scholarly journals Non-Parametric Robust Model Risk Measurement with Path-Dependent Loss Functions

2020 ◽  
Author(s):  
Yu Feng
Keyword(s):  
Risk Measurement ◽  
Loss Functions ◽  
Model Risk ◽  
Robust Model ◽  
Path Dependent ◽  
Non Parametric

scholarly journals Robustness in the Optimization of Risk Measures

2021 ◽  
Author(s):  
Paul Embrechts ◽  
Alexander Schied ◽  
Ruodu Wang
Keyword(s):  
Value At Risk ◽  
Optimization Problem ◽  
Financial Regulation ◽  
Optimization Problems ◽  
Risk Measures ◽  
Risk Measurement ◽  
Loss Functions ◽  
Insurance Regulation ◽  
Convex Risk Measures ◽  
Comparative Advantages

We study issues of robustness in the context of Quantitative Risk Management and Optimization. We develop a general methodology for determining whether a given risk-measurement-related optimization problem is robust, which we call “robustness against optimization.” The new notion is studied for various classes of risk measures and expected utility and loss functions. Motivated by practical issues from financial regulation, special attention is given to the two most widely used risk measures in the industry, Value-at-Risk (VaR) and Expected Shortfall (ES). We establish that for a class of general optimization problems, VaR leads to nonrobust optimizers, whereas convex risk measures generally lead to robust ones. Our results offer extra insight on the ongoing discussion about the comparative advantages of VaR and ES in banking and insurance regulation. Our notion of robustness is conceptually different from the field of robust optimization, to which some interesting links are derived.

Robust risk measurement and model risk

2013 ◽  
Vol 14 (1) ◽  
pp. 29-58 ◽  
Author(s):  
Paul Glasserman ◽  
Xingbo Xu
Keyword(s):  
Risk Measurement ◽  
Model Risk

scholarly journals EVALUATING THE ACCURACY OF TAIL RISK FORECASTS FOR SYSTEMIC RISK MEASUREMENT

2018 ◽  
Vol 13 (02) ◽  
pp. 1850009 ◽  
Author(s):  
CHRISTIAN BROWNLEES ◽  
GIUSEPPE CAVALIERE ◽  
ALICE MONTI
Keyword(s):  
Mean Square Error ◽  
Financial Institutions ◽  
Loss Function ◽  
Systemic Risk ◽  
Value At Risk ◽  
Risk Measurement ◽  
Loss Functions ◽  
Conditional Value At Risk ◽  
Mean Square ◽  
Tail Risk

In this paper, we address how to evaluate tail risk forecasts for systemic risk (SRISK) measurement. We propose two loss functions, the Tail Tick Loss and the Tail Mean Square Error, to evaluate, respectively, Conditional Value-at-Risk (CoVaR) and MES forecasts. We then analyse CoVaR and MES forecasts for a panel of top US financial institutions between 2000 and 2012 constructed using a set of bivariate DCC-GARCH-type models. The empirical results highlight the importance of using an appropriate loss function for the evaluation of such forecasts. Among other findings, the analysis confirms that the DCC-GJR specification provides accurate predictions for both CoVaR and MES, in particular for the riskiest group of institutions in the panel (Broker-Dealers).

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