Results 261 to 270 of about 141,342 (308)

Value-at-risk with heavy-tailed risk factors

Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520), 2002
This paper develops methods for computationally efficient calculation of value-at-risk (VAR) in the presence of heavy-tailed risk factors. The methods model market risk factors through a multivariate t-distribution, which has both heavy tails and empirical support.
Paul Glasserman, Philip Heidelberger, Perwez Shahabuddin   +2 more
openaire   +1 more source

Vector-Valued Tail Value-at-Risk and Capital Allocation

Methodology and Computing in Applied Probability, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cossette, Hélène, Mailhot, Mélina, Marceau, Étienne, Mesfioui, Mhamed   +3 more
openaire   +1 more source

Portfolio Value‐at‐Risk with Heavy‐Tailed Risk Factors

Mathematical Finance, 2002
This paper develops efficient methods for computing portfolio value‐at‐risk (VAR) when the underlying risk factors have a heavy‐tailed distribution. In modeling heavy tails, we focus on multivariate t distributions and some extensions thereof. We develop two methods for VAR calculation that exploit a quadratic approximation to the portfolio loss, such ...
Glasserman, Paul, Heidelberger, Philip, Shahabuddin, Perwez   +2 more
openaire   +1 more source

Tail value-at-risk in uncertain random environment

Soft Computing, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yuhan Liu, Dan A. Ralescu, Chen Xiao, Waichon Lio   +3 more
openaire   +2 more sources

MULTIVARIATE GEOMETRIC TAIL- AND RANGE-VALUE-AT-RISK

ASTIN Bulletin, 2019
AbstractA generalization of range-value-at-risk (RVaR) and tail-value-at-risk (TVaR) for d-dimensional distribution functions is introduced. Properties of these new risk measures are studied and illustrated. We provide special cases, applications and a comparison with traditional univariate and multivariate versions of the TVaR and RVaR.
Klaus Herrmann, Marius Hofert, Mélina Mailhot   +2 more
openaire   +1 more source

Efficient Simulation of Value at Risk with Heavy-Tailed Risk Factors

Operations Research, 2011
Simulation of small probabilities has important applications in many disciplines. The probabilities considered in value-at-risk (VaR) are moderately small. However, the variance reduction techniques developed in the literature for VaR computation are based on large-deviations methods, which are good for very small probabilities.
Cheng-Der Fuh, Inchi Hu, Ya-Hui Hsu, Ren-Her Wang   +3 more
openaire   +2 more sources

Asymptotic subadditivity/superadditivity of Value‐at‐Risk under tail dependence

Mathematical Finance, 2023
AbstractThis paper presents a new method for discussing the asymptotic subadditivity/superadditivity of Value‐at‐Risk (VaR) for multiple risks. We consider the asymptotic subadditivity and superadditivity properties of VaR for multiple risks whose copula admits a stable tail dependence function (STDF).
Wenhao Zhu, Lujun Li, Jingping Yang, Jiehua Xie, Liulei Sun   +4 more
openaire   +1 more source

The Economic Value of Forecasting Left-Tail Risk

The Journal of Portfolio Management, 2016
The authors show that it is possible to reduce tail risk without giving up much return. The key is to forecast forward -looking skewness, which will facilitate the identification of a sweet spot for a mean–variance–skewness investor. In practice, forecasting skewness can help the popular low-volatility strategy to reduce tail risk without lowering the
James X. Xiong, Thomas M. Idzorek, Roger G. Ibbotson   +2 more
openaire   +2 more sources

Efficient Computation of Value at Risk with Heavy-Tailed Risk Factors

SSRN Electronic Journal, 2009
The probabilities considered in value-at-risk (VaR) are typically of moderate deviations. However, the variance reduction techniques developed in the literature for VaR computation are based on large deviations methods. Modeling heavy-tailed risk factors using multivariate $t$ distributions, we develop a new moderate-deviations method for VaR ...
Cheng-der Fuh, Inchi Hu, Kate Hsu, Ren-Her Wang   +3 more
openaire   +1 more source

On the Subadditivity of Tail Value at Risk: An Investigation with Copulas

Variance, 2008
In this paper, we compare the point of view of the regulator and the investors about the required solvency level of an insurance company. We assume that the required solvency level is determined using the Tail Value at Risk and analyze the diversification benefit, both on the required capital and on the residual risk, when merging risks.
S. Desmedt, J.-F. Walhin
openaire   +1 more source