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Expected shortfall is also called conditional value at risk (CVaR), [1] average value at risk (AVaR), expected tail loss (ETL), and superquantile. [ 2 ] ES estimates the risk of an investment in a conservative way, focusing on the less profitable outcomes.
However, it can be bounded by coherent risk measures like Conditional Value-at-Risk (CVaR) or entropic value at risk (EVaR). CVaR is defined by average of VaR values for confidence levels between 0 and α. However VaR, unlike CVaR, has the property of being a robust statistic. A related class of risk measures is the 'Range Value at Risk' (RVaR ...
The average value at risk (sometimes called expected shortfall or conditional value-at-risk or ) is a coherent risk measure, even though it is derived from Value at Risk which is not. The domain can be extended for more general Orlitz Hearts from the more typical Lp spaces .
Under some other settings, TVaR is the conditional expectation of loss above a given value, whereas the expected shortfall is the product of this value with the probability of it occurring. [3] The former definition may not be a coherent risk measure in general, however it is coherent if the underlying distribution is continuous. [4]
The Rachev Ratio (or R-Ratio) is a risk-return performance measure of an investment asset, portfolio, or strategy. ... also known as conditional value at risk ...
Conditional value at risk is a distortion risk measure with associated distortion function () = {<. [2] [3] The negative expectation is a distortion risk measure with associated distortion function g ( x ) = x {\displaystyle g(x)=x} .
In financial mathematics, a deviation risk measure is a function to quantify financial risk ... Conditional value-at-risk (CVaR) deviation, defined for any ...
The authors start by proposing an auxiliary function (), where is a vector of portfolio returns, that is defined by: = {+ [(,)] +} They call this the conditional drawdown-at-risk (CDaR); this is a nod to conditional value-at-risk (CVaR), which may also be optimized using linear programming. There are two limiting cases to be aware of: