Distortion Risk Measures in Portfolio Optimization

Sereda, Ekaterina N.; Bronshtein, E. M.; Rachev, Svetlozar T.; Fabozzi, Frank J.; Sun, Wei; Stoyanov, Stoyan V.

doi:10.1007/978-0-387-77439-8_25

Cited by 46 publications

(38 citation statements)

References 50 publications

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“…The flexibility of this class is a reason why it has received considerable attention recently, see e.g. Wirch andHardy (1999, 2002), Cotter and Dowd (2006) who worked with the particular subclass of spectral risk measures and Sereda et al (2010), among others. The focus of our paper is to show that Wang DRMs can be nicely extended to the study of extreme risk.…”

Section: Introductionmentioning

confidence: 99%

Extreme versions of Wang risk measures and their estimation for heavy-tailed distributions

Methni¹,

Stupfler²

2017

STAT SINICA

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Abstract. Among the many possible ways to study the right tail of a real-valued random variable, a particularly general one is given by considering the family of its Wang distortion risk measures. This class of risk measures encompasses various interesting indicators, such as the widely used Value-at-Risk and Tail Value-at-Risk, which are especially popular in actuarial science, for instance. In this paper, we first build simple extreme analogues of Wang distortion risk measures and we show how this makes it possible to consider many standard measures of extreme risk, including the usual extreme Value-at-Risk or Tail-Value-at-Risk, as well as the recently introduced extreme Conditional Tail Moment, in a unified framework. We then introduce adapted estimators when the random variable of interest has a heavy-tailed distribution and we prove their asymptotic normality. The finite sample performance of our estimators is assessed on a simulation study and we showcase our techniques on two sets of real data.

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Section: Introductionmentioning

confidence: 99%

Extreme versions of Wang risk measures and their estimation for heavy-tailed distributions

Methni¹,

Stupfler²

2017

STAT SINICA

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“…For more reading on distortion risk measures, one can see Sereda et al (2010), Wu and Zhou (2006), Balbás et al (2009) and Wang et al (1997).…”

Section: Distortion Risk Measuresmentioning

confidence: 99%

Risk management under a prudential policy

Assa

2015

Decisions Econ Finan

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In this paper, we study the structure of optimal contracts in banking system when there is no risk of moral hazard. We consider a risk management problem under a policy that reduces the excessive risk-taking behavior by making all banks bear part of the risk that they transfer to other parties in the market. First, we characterize the optimal solutions to the risk management problem, and, second, we find a necessary and sufficient condition under which the "risk of the tail events" will not be transferred. In particular, we will study the problem using two known risk measures, value at risk and conditional value at risk, and will show that in these cases, the optimal solutions are in the form of stop-loss policies.

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“…For instance, the CAPM is now modelled under the Generalised Autogressive Conditional Heteroscedasticity (GARCH) approach based on the existence of volatility clustering (symmetric GARCH) and leverage effects (asymmetric GARCH). Studies stretching beyond conventional first two moments have also been considered such as Sereda et al (2010). Advanced technique from mathematical physics was also considered such as asset selection filters based on Random Matrix Theory as performed by Daly et al (2008).…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Portfolio Optimisation Strategies: Evidence from the Exchange Market

Narsoo¹

2017

IJEF

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Portfolio allocation is embedded in many decisional tasks for ensuring best returns under the constraint of minimising risk. In this paper, we implement several strategies in order to generate a holistic assessment of portfolio evaluation. The study analyses the performance of an extended framework of the classical tangency and targeted portfolio strategies. The extension is essentially the use of the skewed student-t distribution for the individual assets" log-return. Our investigation is based on 15 currencies with US dollar as the base currency for the period spanning from 1999 to 2015. A comparative performance analysis between the portfolio optimization strategies is undertaken on the basis of various performance measures, namely the portfolio expected return, standard deviation, Beta coefficient, Sharpe Ratio, Jensen"s Alpha, Treynor ratio and Roy ratio. The portfolio VaR being perceived as one of the core metrics for risk management is also computed. It is actually proxied by 5 VaR estimates -the parametric Gaussian, the equally-weighted historical VaR, the bootstrapping historical VaR, the Monte-Carlo simulation VaR and the parametric GHD VaR. The results show that both tangency portfolios, with the Gaussian or the skewed student-t distribution perform best, particularly on the basis of highest Sharpe reward-to-variability ratio and lowest Value-at-Risk.

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Distortion Risk Measures in Portfolio Optimization

Cited by 46 publications

References 50 publications

Extreme versions of Wang risk measures and their estimation for heavy-tailed distributions

Extreme versions of Wang risk measures and their estimation for heavy-tailed distributions

Risk management under a prudential policy

Performance Analysis of Portfolio Optimisation Strategies: Evidence from the Exchange Market

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