2012/03/12 by Lorenzo Hernández, Hernández, Lorenzo, Jorge Tejero +5
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Applications (stat.AP) #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #FOS: Economics and business #Probability and Risk Models #Risk Management (q-fin.RM) #Statistical Distribution Estimation and Applications #Statistical Finance (q-fin.ST) #q-fin.RM #q-fin.ST #stat.AP
paper · pdf · doi:10.48550/arxiv.1203.2564
18 pages, 20 figures
openalex publication_date 2012/03/12 · arxiv created 2012/12/18 · arxiv updated 2015/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
A perturbative approach is used to derive approximations of arbitrary order to estimate high percentiles of sums of positive independent random variables that exhibit heavy tails. Closed-form expressions for the successive approximations are obtained both when the number of terms in the sum is deterministic and when it is random. The zeroth order approximation is the percentile of the maximum term in the sum. Higher orders in the perturbative series involve the right-truncated moments of the individual random variables that appear in the sum. These censored moments are always finite. As a result, and in contrast to previous approximations proposed in the literature, the perturbative series has the same form regardless of whether these random variables have a finite mean or not. The accuracy of the approximations is illustrated for a variety of distributions and a wide range of parameters. The quality of the estimate improves as more terms are included in the perturbative series, specially for higher percentiles and heavier tails.