Abstract
We present a robust value-at-risk model that takes into account the possibility of model misspecification. In place of a single prior distribution, we utilize multiple priors in the form of an ‘uncertainty set’ around the estimated expected returns and covariance matrix, constructed using the information-theoretic notion of Kullback–Leibler divergence. An extension to conditional value-at-risk is also specified.
Notes
This article contains the current opinions of the authors and not necessarily those of PIMCO (Pacific Investment Management Company LLC). These opinions are subject to change without notice.