Expected shortfall


Expected shortfall is a risk measure—a concept used in the field of financial risk measurement to evaluate the market risk or credit risk of a portfolio. The "expected shortfall at q% level" is the expected return on the portfolio in the worst of cases. ES is an alternative to value at risk that is more sensitive to the shape of the tail of the loss distribution.
Expected shortfall is also called conditional value at risk, average value at risk, and expected tail loss.
ES estimates the risk of an investment in a conservative way, focusing on the less profitable outcomes. For high values of it ignores the most profitable but unlikely possibilities, while for small values of it focuses on the worst losses. On the other hand, unlike the discounted maximum loss, even for lower values of the expected shortfall does not consider only the single most catastrophic outcome. A value of often used in practice is 5%.
Expected shortfall is considered a more useful risk measure than VaR because it is a coherent, and moreover a spectral, measure of financial portfolio risk. It is calculated for a given quantile-level, and is defined to be the mean loss of portfolio value given that a loss is occurring at or below the -quantile.

Formal definition

If is the payoff of a portfolio at some future time and then we define the expected shortfall as
where is the value at risk. This can be equivalently written as
where is the lower -quantile and is the indicator function. The dual representation is
where is the set of probability measures which are absolutely continuous to the physical measure such that almost surely. Note that is the Radon–Nikodym derivative of with respect to.
Expected shortfall can be generalized to a general class of coherent risk measures on spaces with a corresponding dual characterization in the corresponding dual space. The domain can be extended for more general Orlicz Hearts.
If the underlying distribution for is a continuous distribution then the expected shortfall is equivalent to the tail conditional expectation defined by.
Informally, and non rigorously, this equation amounts to saying "in case of losses so severe that they occur only alpha percent of the time, what is our average loss".
Expected shortfall can also be written as a distortion risk measure given by the distortion function

Examples

Example 1. If we believe our average loss on the worst 5% of the possible outcomes for our portfolio is EUR 1000, then we could say our expected shortfall is EUR 1000 for the 5% tail.
Example 2. Consider a portfolio that will have the following possible values at the end of the period:
probabilityending value
of eventof the portfolio
10%0
30%80
40%100
20%150

Now assume that we paid 100 at the beginning of the period for this portfolio. Then the profit in each case is or:
probability
of eventprofit
10%−100
30%−20
40%0
20%50

From this table let us calculate the expected shortfall for a few values of :
expected shortfall
5%100
10%100
20%60
30%46.6
40%40
50%32
60%26.6
80%20
90%12.2
100%6

To see how these values were calculated, consider the calculation of, the expectation in the worst 5% of cases. These cases belong to row 1 in the profit table, which have a profit of −100. The expected profit for these cases is −100.
Now consider the calculation of, the expectation in the worst 20 out of 100 cases. These cases are as follows: 10 cases from row one, and 10 cases from row two. For row 1 there is a profit of −100, while for row 2 a profit of −20. Using the expected value formula we get
Similarly for any value of. We select as many rows starting from the top as are necessary to give a cumulative probability of and then calculate an expectation over those cases. In general the last row selected may not be fully used.
As a final example, calculate. This is the expectation over all cases, or
The value at risk is given below for comparison.

Properties

The expected shortfall increases as decreases.
The 100%-quantile expected shortfall equals negative of the expected value of the portfolio.
For a given portfolio, the expected shortfall is greater than or equal to the Value at Risk at the same level.

Optimization of expected shortfall

Expected shortfall, in its standard form, is known to lead to a generally non-convex optimization problem. However, it is possible to transform the problem into a linear program and find the global solution. This property makes expected shortfall a cornerstone of alternatives to mean-variance portfolio optimization, which account for the higher moments of a return distribution.
Suppose that we want to minimize the expected shortfall of a portfolio. The key contribution of Rockafellar and Uryasev in their 2000 paper is to introduce the auxiliary function for the expected shortfall:Where and is a loss function for a set of portfolio weights to be applied to the returns. Rockafellar/Uryasev proved that is convex with respect to and is equivalent to the expected shortfall at the minimum point. To numerically compute the expected shortfall for a set of portfolio returns, it is necessary to generate simulations of the portfolio constituents; this is often done using copulas. With these simulations in hand, the auxiliary function may be approximated by:This is equivalent to the formulation: Finally, choosing a linear loss function turns the optimization problem into a linear program. Using standard methods, it is then easy to find the portfolio that minimizes expected shortfall.

Formulas for continuous probability distributions

Closed-form formulas exist for calculating the expected shortfall when the payoff of a portfolio or a corresponding loss follows a specific continuous distribution. In the former case the expected shortfall corresponds to the opposite number of the left-tail conditional expectation below :
Typical values of in this case are 5% and 1%.
For engineering or actuarial applications it is more common to consider the distribution of losses, the expected shortfall in this case corresponds to the right-tail conditional expectation above and the typical values of are 95% and 99%:
Since some formulas below were derived for the left-tail case and some for the right-tail case, the following reconciliations can be useful:

Normal distribution

If the payoff of a portfolio follows normal distribution with the p.d.f. then the expected shortfall is equal to, where is the standard normal p.d.f., is the standard normal c.d.f., so is the standard normal quantile.
If the loss of a portfolio follows normal distribution, the expected shortfall is equal to.

Generalized Student's t-distribution

If the payoff of a portfolio follows generalized Student's t-distribution with the p.d.f. then the expected shortfall is equal to, where is the standard t-distribution p.d.f., is the standard t-distribution c.d.f., so is the standard t-distribution quantile.
If the loss of a portfolio follows generalized Student's t-distribution, the expected shortfall is equal to.

Laplace distribution

If the payoff of a portfolio follows Laplace distribution with the p.d.f.
and the c.d.f.
then the expected shortfall is equal to for.
If the loss of a portfolio follows Laplace distribution, the expected shortfall is equal to

Logistic distribution

If the payoff of a portfolio follows logistic distribution with the p.d.f. and the c.d.f. then the expected shortfall is equal to.
If the loss of a portfolio follows logistic distribution, the expected shortfall is equal to.

Exponential distribution

If the loss of a portfolio follows exponential distribution with the p.d.f. and the c.d.f. then the expected shortfall is equal to.

Pareto distribution

If the loss of a portfolio follows Pareto distribution with the p.d.f. and the c.d.f. then the expected shortfall is equal to.

Generalized Pareto distribution (GPD)

If the loss of a portfolio follows GPD with the p.d.f.
and the c.d.f.
then the expected shortfall is equal to
and the VaR is equal to

Weibull distribution

If the loss of a portfolio follows Weibull distribution with the p.d.f. and the c.d.f. then the expected shortfall is equal to, where is the upper incomplete gamma function.

Generalized extreme value distribution (GEV)

If the payoff of a portfolio follows GEV with the p.d.f. and the c.d.f. then the expected shortfall is equal to and the VaR is equal to, where is the upper incomplete gamma function, is the logarithmic integral function.
If the loss of a portfolio follows GEV, then the expected shortfall is equal to, where is the lower incomplete gamma function, is the Euler-Mascheroni constant.

Generalized hyperbolic secant (GHS) distribution

If the payoff of a portfolio follows GHS distribution with the p.d.f. and the c.d.f. then the expected shortfall is equal to, where is the Spence's function, is the imaginary unit.

Johnson's SU-distribution

If the payoff of a portfolio follows Johnson's SU-distribution with the c.d.f. then the expected shortfall is equal to, where is the c.d.f. of the standard normal distribution.

Burr type XII distribution

If the payoff of a portfolio follows the Burr type XII distribution with the p.d.f. and the c.d.f., the expected shortfall is equal to, where is the hypergeometric function. Alternatively,.

Dagum distribution

If the payoff of a portfolio follows the Dagum distribution with the p.d.f. and the c.d.f., the expected shortfall is equal to, where is the hypergeometric function.

Lognormal distribution

If the payoff of a portfolio follows lognormal distribution, i.e. the random variable follows normal distribution with the p.d.f., then the expected shortfall is equal to, where is the standard normal c.d.f., so is the standard normal quantile.

Log-logistic distribution

If the payoff of a portfolio follows log-logistic distribution, i.e. the random variable follows logistic distribution with the p.d.f., then the expected shortfall is equal to, where is the regularized incomplete beta function,.
As the incomplete beta function is defined only for positive arguments, for a more generic case the expected shortfall can be expressed with the hypergeometric function:.
If the loss of a portfolio follows log-logistic distribution with p.d.f. and c.d.f., then the expected shortfall is equal to, where is the incomplete beta function.

Log-Laplace distribution

If the payoff of a portfolio follows log-Laplace distribution, i.e. the random variable follows Laplace distribution the p.d.f., then the expected shortfall is equal to.

Log-generalized hyperbolic secant (log-GHS) distribution

If the payoff of a portfolio follows log-GHS distribution, i.e. the random variable follows GHS distribution with the p.d.f., then the expected shortfall is equal to, where is the hypergeometric function.

Dynamic expected shortfall

The conditional version of the expected shortfall at the time t is defined by
where.
This is not a time-consistent risk measure. The time-consistent version is given by
such that