scholarly journals A Breakthrough Idea in Risk Measure Validation – Is the Way Paved for an Effective Expected Shortfall Backtest?

2019 ◽  
Vol 18 (4) ◽  
pp. 130-145
Author(s):  
Gyöngyi Bugár
Keyword(s):  
Risk Measure ◽  
Expected Shortfall ◽  
Measure Validation ◽  
The Way

scholarly journals MEAN-VARIANCE VERSUS MEAN–EXPECTED SHORTFALL MODELS: AN APPLICATION TO WHEAT VARIETY SELECTION

2016 ◽  
Vol 48 (2) ◽  
pp. 148-172 ◽  
Author(s):  
KUNLAPATH SUKCHAROEN ◽  
DAVID LEATHAM
Keyword(s):  
Risk Measure ◽  
Management Strategies ◽  
Wheat Variety ◽  
Expected Shortfall ◽  
Wheat Varieties ◽  
Variance Model ◽  
Variety Selection ◽  
Risk Management Strategies ◽  
The Mean ◽  
Mean Variance

AbstractOne of the most popular risk management strategies for wheat producers is varietal diversification. Previous studies proposed a mean-variance model as a tool to optimally select wheat varieties. However, this study suggests that the mean–expected shortfall (ES) model (which is based on a downside risk measure) may be a better tool because variance is not a correct risk measure when the distribution of wheat variety yields is multivariate nonnormal. Results based on data from Texas Blacklands confirm our conjecture that the mean-ES framework performs better in term of selecting wheat varieties than the mean-variance method.

INSTABILITY OF PORTFOLIO OPTIMIZATION UNDER COHERENT RISK MEASURES

2010 ◽  
Vol 13 (03) ◽  
pp. 425-437 ◽  
Author(s):  
IMRE KONDOR ◽  
ISTVÁN VARGA-HASZONITS
Keyword(s):  
Portfolio Optimization ◽  
Risk Measures ◽  
Risk Measure ◽  
Expected Shortfall ◽  
Optimal Portfolio ◽  
The Other ◽  
Coherent Risk Measures ◽  
Coherent Risk ◽  
Large Samples

It is shown that the axioms for coherent risk measures imply that whenever there is a pair of portfolios such that one of them dominates the other in a given sample (which happens with finite probability even for large samples), then there is no optimal portfolio under any coherent measure on that sample, and the risk measure diverges to minus infinity. This instability was first discovered in the special example of Expected Shortfall which is used here both as an illustration and as a springboard for generalization.

scholarly journals Risk Measurement by G-Expected Shortfall

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ziting Pei ◽  
Xuhui Wang ◽  
Xingye Yue
Keyword(s):  
Explicit Formula ◽  
Empirical Analysis ◽  
Risk Measure ◽  
Computational Cost ◽  
Expected Shortfall ◽  
Risk Measurement ◽  
Worst Case ◽  
New Type ◽  
The Empirical Analysis ◽  
Low Computational Cost

G-expected shortfall (G-ES), which is a new type of worst-case expected shortfall (ES), is defined as measuring risk under infinite distributions induced by volatility uncertainty. Compared with extant notions of the worst-case ES, the G-ES can be computed using an explicit formula with low computational cost. We also conduct backtests for the G-ES. The empirical analysis demonstrates that the G-ES is a reliable risk measure.

Modified expected shortfall: a new robust coherent risk measure

2013 ◽  
Vol 16 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Deepak Jadhav ◽  
T. V. Ramanathan ◽  
U. V. Naik-Nimbalkar
Keyword(s):  
Risk Measure ◽  
Expected Shortfall ◽  
Coherent Risk Measure ◽  
Coherent Risk

A LIQUIDATION RISK ADJUSTMENT FOR VALUE AT RISK AND EXPECTED SHORTFALL

2018 ◽  
Vol 21 (03) ◽  
pp. 1850010 ◽  
Author(s):  
LAKSHITHE WAGALATH ◽  
JORGE P. ZUBELLI
Keyword(s):  
At Risk ◽  
Financial Institutions ◽  
Risk Adjustment ◽  
Value At Risk ◽  
Financial Institution ◽  
Risk Measures ◽  
Risk Measure ◽  
Expected Shortfall ◽  
Adjusted Risk ◽  
Risk Managers

This paper proposes an intuitive and flexible framework to quantify liquidation risk for financial institutions. We develop a model where the “fundamental” dynamics of assets is modified by price impacts from fund liquidations. We characterize mathematically the liquidation schedule of financial institutions and study in detail the fire sales resulting endogenously from margin constraints when a financial institution trades through an exchange. Our study enables to obtain tractable formulas for the value at risk and expected shortfall of a financial institution in the presence of fund liquidation. In particular, we find an additive decomposition for liquidation-adjusted risk measures. We show that such a measure can be expressed as a “fundamental” risk measure plus a liquidation risk adjustment that is proportional to the size of fund positions as a fraction of asset market depths. Our results can be used by risk managers in financial institutions to tackle liquidity events arising from fund liquidations better and adjust their portfolio allocations to liquidation risk more accurately.

Asymptotics for Operational Risk Quantified with Expected Shortfall

2009 ◽  
Vol 39 (2) ◽  
pp. 735-752 ◽  
Author(s):  
Francesca Biagini ◽  
Sascha Ulmer
Keyword(s):  
Confidence Level ◽  
Risk Measure ◽  
Operational Risk ◽  
Expected Shortfall ◽  
Dependence Structure ◽  
Practical Relevance ◽  
Convex Risk Measure ◽  
Multivariate Case ◽  
Univariate Case ◽  
Levy Copula

AbstractIn this paper we estimate operational risk by using the convex risk measure Expected Shortfall (ES) and provide an approximation as the confidence level converges to 100% in the univariate case. Then we extend this approach to the multivariate case, where we represent the dependence structure by using a Lévy copula as in Böcker and Klüppelberg (2006) and Böcker and Klüppelberg, C. (2008). We compare our results to the ones obtained in Böcker and Klüppelberg (2006) and (2008) for Operational VaR and discuss their practical relevance.

scholarly journals An evaluation and comparison of Value at Risk and Expected Shortfall

2018 ◽  
Vol 15 (4) ◽  
pp. 17-34 ◽  
Author(s):  
Tom Burdorf ◽  
Gary van Vuuren
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Emerging Economies ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Expected Shortfall ◽  
Significance Level ◽  
Tail Conditional Expectation ◽  
The Uk

As a risk measure, Value at Risk (VaR) is neither sub-additive nor coherent. These drawbacks have coerced regulatory authorities to introduce and mandate Expected Shortfall (ES) as a mainstream regulatory risk management metric. VaR is, however, still needed to estimate the tail conditional expectation (the ES): the average of losses that are greater than the VaR at a significance level These two risk measures behave quite differently during growth and recession periods in developed and emerging economies. Using equity portfolios assembled from securities of the banking and retail sectors in the UK and South Africa, historical, variance-covariance and Monte Carlo approaches are used to determine VaR (and hence ES). The results are back-tested and compared, and normality assumptions are tested. Key findings are that the results of the variance covariance and the Monte Carlo approach are more consistent in all environments in comparison to the historical outcomes regardless of the equity portfolio regarded. The industries and periods analysed influenced the accuracy of the risk measures; the different economies did not.

Coherent Risk Measures

2014 ◽  
pp. 115-142
Keyword(s):  
At Risk ◽  
Extreme Value Theory ◽  
Risk Measures ◽  
Risk Measure ◽  
Value Theory ◽  
Extreme Value ◽  
Expected Shortfall ◽  
Coherent Risk Measures ◽  
Coherent Risk ◽  
Distortion Risk Measure

This chapter introduces some alternative risk measures to Vale-At-Risk (VaR) calculations: Extreme Value Theory (EVT), Expected Shortfall (ES) and distortion risk measure. It also discusses their more coherent characteristics useful for shoring up the weaknesses of VaR.

An Axiomatic Foundation for the Expected Shortfall

2020 ◽  
Author(s):  
Ruodu Wang ◽  
Ričardas Zitikis
Keyword(s):  
Value At Risk ◽  
Banking Sector ◽  
Financial Regulation ◽  
Risk Measure ◽  
Expected Shortfall ◽  
Axiomatic Foundation ◽  
Coherent Risk ◽  
The Family ◽  
Economic Foundation ◽  
Tail Events

In the recent Basel Accords, the expected shortfall (ES) replaces the value-at-risk (VaR) as the standard risk measure for market risk in the banking sector, making it the most popular risk measure in financial regulation. Although ES is—in addition to many other nice properties—a coherent risk measure, it does not yet have an axiomatic foundation. In this paper, we put forward four intuitive economic axioms for portfolio risk assessment—monotonicity, law invariance, prudence, and no reward for concentration—that uniquely characterize the family of ES. Therefore, the results developed herein provide the first economic foundation for using ES as a globally dominating regulatory risk measure, currently employed in Basel III/IV. Key to the main results, several novel notions such as tail events and risk concentration naturally arise, and we explore them in detail. As a most important feature, ES rewards portfolio diversification and penalizes risk concentration in a special and intuitive way, not shared by any other risk measure. This paper was accepted by Manel Baucells, decision analysis.

Sign in / Sign up

Export Citation Format

Share Document