Reduced-bias and partially reduced-bias mean-of-order-p value-at-risk estimation: a Monte-Carlo comparison and an application

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Gomes, MI (Author) Other; Caeiro, F (Author) Other; Fernanda Figueiredo (Author) FEP; Henriques Rodrigues, L (Author) Other; Pestana, D (Author) Other

Journal

Title: Journal of Statistical Computation and Simulation

Vol. 90 No. 10

Pages: 1735-1752

ISSN: 0094-9655

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00R-YFE

DOI: 10.1080/00949655.2020.1746787

Abstract (EN): On the basis of a sample of either independent, identically distributed or possibly weakly dependent and stationary random variables from an unknown model F with a heavy right-tail function, and for any small level q, the value-at-risk (VaR) at the level q, i.e. the size of the loss that occurs with a probability q, is estimated by new semi-parametric reduced-bias procedures based on the mean-of-order-p of a set of k quotients of upper order statistics, with p an adequate real number. After a brief reference to the asymptotic properties of these new VaR-estimators, we proceed to an overall comparison of alternative VaR-estimators, for finite samples, through large-scale Monte-Carlo simulation techniques. Possible algorithms for an adaptive VaR-estimation, an application to financial data and concluding remarks are also provided. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.

Language: English

Type (Professor's evaluation): Scientific