"We present a framework for constructing multivariate risk measures that is inspired from univariate Optimized Certainty Equivalent (OCE) risk measures. We show that this new class of risk measures verifies the desirable properties such as convexity, monotonocity and cash invariance. We also address numerical aspects of their computations using stochastic algorithms instead of using Monte Carlo or Fourier methods that do not provide any error of the estimation."
top of page
Rechercher
Posts récents
Voir tout“As analysts are primary recipients of these reports, we investigate whether and how analyst forecast properties have changed following...
00
This study proposes a new method for detecting insider trading. The method combines principal component analysis (PCA) with random forest...
10
Cyber risk classifications often fail in out-of-sample forecasting despite their in-sample fit. Dynamic, impact-based classifiers...
40
bottom of page
Comments