Related Experiment Video
Updated: May 10, 2026
13:04
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Published on: September 19, 2012
Beyond Value-at-Risk: GlueVaR Distortion Risk Measures
Jaume Belles-Sampera1, Montserrat Guillén1, Miguel Santolino1
1Department of Econometrics, Riskcenter-IREA, University of Barcelona, Barcelona, Spain.
Summary
We introduce GlueVaR, a novel risk measure. This study details its mathematical properties, compares it to existing measures like Value-at-Risk, and explores its use in finance and beyond.
Area of Science:
- Quantitative Finance
- Risk Management
- Actuarial Science
Background:
- Distortion risk measures are widely used in finance and insurance.
- Existing measures like Value-at-Risk (VaR) and Tail Value-at-Risk (TVaR) have limitations.
- There is a need for robust and versatile risk measurement tools.
Purpose of the Study:
- Introduce a new family of risk measures, GlueVaR.
- Provide analytical expressions for common financial and insurance distributions.
- Analyze the properties and applicability of GlueVaR.
Main Methods:
- Developed analytical closed-form expressions for GlueVaR.
- Investigated the relationship between GlueVaR, VaR, and TVaR.
- Examined tail subadditivity properties of GlueVaR.
Main Results:
- Derived closed-form expressions for GlueVaR with standard distributions.
- Clarified the connections between GlueVaR, VaR, and TVaR.
- Demonstrated that certain GlueVaR measures exhibit tail subadditivity.
Conclusions:
- GlueVaR offers a flexible and mathematically tractable alternative to existing risk measures.
- The properties of GlueVaR support its application in financial and insurance contexts.
- GlueVaR shows potential for broader applications in non-financial risk management, including health and environmental safety.
Related Concept Videos
01:10Standard Deviation
The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The standard deviation value is small when the data are concentrated close to the mean, exhibiting slight variation or spread. The standard deviation value is never negative, it is either positive or zero. The standard deviation is larger when the data values are more spread out from the mean, which means the data values are exhibiting more...
01:12Relative Risk
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
01:26Equity Theory
Equity theory explains how our sense of fairness influences the dynamics of close relationships. Rooted in social psychology, the theory posits that individuals evaluate fairness by comparing the ratio of their contributions to the rewards they receive. Relationship satisfaction is highest when these ratios are perceived as balanced between partners, promoting mutual reciprocity and a sense of justice.Equity vs. Equality in RelationshipsEquity is distinct from equality. Fairness does not...
01:02Quantifying and Rejecting Outliers: The Grubbs Test
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
02:46Uncertainty in Measurement: Reading Instruments
Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
01:31Critical Values
A critical value is a definite value obtained from a particular probability distribution at a predecided confidence level (or a predecided significance level) for a given population parameter. The critical value provides demarcation that separates the sample statistics that are likely to occur from the ones that are unlikely to occur based on the given probability distribution and the population parameter to be estimated. The critical value for normal distribution is obtained from the z...