Conditional generalized quantiles as systemic risk measures: Properties, estimation, and application

IF 4.4 2区 数学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Arief Hakim, A.N.M. Salman, Khreshna Syuhada
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引用次数: 0

Abstract

The conditional L1-quantile, or simply conditional quantile, is vital for measuring systemic risk, i.e., the risk that the distress experienced by one or more financial markets spreads to the others. One may formulate conditional quantile-based value-at-risk (CoVaR), but it depends only on the probability of loss occurrence. Alternatively, one may define conditional expectile-based value-at-risk (CoEVaR) or L2-CoVaR, but it is too sensitive and thus unrobust to extreme losses. In this paper, we aim to construct a generalized measure of systemic risk, called Lp-CoVaR, based on conditional Lp-quantiles when the conditioning risks are measured using Lp-VaR, where p1. We find that the Lp-VaR and Lp-CoVaR are coherent for all linear portfolios of elliptically distributed losses and are asymptotically coherent at high confidence level for independently and identically distributed losses with heavy right-tail. In addition, we determine their estimators and the respective asymptotic properties. In particular, we perform the Lp-CoVaR estimation using multivariate copulas, enabling us to link marginal risk models and capture their complex dependence. Our Monte Carlo simulation study demonstrates that the Lp-VaR and Lp-CoVaR estimators with 1<p<2, respectively, exhibit relatively better (conditional) coverage performance than the VaR and CoVaR as well as EVaR and CoEVaR estimators. Furthermore, our empirical study based on cryptocurrency return data with the best-fitting dependence model having heavy-tailed margins and tail dependence structures validates this result. It also confirms that the Lp-VaR and Lp-CoVaR with 1<p<2 are, respectively, not as insensitive as VaR and CoVaR and not as sensitive as EVaR and CoEVaR to an extreme loss. Their less conservativity compared to the respective VaR and CoVaR at high confidence level is practically important for determining required capital reserves.
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来源期刊
Mathematics and Computers in Simulation
Mathematics and Computers in Simulation 数学-计算机:跨学科应用
CiteScore
8.90
自引率
4.30%
发文量
335
审稿时长
54 days
期刊介绍: The aim of the journal is to provide an international forum for the dissemination of up-to-date information in the fields of the mathematics and computers, in particular (but not exclusively) as they apply to the dynamics of systems, their simulation and scientific computation in general. Published material ranges from short, concise research papers to more general tutorial articles. Mathematics and Computers in Simulation, published monthly, is the official organ of IMACS, the International Association for Mathematics and Computers in Simulation (Formerly AICA). This Association, founded in 1955 and legally incorporated in 1956 is a member of FIACC (the Five International Associations Coordinating Committee), together with IFIP, IFAV, IFORS and IMEKO. Topics covered by the journal include mathematical tools in: •The foundations of systems modelling •Numerical analysis and the development of algorithms for simulation They also include considerations about computer hardware for simulation and about special software and compilers. The journal also publishes articles concerned with specific applications of modelling and simulation in science and engineering, with relevant applied mathematics, the general philosophy of systems simulation, and their impact on disciplinary and interdisciplinary research. The journal includes a Book Review section -- and a "News on IMACS" section that contains a Calendar of future Conferences/Events and other information about the Association.
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