ExtremeRisks (version 0.0.4-1)

expectiles: Expectile Computation

Description

Computes the true expectile for some families of parametric models.

Usage

expectiles(par, tau, tsDist="gPareto", tsType="IID", trueMethod="true",
           estMethod="LAWS", nrep=1e+05, ndata=1e+06, burnin=1e+03)

Value

The \(\tau\)-th expectile.

Arguments

par

A vector of \((1 \times p)\) parameters of the time series parametric family. See Details.

tau

A real in \((0,1)\) specifying the level \(\tau\) of the expectile to be computed. See Details.

tsDist

A string specifying the parametric family of the innovations distribution. By default tsDist="gPareto" specifies a Pareto family of distributions. See Details.

tsType

A string specifying the type of time series. By default tsType="IID" specifies a sequence of independent and indentically distributed random variables. See Details.

trueMethod

A string specifying the method used to computed the expecile. By default trueMethod="true" specifies that the true analytical expression to computed the expectile is used. See Details.

estMethod

A string specifying the method used to estimate the expecile. By default est="LAWS" specifies the use of the direct LAWS estimator. See Details.

nrep

A positive interger specifying the number of simulations to use for computing an approximation of the expectile. See Details.

ndata

A positive interger specifying the number of observations to genreated for each simulation. See Details.

burnin

A positive interger specifying the number of initial observations to discard from the simulated sample.

Author

Simone Padoan, , https://faculty.unibocconi.it/simonepadoan/; Gilles Stupfler, , https://math.univ-angers.fr/~stupfler/

Details

For a parametric family of time series models or a parametric family of distributions (for the case of independent observations) the \(\tau\)-th expectile (or expectile of level tau) is computed.

• There are two methods to compute the \(\tau\)-th expectile. For the Generalised Pareto and Student-t parametric families of distributions, the analytical epxression of the expectile is available. This is used to compute the \(\tau\)-th expectile if the parameter trueMethod="true" is specified. For most of parametric family of distributions or parametric families of time series models the analytical epxression of the expectile is not available. In this case an approximate value of the \(\tau\)-th expectile is computed via a Monte Carlo method if the parameter trueMethod=="approx" is specified. In particular, ndata observations from a family of time series models (e.g. tsType="AR" and tsDist="studentT") or a sequence of independent and indentically distributed random variables with common family of distributions (e.g. tsType="IID" and tsDist="gPareto") are simulated nrep times. For each simulation the \(\tau\)-th expectile is estimate by the estimation method specified by estMethod. The mean of such estimate provides an approximate value of the \(\tau\)-th expectile. The available estimator to esitmate the expecile are the direct LAWS (estMethod="LAWS") and the indirect QB (estMethod="QB"), see estExpectiles for details. The available families of distributions are: Generalised Pareto (tsDist="gPareto"), Student-t (tsDist="studentT") and Frechet (tsDist="Frechet"). The available classes of time series with parametric innovations families of distributions are specified in rtimeseries.

References

Anthony C. Davison, Simone A. Padoan and Gilles Stupfler (2023). Tail Risk Inference via Expectiles in Heavy-Tailed Time Series, Journal of Business & Economic Statistics, 41(3) 876-889.

See Also

rtimeseries

Examples

# Derivation of the true tau-th expectile for the Pareto distribution
# via accurate simulation

# parameter value
par <- c(1, 0.3)

# Intermediate level (or sample tail probability 1-tau)
tau <- 0.99

trueExp <- expectiles(par, tau)
trueExp

# \donttest{
# tau-th expectile of the AR(1) with Student-t innovations
tsDist <- "studentT"
tsType <- "AR"

# Approximation via Monte Carlo methods
trueMethod <- "approx"

# parameter setting
corr <- 0.8
df <- 3
par <- c(corr, df)

# Intermediate level (or sample tail probability 1-tau)
tau <- 0.99

trueExp <- expectiles(par, tau, tsDist, tsType, trueMethod)
trueExp
# }