Next Article in Journal
A Genetic Algorithm for Investment–Consumption Optimization with Value-at-Risk Constraint and Information-Processing Cost
Previous Article in Journal
Model-Free Stochastic Collocation for an Arbitrage-Free Implied Volatility, Part II
Open AccessArticle

On Double Value at Risk

1
School of Science, Nanjing University of Science and Technology, Nanjing 210094, China
2
Securities Co., Ltd., Beijing 102627, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Risks 2019, 7(1), 31; https://doi.org/10.3390/risks7010031
Received: 10 February 2019 / Revised: 2 March 2019 / Accepted: 5 March 2019 / Published: 8 March 2019
Value at Risk (VaR) is used to illustrate the maximum potential loss under a given confidence level, and is just a single indicator to evaluate risk ignoring any information about income. The present paper will generalize one-dimensional VaR to two-dimensional VaR with income-risk double indicators. We first construct a double-VaR with ( μ , σ 2 ) (or ( μ , V a R 2 ) ) indicators, and deduce the joint confidence region of ( μ , σ 2 ) (or ( μ , V a R 2 ) ) by virtue of the two-dimensional likelihood ratio method. Finally, an example to cover the empirical analysis of two double-VaR models is stated. View Full-Text
Keywords: double-VaR; joint confidence region; (μ,VaR2) double-VaR; joint confidence region; (μ,VaR2)
Show Figures

Figure 1

MDPI and ACS Style

Zhang, W.; Zhang, S.; Zhao, P. On Double Value at Risk. Risks 2019, 7, 31.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop