CAC40return

This data set considers CAC40 return at the closure of the market from
March 2, 1990 to June 14, 2021.

datasets

Numerous time series admit autoregressive moving average (ARMA)
representations, in which the errors are uncorrelated but not necessarily
independent.
These models are called weak ARMA by opposition to the standard ARMA models,
also called strong ARMA models, in which the error terms are supposed to be
independent and identically distributed (iid).
This package allows the study of nonlinear time series models through weak
ARMA representations.
It determines identification, estimation and validation for ARMA models and
for AR and MA models in particular.
Functions can also be used in the strong case.
This package also works on white noises by omitting arguments 'p', 'q', 'ar'
and 'ma'.
See Francq, C. and Zako<c3><af>an, J. (1998) <doi:10.1016/S0378-3758(97)00139-0> and
Boubacar Ma<c3><af>nassara, Y. and Saussereau, B. (2018)
<doi:10.1080/01621459.2017.1380030> for more details.

Julien Yves Rolland

weakARMA

Tools for the Analysis of Weak ARMA Models

Yacouba Boubacar Ma<c3><af>nassara

Coraline Parguey

Vincent Mouillot

CAC40return function

A numerical vector with 7935 observations.
We computed every value from the dataset <code><a rd-options="" href="/link/CAC40?package=weakARMA&version=1.0.3" data-mini-rdoc="weakARMA::CAC40">CAC40</a></code>
with the following code:<pre>
 cac&lt;-CAC40;
 n&lt;-length(cac);
 rend&lt;-rep(0,n);
 rend[2:n]&lt;-(log(cac[2:n]/cac[1:(n-1)])*100);
 CAC40return&lt;-rend[2:n]
 </pre>

Format

Paris stock exchange return — CAC40return

A numerical vector with 7935 observations.
We computed every value from the dataset <code><a rd-options='' href='CAC40'>CAC40</a></code>
with the following code:<pre>
 cac&lt;-CAC40;
 n&lt;-length(cac);
 rend&lt;-rep(0,n);
 rend[2:n]&lt;-(log(cac[2:n]/cac[1:(n-1)])*100);
 CAC40return&lt;-rend[2:n]
 </pre>

CAC40return: Paris stock exchange return

Description

Usage

Arguments

Format

See Also