Integrated Portfolio Risk Measure: Estimation and Asymptotics of Multivariate Geometric Quantiles
Edward Sun,
Yu-Jen Wang and
Min-Teh Yu ()
Additional contact information
Yu-Jen Wang: National Chiao Tung University
Min-Teh Yu: China University of Technology
Computational Economics, 2018, vol. 52, issue 2, No 15, 627-652
Abstract:
Abstract Portfolio management and integrated risk management are more commonly applied toward enterprise risk management, requiring multivariate risk measures that capture the dependence among many risk factors. In this paper we propose the non-parametric estimator for multivariate value at risk (MVaR) and multivariate average value at risk (MAVaR) based on the multivariate geometric quantile approach and derive the symptotic properties of the proposed estimators for MVaR. We also present their performances under both simulated data and high-frequency financial data from the New York Stock Exchange. In addition, we compare our method with the delta normal approach and order statistics approach. The overall empirical results confirm that the multivariate geometric quantile approach significantly improves the risk management performance of MVaR and MAVaR.
Keywords: Multivariate value at risk; Multivariate average value at risk; Multivariate quantile regression; Asymptotics variance; M-estimation (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)
Downloads: (external link)
http://link.springer.com/10.1007/s10614-017-9708-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:52:y:2018:i:2:d:10.1007_s10614-017-9708-2
Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2
DOI: 10.1007/s10614-017-9708-2
Access Statistics for this article
Computational Economics is currently edited by Hans Amman
More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().