Asymptotic Analysis for Spectral Risk Measures Parameterized by Confidence Level

Abstract

We study the asymptotic behavior of the difference $\Delta \rho ^{X, Y}_\alpha := \rho _\alpha (X + Y) - \rho _\alpha (X)$ as $\alpha \rightarrow 1$, where $\rho_\alpha $ is a risk measure equipped with a confidence level parameter $0 < \alpha < 1$, and where $X$ and $Y$ are non-negative random variables whose tail probability functions are regularly varying. The case where $\rho _\alpha $ is the value-at-risk (VaR) at $\alpha $, is treated in Kato (2017). This paper investigates the case where $\rho _\alpha $ is a spectral risk measure that converges to the worst-case risk measure as $\alpha \rightarrow 1$. We give the asymptotic behavior of the difference between the marginal risk contribution and the Euler contribution of $Y$ to the portfolio $X + Y$. Similarly to Kato (2017), our results depend primarily on the relative magnitudes of the thicknesses of the tails of $X$ and $Y$. We also conducted a numerical experiment, finding that when the tail of $X$ is sufficiently thicker than that of $Y$, $\Delta \rho ^{X, Y}_\alpha $ does not increase monotonically with $\alpha$ and takes a maximum at a confidence level strictly less than $1$.