We try to show the danger of confusing the concept of volatility with that of the standard deviation of a probability distribution. We work in the theoretical Black-Scholes model to give an explicit relationship between the two measures. We apply and then illustrate this relationship, firstly in...

Robustness of risk measures to changes in underlying loss distributions (distributional uncertainty) is of crucial importance when making well-informed risk management decisions. In this paper, we quantify for any given distortion risk measure its robustness to distributional uncertainty by...

This article applies a two-step conditional Bayesian approach to hedge fund risk. First, a mixture or-two normal distributions is estimated for a core asset; one distribution being identified as linked to a "quiet" regime and the other to a "hectic" regime. The conditional probabilities of each...

This article applies a two-step conditional Bayesian approach to hedge fund risk. First, a mixture or-two normal distributions is estimated for a core asset; one distribution being identified as linked to a "quiet" regime and the other to a "hectic" regime. The conditional probabilities of each...

An asset pricing model is customarily evaluated by how well it explains means of returns. But how well the model explains fluctuations of returns is similarly important though often overlooked in the literature. We derive “efficient” factors that combine both objectives and turn out to...

We seek fundamental risks from news text. Conceptually, news is closely related to the idea of systematic risk, in particular the "state variables" in the ICAPM. News captures investors' concerns about future investment opportunities, and hence drives the current pricing kernel. This paper...

Motivated by multi-dimensional sources of systemic risk in the economy and the low frequency nature of the observed empirical proxies for some of these, we propose a two-stage learning procedure to construct one better high-frequency (i.e., daily) systemic risk factor. In the first stage, we use...

It is well known that Markowitz Portfolio Optimization often leads to unreasonable and unbalanced portfolios that are optimal in-sample but perform very poorly out-of-sample. There is a strong relationship between these poor returns and the fact that covariance matrices that are used within the...