LogtES: ES for t distributed geometric returns

In Dowd: Functions Ported from 'MMR2' Toolbox Offered in Kevin Dowd's Book Measuring Market Risk

Description

Estimates the ES of a portfolio assuming that geometric returns are Student-t distributed, for specified confidence level and holding period.

Usage

LogtES(...)

Arguments

...

The input arguments contain either return data or else mean and standard deviation data. Accordingly, number of input arguments is either 5 or 6. In case there 5 input arguments, the mean and standard deviation of data is computed from return data. See examples for details. returns Vector of daily geometric return data mu Mean of daily geometric return data sigma Standard deviation of daily geometric return data investment Size of investment df Number of degrees of freedom in the t distribution cl VaR confidence level hp VaR holding period

Value

Matrix of ES whose dimension depends on dimension of hp and cl. If cl and hp are both scalars, the matrix is 1 by 1. If cl is a vector and hp is a scalar, the matrix is row matrix, if cl is a scalar and hp is a vector, the matrix is column matrix and if both cl and hp are vectors, the matrix has dimension length of cl * length of hp.

Author(s)

Dinesh Acharya

References

Dowd, K. Measuring Market Risk, Wiley, 2007.

Examples

# Computes ES given geometric return data
   data <- runif(5, min = 0, max = .2)
   LogtES(returns = data, investment = 5, df = 6, cl = .95, hp = 90)

   # Computes ES given mean and standard deviation of return data
   LogtES(mu = .012, sigma = .03, investment = 5, df = 6, cl = .95, hp = 90)

Dowd documentation built on May 2, 2019, 6:15 p.m.