meanEstimation: Estimation of mean returns
In ExpectedReturns: Computation of implied expected returns

Description Usage Arguments Details Value Author(s) References Examples

Function which is used to compute the estimation of the mean returns

1	meanEstimation(rets, control = list())

`rets`	a (T x N) matrix of past returns.
`control`	control parameters (see Details).

The argument control is a list that can supply any of the following components:

type

method used to estimate the mean returns, among 'naive', 'ewma', 'bs' and 'mart' where:

'naive' is used to compute the arithmetic mean of the returns.

'ewma' is used to compute the exponential weighted moving average mean of the returns. The data must be sorted from the oldest to the latest.

'bs' is used to compute the Bayes-Stein estimation.

'mart' is used to compute the Martinelli implied returns.

Default: type = 'naive'.

lambda

decay parameter. Default: lambda = 0.94.

A (N x 1) vector of expected returns.

David Ardia <david.ardia@unifr.ch> and Jean-Philippe Gagnon Fleury.

Jorion, P. (2004). Bayes-Stein Estimation for Portfolio Analysis Journal of Finance and Quantitative Analysis 21(3), pp.279–292.

# For the examples, we simply generate a 100 x 25 random matrix.
set.seed(3214)
T = 100
N = 25
rets = matrix(rnorm(T*N), nrow = T, ncol = N)

#Computes the naive estimation of the mean.
meanEstimation(rets)

#Computes the naive estimation of the mean.
meanEstimation(rets, control = list(type = "naive"))

#Computes the ewma estimation of the mean with default lambda = 0.94.
meanEstimation(rets, control = list(type = "ewma"))

#Computes the ewma estimation of the mean with lambda = 0.9.
meanEstimation(rets, control = list(type = "ewma", lambda = 0.9))

#Computes the Martinelli's estimation of the mean.
meanEstimation(rets, control = list(type = "mart"))

#Computes the Bayes-Stein's estimation of the mean.
meanEstimation(rets, control = list(type = "bs"))