3

Let's derive a possible approach from utility theory. Our investor is risk averse and exhibits CARA utility using an exponential utility function with risk aversion parameter $\gamma>0$ (risk averse agent): $$u(x)=\frac{1-e^{-\gamma x}}{\gamma}$$ A 3rd order Taylor series expansion around $x=0$ yields \begin{align} u(x)\approx& x - \frac{1}{2}\gamma ...


2

Instead of starting from a CARA utility function like how the other answer does, an alternative for incorporating portfolio skewness in the mean-variance model's objective function, without risk-aversion parameter $\gamma$ or going through a Taylor series expansion of some arbitrarily asserted utility function, could be $$\arg \max_w \enspace w^T\mu-\frac{1}...


2

The problem is not the sum $L + L_1$ but the question whether your $L_1$ is really a good model for whatever you might be missing in $L$. I personally (and maybe also some regulators) would regard losses always equal to 10K and completely independent from everything else not to be a good model for the low frequency high severity events typically missing from ...


1

Taken from my experience as a trader I would suggest there are two parameters that comprise OperationalRisk: 1) A distribution of the size of losses due to the event, 2) A distribution of the frequency of events. I suspect a Poission distribution is fine to use to predict the frequency. Empirically this would tally with my experience. Secondly, with regard ...


1

All the big factor risk model providers do macro scenario analysis as part of their suite of products. From the point of view of portfolio management, these sorts of products are ideal for putting your own portfolios through what-if scenarios. For example, MSCI Barra does a macroeconomic factor model described here. Part of their sales pitch includes a "...


Only top voted, non community-wiki answers of a minimum length are eligible