MLTailIndex {ExtremeRisks} R Documentation

## Maximum Likelihood Tail Index Estimation

### Description

Computes a point and interval estimate of the tail index based on the Maximum Likelihood (ML) estimator.

### Usage

MLTailIndex(data, k, var=FALSE, varType="asym-Dep", bigBlock=NULL,
smallBlock=NULL, alpha=0.05)


### Arguments

 data A vector of (1 \times n) observations. k An integer specifying the value of the intermediate sequence k_n. See Details. var If var=TRUE then an estimate of the asymptotic variance of the tail index estimator is computed. varType A string specifying the asymptotic variance to compute. By default varType="asym-Dep" specifies the variance estimator for serial dependent observations. See Details. bigBlock An interger specifying the size of the big-block used to estimaste the asymptotic variance. See Details. smallBlock An interger specifying the size of the small-block used to estimaste the asymptotic variance. See Details. alpha A real in (0,1) specifying the confidence level (1-\alpha)100\% of the approximate confidence interval for the tail index.

### Details

For a dataset data of sample size n, the tail index \gamma of its (marginal) distribution is computed by applying the ML estimator. The observations can be either independent or temporal dependent.

• k or k_n is the value of the so-called intermediate sequence k_n, n=1,2,\ldots. Its represents a sequence of positive integers such that k_n \to \infty and k_n/n \to 0 as n \to \infty. Practically, the value k_n specifies the numer of k+1 larger order statistics to be used to estimate \gamma.

• If var=TRUE then the asymptotic variance of the Hill estimator is computed. With independent observations the asymptotic variance is estimated by the formula in Theorem 3.4.2 of de Haan and Ferreira (2006). This is achieved through varType="asym-Ind". With serial dependent observations the asymptotic variance is estimated by the formula in 1288 in Drees (2000). This is achieved through varType="asym-Dep". In this latter case the serial dependence is estimated by exploiting the "big blocks seperated by small blocks" techinque which is a standard tools in time series, see Leadbetter et al. (1986). See also formula (11) in Drees (2003). The size of the big and small blocks are specified by the parameters bigBlock and smallBlock, respectively.

• Given a small value \alpha\in (0,1) then an asymptotic confidence interval for the tail index, with approximate nominal confidence level (1-\alpha)100\% is computed.

### Value

A list with elements:

• gammaHat: an estimate of tail index \gamma;

• VarGamHat: an estimate of the variance of the ML estimator;

• CIgamHat: an estimate of the approximate (1-\alpha)100\% confidence interval for \gamma.

### References

Padoan A.S. and Stupfler, G. (2020). Extreme expectile estimation for heavy-tailed time series. arXiv e-prints arXiv:2004.04078, https://arxiv.org/abs/2004.04078.

Drees, H. (2000). Weighted approximations of tail processes for \beta-mixing random variables. Annals of Applied Probability, 10, 1274-1301.

de Haan, L. and Ferreira, A. (2006). Extreme Value Theory: An Introduction. Springer-Verlag, New York.

Leadbetter, M.R., Lindgren, G. and Rootzen, H. (1989). Extremes and related properties of random sequences and processes. Springer.

### Examples

# Tail index estimation based on the Maximum Likelihood estimator obtained with
# 1-dimensional data simulated from an AR(1) with univariate Student-t
# distributed innovations

tsDist <- "studentT"
tsType <- "AR"

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

# Big- small-blocks setting
bigBlock <- 65
smallBlock <- 15

# Number of larger order statistics
k <- 150

# sample size
ndata <- 2500

# Simulates a sample from an AR(1) model with Student-t innovations
data <- rtimeseries(ndata, tsDist, tsType, par)

# tail index estimation
gammaHat <- MLTailIndex(data, k, TRUE, bigBlock=bigBlock, smallBlock=smallBlock)
gammaHat$gammaHat gammaHat$CIgamHat


[Package ExtremeRisks version 0.0.4 Index]