which of the two credit events is more likely? And which is expected to occur first? For Italy, France, Germany and Netherlands we find that redenomination abruptly rises above default risk since February 2017,...

As common practice, oil studies in the economic literature are carried out by taking the correct specification of a model as given, and ignoring the problem of estimating overly optimistic confidence sets. This means that model uncertainty is pervasive in the empirical results. In this work I...

This paper revisits the study of Cochrane (2005), to estimate the risk and returns of venture capital investments while correcting for the selection bias. We use an up-to-date dataset and enhance it to account for missing firm valuations using machine learning. The model is able to infer, with a...

Goal: ISO 31000 Risk Management (RM) recently re-defined risk as the effect of uncertainty on an organization's ability to meet the objectives. Earlier, it defined risk as a combination of the probability and scope of the (predicted) consequences. The revised ISO Risk advances beyond a static...