Efficient randomized quasi-Monte Carlo methods for portfolio market risk
Yükleniyor...
Dosyalar
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
We consider the problem of simulating loss probabilities and conditional excesses for linear asset portfolios under the t-copula model. Although in the literature on market risk management there are papers proposing efficient variance reduction methods for Monte Carlo simulation of portfolio market risk, there is no paper discussing combining the randomized quasi-Monte Carlo method with variance reduction techniques. In this paper, we combine the randomized quasi-Monte Carlo method with importance sampling and stratified importance sampling. Numerical results for realistic portfolio examples suggest that replacing pseudorandom numbers (Monte Carlo) with quasi-random sequences (quasi-Monte Carlo) in the simulations increases the robustness of the estimates once we reduce the effective dimension and the impact of the non-smoothness of the integrands. (C) 2017 Elsevier B.V. All rights reserved.
Açıklama
Sak, Halis/0000-0001-9205-0619; basoglu, ismail/0000-0001-7564-0801
Anahtar Kelimeler
Risk Management, Quasi-Monte Carlo, Importance Sampling, Stratified Sampling, T-Copula
Kaynak
Insurance Mathematics & Economics
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
76
