RcmdrPlugin.RiskDemo (version 1.9)

returns: Computing expected returns and their covariance matrix

Description

Computing expected returns and their covariance matrix when the returns are lognormal.

Usage

returns(volvec, indexvol, beta)

Arguments

volvec

vector of volatilities

indexvol

volatility of the portfolio index

beta

vector of betas

Value

mean

vector of expected returns

cov

covariance matrix of returns

Details

The arguments are given in decimals. The single index model is used to compute the covariance matrix of a multivariate normal distribution. The mean vector is assumed to be zero. The properties of the log-normal distribution are then used to compute the mean vector and covariance matrix of the corresponding multivariate log-normal distribution.

References

Bodie, Kane, and Marcus (2014) Investments, 10th Global Edition, McGraw-Hill Education, (see Section 8.2 The Single-Index Model).

Examples

Run this code
# NOT RUN {
returns(volvec=c(0.1,0.2,0.3),indexvol=0.2, beta=c(0.5,-0.1,1.1)) 
# }

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