ESTIMATION OF RISK MEASURES FROM HEAVY TAILED DISTRIBUTIONS

2021 ◽  
Vol 62 (1) ◽  
pp. 35-80
Author(s):  
El Hadji Deme ◽  
Mouhamad M. Allaya ◽  
Siradhi Deme ◽  
Hamza Dhaker ◽  
Ali Souleyman Dabye
Keyword(s):  
Risk Measures ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Estimation Of Risk

scholarly journals Estimating -Functionals for Heavy-Tailed Distributions and Application

2010 ◽  
Vol 2010 ◽  
pp. 1-34 ◽  
Author(s):  
Abdelhakim Necir ◽  
Djamel Meraghni
Keyword(s):  
Asymptotic Normality ◽  
Extreme Value Theory ◽  
Financial Risk ◽  
Risk Measures ◽  
Value Theory ◽  
Statistical Parameters ◽  
Linear Combinations ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Actuarial Risk

-functionals summarize numerous statistical parameters and actuarial risk measures. Their sample estimators are linear combinations of order statistics (-statistics). There exists a class of heavy-tailed distributions for which the asymptotic normality of these estimators cannot be obtained by classical results. In this paper we propose, by means of extreme value theory, alternative estimators for -functionals and establish their asymptotic normality. Our results may be applied to estimate the trimmed -moments and financial risk measures for heavy-tailed distributions.

scholarly journals Comparing downside risk measures for heavy tailed distributions

2006 ◽  
Vol 92 (2) ◽  
pp. 202-208 ◽  
Author(s):  
Jón Daníelsson ◽  
Bjørn N. Jorgensen ◽  
Mandira Sarma ◽  
Casper G. de Vries
Keyword(s):  
Risk Measures ◽  
Downside Risk ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed

Pitfalls in the Use of Systemic Risk Measures

2018 ◽  
Vol 53 (1) ◽  
pp. 269-298 ◽  
Author(s):  
Gunter Löffler ◽  
Peter Raupach
Keyword(s):  
Side Effects ◽  
Systemic Risk ◽  
Risk Measures ◽  
Systematic Risk ◽  
Idiosyncratic Risk ◽  
Adverse Side Effects ◽  
Heavy Tailed Distributions ◽  
Linear And Nonlinear ◽  
Heavy Tailed

We examine pitfalls in the use of return-based measures of systemic risk contributions (SRCs). For both linear and nonlinear return frameworks, assuming normal and heavy-tailed distributions, we identify nonexotic cases in which a change in a bank’s systematic risk, idiosyncratic risk, size, or contagiousness increases the risk of the system but lowers the measured SRC of the bank. Assessments based on estimated SRCs could thus produce false interpretations and incentives. We also identify potentially adverse side effects: A change in a bank’s risk structure can make the measured SRC of its competitors increase more strongly than its own.

scholarly journals Monetary risk measures for stochastic processes via Orlicz duality

2021 ◽  
Author(s):  
Christos E. Kountzakis ◽  
Damiano Rossello
Keyword(s):  
Financial Performance ◽  
Stochastic Processes ◽  
Continuous Time ◽  
Risk Measures ◽  
Cash Flows ◽  
The Other ◽  
Future Evolution ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
The One

AbstractIn this article, we extend the framework of monetary risk measures for stochastic processes to account for heavy tailed distributions of random cash flows evolving over a fixed trading horizon. To this end, we transfer the $$L^p$$ L p -duality underlying the representation of monetary risk measures to a more flexible Orlicz duality, in spaces of stochastic processes modelling random future evolution of financial values in continuous time over a finite horizon. This contributes, on the one hand, to the theory of real-valued monetary risk measures for processes and, on the other hand, supports a new representation of acceptability indices of financial performance.

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