# Expectile Computation Computes the true expectile for some families of parametric models. ```r expectiles(par, tau, tsDist="gPareto", tsType="IID", trueMethod="true", estMethod="LAWS", nrep=1e+05, ndata=1e+06, burnin=1e+03) ``` ## Arguments - `par`: A vector of $(1 x p)$ 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. ## 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 . ## Returns The $\tau$-**th** expectile. ## 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. ## Author(s) Simone Padoan, simone.padoan@unibocconi.it , [https://faculty.unibocconi.it/simonepadoan/](https://faculty.unibocconi.it/simonepadoan/); Gilles Stupfler, gilles.stupfler@univ-angers.fr , [https://math.univ-angers.fr/~stupfler/](https://math.univ-angers.fr/~stupfler/) ## See Also rtimeseries ## Examples ```r # 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 # 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 ```

expectiles function