calculates Expected Shortfall(ES) (or Conditional Value-at-Risk(CVaR) for univariate and component, using a variety of analytical methods.

Description

Calculates Expected Shortfall(ES) (also known as) Conditional Value at Risk(CVaR) for univariate, component, and marginal cases using a variety of analytical methods.

Usage

ES(R, p = 0.95, method = c("modified", "gaussian", "historical", "kernel"), clean = c("none", "boudt","geltner"), portfolio_method = c("single", "component"), weights = NULL, mu = NULL, sigma = NULL, m3 = NULL, m4 = NULL, invert = TRUE, operational = TRUE, ...)

Arguments

Value

ES measure

Background

This function provides several estimation methods for the Expected Shortfall (ES) (also called Conditional Value at Risk (CVaR)) of a return series and the Component ES of a portfolio. At a preset probability level denoted c, which typically is between 1 and 5 per cent, the ES of a return series is the negative value of the expected value of the return when the return is less than its c-quantile. Unlike value-at-risk, conditional value-at-risk has all the properties a risk measure should have to be coherent and is a convex function of the portfolio weights (Pflug, 2000). With a sufficiently large data set, you may choose to estimate ES with the sample average of all returns that are below the c empirical quantile. More efficient estimates of VaR are obtained if a (correct) assumption is made on the return distribution, such as the normal distribution. If your return series is skewed and/or has excess kurtosis, Cornish-Fisher estimates of ES can be more appropriate. For the ES of a portfolio, it is also of interest to decompose total portfolio ES into the risk contributions of each of the portfolio components. For the above mentioned ES estimators, such a decomposition is possible in a financially meaningful way.

Estimation of ES of a univariate return series

The ES at a probability level p (e.g. 95%) is the negative value of the expected value of the return when the return is less than its c=1-p quantile. In a set of returns for which sufficently long history exists, the per-period ES can be estimated by the negative value of the sample average of all returns below the quantile. This method is also sometimes called “historical ES”, as it is by definition ex post analysis of the return distribution, and may be accessed with method="historical".

When you don't have a sufficiently long set of returns to use non-parametric or historical ES, or wish to more closely model an ideal distribution, it is common to us a parmetric estimate based on the distribution. Parametric ES does a better job of accounting for the tails of the distribution by more precisely estimating shape of the distribution tails of the risk quantile. The most common estimate is a normal (or Gaussian) distribution R\sim N(\mu,\sigma) for the return series. In this case, estimation of ES requires the mean return \bar{R}, the return distribution and the variance of the returns \sigma. In the most common case, parametric VaR is thus calculated by

sigma=var(R)

VaR= -mean(R) + sqrt(sigma)*dnorm(z_c)/c

where z_{c} is the c-quantile of the standard normal distribution. Represented in R by qnorm(c), and may be accessed with method="gaussian". The function dnorm is the Gaussian density function.

The limitations of Gaussian ES are well covered in the literature, since most financial return series are non-normal. Boudt, Peterson and Croux (2008) provide a modified ES calculation that takes the higher moments of non-normal distributions (skewness, kurtosis) into account through the use of a Cornish-Fisher expansion, and collapses to standard (traditional) Gaussian ES if the return stream follows a standard distribution. More precisely, for a loss probability c, modified ES is defined as the negative of the expected value of all returns below the c Cornish-Fisher quantile and where the expectation is computed under the second order Edgeworth expansion of the true distribution function.

Modified expected shortfall should always be higher than modified Value at Risk. Due to estimation problems, this might not always be the case. Set Operational = TRUE to replace modified ES with modified VaR in the (exceptional) case where the modified ES is smaller than modified VaR.

Component ES

By setting portfolio_method="component" you may calculate the ES contribution of each element of the portfolio. The return from the function in this case will be a list with three components: the univariate portfolio ES, the scalar contribution of each component to the portfolio ES (these will sum to the portfolio ES), and a percentage risk contribution (which will sum to 100%).

Both the numerical and percentage component contributions to ES may contain both positive and negative contributions. A negative contribution to Component ES indicates a portfolio risk diversifier. Increasing the position weight will reduce overall portoflio ES.

If a weighting vector is not passed in via weights, the function will assume an equal weighted (neutral) portfolio.

Multiple risk decomposition approaches have been suggested in the literature. A na�ve approach is to set the risk contribution equal to the stand-alone risk. This approach is overly simplistic and neglects important diversification effects of the units being exposed differently to the underlying risk factors. An alternative approach is to measure the ES contribution as the weight of the position in the portfolio times the partial derivative of the portfolio ES with respect to the component weight.

C[i]ES = w[i]*(dES/dw[i]).

Because the portfolio ES is linear in position size, we have that by Euler's theorem the portfolio VaR is the sum of these risk contributions. Scaillet (2002) shows that for ES, this mathematical decomposition of portfolio risk has a financial meaning. It equals the negative value of the asset's expected contribution to the portfolio return when the portfolio return is less or equal to the negative portfolio VaR:

C[i]ES = -E( w[i]r[i]|rp<=-VaR )

For the decomposition of Gaussian ES, the estimated mean and covariance matrix are needed. For the decomposition of modified ES, also estimates of the coskewness and cokurtosis matrices are needed. If r denotes the Nx1 return vector and mu is the mean vector, then the N \times N^2 co-skewness matrix is

m3 = E[ (r - mu)(r - mu)' %x% (r - \mu)']

The N \times N^3 co-kurtosis matrix is

E[ (r - \mu)(r - \mu)' %x% (r - \mu)'%x% (r - \mu)']

where %x% stands for the Kronecker product. The matrices can be estimated through the functions skewness.MM and kurtosis.MM. More efficient estimators were proposed by Martellini and Ziemann (2007) and will be implemented in the future.

As discussed among others in Cont, Deguest and Scandolo (2007), it is important that the estimation of the ES measure is robust to single outliers. This is especially the case for modified VaR and its decomposition, since they use higher order moments. By default, the portfolio moments are estimated by their sample counterparts. If clean="boudt" then the 1-p most extreme observations are winsorized if they are detected as being outliers. For more information, see Boudt, Peterson and Croux (2008) and Return.clean. If your data consist of returns for highly illiquid assets, then clean="geltner" may be more appropriate to reduce distortion caused by autocorrelation, see Return.Geltner for details.

Note

The option to invert the ES measure should appease both academics and practitioners. The mathematical definition of ES as the negative value of extreme losses will (usually) produce a positive number. Practitioners will argue that ES denotes a loss, and should be internally consistent with the quantile (a negative number). For tables and charts, different preferences may apply for clarity and compactness. As such, we provide the option, and set the default to TRUE to keep the return consistent with prior versions of PerformanceAnalytics, but make no value judgement on which approach is preferable.

Author(s)

Brian G. Peterson and Kris Boudt

References

Boudt, Kris, Peterson, Brian, and Christophe Croux. 2008. Estimation and decomposition of downside risk for portfolios with non-normal returns. 2008. The Journal of Risk, vol. 11, 79-103.

Cont, Rama, Deguest, Romain and Giacomo Scandolo. Robustness and sensitivity analysis of risk measurement procedures. Financial Engineering Report No. 2007-06, Columbia University Center for Financial Engineering.

Laurent Favre and Jose-Antonio Galeano. Mean-Modified Value-at-Risk Optimization with Hedge Funds. Journal of Alternative Investment, Fall 2002, v 5.

Martellini, Lionel, and Volker Ziemann. Improved Forecasts of Higher-Order Comoments and Implications for Portfolio Selection. 2007. EDHEC Risk and Asset Management Research Centre working paper.

Pflug, G. Ch. Some remarks on the value-at-risk and the conditional value-at-risk. In S. Uryasev, ed., Probabilistic Constrained Optimization: Methodology and Applications, Dordrecht: Kluwer, 2000, 272-281.

Scaillet, Olivier. Nonparametric estimation and sensitivity analysis of expected shortfall. Mathematical Finance, 2002, vol. 14, 74-86.

See Also

VaR
SharpeRatio.modified
chart.VaRSensitivity
VaR.gpd
VaR.norm
VaR.backtest
Return.clean

Examples

    data(edhec)

    # first do normal ES calc
    ES(edhec, p=.95, method="historical")

    # now use Gaussian
    ES(edhec, p=.95, method="gaussian")

    # now use modified Cornish Fisher calc to take non-normal distribution into account
    ES(edhec, p=.95, method="modified")

    # now use p=.99
    ES(edhec, p=.99)
    # or the equivalent alpha=.01
    ES(edhec, p=.01)

    # now with outliers squished
    ES(edhec, clean="boudt")

    # add Component ES for the equal weighted portfolio
    ES(edhec, clean="boudt", portfolio_method="component")