Multivariate doubly truncated moments for generalized skew-elliptical distributions with applications

Baishuai Zuo, Shaoxin Wang and Chuancun Yin

Mathematical and Computer Modelling of Dynamical Systems, 2024, vol. 30, issue 1, 444-476

Abstract: In this paper, we focus on multivariate doubly truncated first two moments of generalized skew-elliptical distributions. This class of distributions includes many useful distributions, such as skew-normal, skew Student-$t$t, skew-logistic and skew-Laplace-normal distributions, as special cases. The formulas of multivariate doubly truncated covariance (MDTCov) for generalized skew-elliptical distributions are also given. Further, we compute multivariate doubly truncated expectations (MDTEs) and MDTCovs for $2$2-variate skew-normal, skew-Student-$t$t, skew-logistic and skew-Laplace-normal distributions, and use Monte-Carlo method to simulate and compare with the above results. As applications, the results of multivariate tail conditional expectation (MTCE) and multivariate tail covariance (MTCov) for generalized skew-elliptical distributions are derived. In addition, an optimal problem involving MDTE and MDTCov risk measures is proposed. Finally, we use real data to fit skew-normal distribution and to discuss MTCEs and MTCovs of logarithm of adjusted prices for two portfolios consisting of three companies from S&P (Standard & Poor’s) sectors.

Date: 2024
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DOI: 10.1080/13873954.2024.2351429

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