Package 'fExtremes' reference manual

Title:	Rmetrics - Modelling Extreme Events in Finance
Description:	Provides functions for analysing and modelling extreme events in financial time Series. The topics include: (i) data pre-processing, (ii) explorative data analysis, (iii) peak over threshold modelling, (iv) block maxima modelling, (v) estimation of VaR and CVaR, and (vi) the computation of the extreme index.
Authors:	Diethelm Wuertz [aut], Tobias Setz [aut], Yohan Chalabi [aut], Paul J. Northrop [cre, ctb]
Maintainer:	Paul J. Northrop <[email protected]>
License:	GPL (>= 2)
Version:	4032.84
Built:	2024-12-14 03:02:14 UTC
Source:	https://github.com/r-forge/rmetrics

Modelling Extreme Events in Finance

Description

The Rmetrics "fExtremes" package is a collection of functions to analyze and model extreme events in Finance and Insurance.

Details

        Package:    fExtremes
        Type:       Package
        License:    GPL Version 2 or later
        Copyright:  (c) 1999-2014 Rmetrics Association
        URL:        https://www.rmetrics.org

1 Introduction

The fExtremes package provides functions for analyzing and modeling extreme events in financial time Series. The topics include: (i) data pre-processing, (ii) explorative data analysis, (iii) peak over threshold modeling, (iv) block maxima modeling, (v) estimation of VaR and CVaR, and (vi) the computation of the extreme index.

2 Data and their Preprocessing

Data Sets:

Data sets used in the examples of the timeSeries packages.

Data Preprocessing:

These are tools for data preprocessing, including functions to separate data beyond a threshold value, to compute blockwise data like block maxima, and to decluster point process data.

    blockMaxima     extracts block maxima from a vector or a time series 
    findThreshold   finds upper threshold for a given number of extremes  
    pointProcess    extracts peaks over Threshold from a vector or a time series 
    deCluster       de-clusters clustered point process data

2 Explorative Data Analysis of Extremes

This section contains a collection of functions for explorative data analysis of extreme values in financial time series. The tools include plot functions for empirical distributions, quantile plots, graphs exploring the properties of exceedances over a threshold, plots for mean/sum ratio and for the development of records. The functions are:

    emdPlot         plots of empirical distribution function
    qqparetoPlot    exponential/Pareto quantile plot
    mePlot          plot of mean excesses over a threshold
    mrlPlot         another variant, mean residual life plot
    mxfPlot         another variant, with confidence intervals
    msratioPlot     plot of the ratio of maximum and sum

    recordsPlot     Record development compared with iid data
    ssrecordsPlot   another variant, investigates subsamples
    sllnPlot        verifies Kolmogorov's strong law of large numbers
    lilPlot         verifies Hartman-Wintner's law of the iterated logarithm

    xacfPlot        plots ACF of exceedances over a threshold

Parameter Fitting of Mean Excesses:

    normMeanExcessFit    fits mean excesses with a normal density
    ghMeanExcessFit      fits mean excesses with a GH density   
    hypMeanExcessFit     fits mean excesses with a HYP density   
    nigMeanExcessFit     fits mean excesses with a NIG density  
    ghtMeanExcessFit     fits mean excesses with a GHT density

3 GPD Peak over Threshold Modeling

GPD Distribution:

A collection of functions to compute the generalized Pareto distribution. The functions compute density, distribution function, quantile function and generate random deviates for the GPD. In addition functions to compute the true moments and to display the distribution and random variates changing parameters interactively are available.

    dgpd            returns the density of the GPD distribution
    pgpd            returns the probability function of the GPD
    qgpd            returns quantile function of the GPD distribution
    rgpd            generates random variates from the GPD distribution
    gpdSlider       displays density or rvs from a GPD

GPD Moments:

    gpdMoments      computes true mean and variance of GDP

GPD Parameter Estimation:

This section contains functions to fit and to simulate processes that are generated from the generalized Pareto distribution. Two approaches for parameter estimation are provided: Maximum likelihood estimation and the probability weighted moment method.

    gpdSim          generates data from the GPD distribution
    gpdFit          fits data to the GPD istribution

GPD print, plot and summary methods:

    print           print method for a fitted GPD object
    plot            plot method for a fitted GPD object
    summary         summary method for a fitted GPD object

GDP Tail Risk:

The following functions compute tail risk under the GPD approach.

    gpdQPlot        estimation of high quantiles
    gpdQuantPlot    variation of high quantiles with threshold
    gpdRiskMeasures prescribed quantiles and expected shortfalls
    gpdSfallPlot    expected shortfall with confidence intervals
    gpdShapePlot    variation of GPD shape with threshold
    gpdTailPlot     plot of the GPD tail

4 GEV Block Maxima Modeling

GEV Distribution:

This section contains functions to fit and to simulate processes that are generated from the generalized extreme value distribution including the Frechet, Gumbel, and Weibull distributions.

    dgev            returns density of the GEV distribution
    pgev            returns probability function of the GEV
    qgev            returns quantile function of the GEV distribution
    rgev            generates random variates from the GEV distribution
    gevSlider       displays density or rvs from a GEV

GEV Moments:

    gevMoments      computes true mean and variance

GEV Parameter Estimation:

A collection to simulate and to estimate the parameters of processes generated from GEV distribution.

    gevSim          generates data from the GEV distribution
    gumbelSim       generates data from the Gumbel distribution
    gevFit          fits data to the GEV distribution
    gumbelFit       fits data to the Gumbel distribution

    print           print method for a fitted GEV object
    plot            plot method for a fitted GEV object
    summary         summary method for a fitted GEV object

GEV MDA Estimation:

Here we provide Maximum Domain of Attraction estimators and visualize the results by a Hill plot and a common shape parameter plot from the Pickands, Einmal-Decker-deHaan, and Hill estimators.

    hillPlot        shape parameter and Hill estimate of the tail index
    shaparmPlot     variation of shape parameter with tail depth

GEV Risk Estimation:

    gevrlevelPlot   k-block return level with confidence intervals

4 Value at Risk

Two functions to compute Value-at-Risk and conditional Value-at-Risk.

    VaR             computes Value-at-Risk
    CVaR            computes conditional Value-at-Risk

5 Extreme Index

A collection of functions to simulate time series with a known extremal index, and to estimate the extremal index by four different kind of methods, the blocks method, the reciprocal mean cluster size method, the runs method, and the method of Ferro and Segers.

    thetaSim             simulates a time Series with known theta
    blockTheta           computes theta from Block Method
    clusterTheta         computes theta from Reciprocal Cluster Method
    runTheta             computes theta from Run Method
    ferrosegersTheta     computes theta according to Ferro and Segers

    exindexPlot          calculates and plots Theta(1,2,3)
    exindexesPlot        calculates Theta(1,2) and plots Theta(1)

About Rmetrics

The fExtremes Rmetrics package is written for educational support in teaching "Computational Finance and Financial Engineering" and licensed under the GPL.

Extremes Data Preprocessing

Description

A collection and description of functions for data preprocessing of extreme values. This includes tools to separate data beyond a threshold value, to compute blockwise data like block maxima, and to decluster point process data.

The functions are:

`blockMaxima`	Block Maxima from a vector or a time series,
`findThreshold`	Upper threshold for a given number of extremes,
`pointProcess`	Peaks over Threshold from a vector or a time series,
`deCluster`	Declusters clustered point process data.

Usage

blockMaxima(x, block = c("monthly", "quarterly"), doplot = FALSE)
findThreshold(x, n = floor(0.05*length(as.vector(x))), doplot = FALSE)
pointProcess(x, u = quantile(x, 0.95), doplot = FALSE)
deCluster(x, run = 20, doplot = TRUE)
blockMaxima(x, block = c("monthly", "quarterly"), doplot = FALSE)
findThreshold(x, n = floor(0.05*length(as.vector(x))), doplot = FALSE)
pointProcess(x, u = quantile(x, 0.95), doplot = FALSE)
deCluster(x, run = 20, doplot = TRUE)

Arguments

`block`	the block size. A numeric value is interpreted as the number of data values in each successive block. All the data is used, so the last block may not contain `block` observations. If the `data` has a `times` attribute containing (in an object of class `"POSIXct"`, or an object that can be converted to that class, see `as.POSIXct`) the times/dates of each observation, then `block` may instead take the character values `"month"`, `"quarter"`, `"semester"` or `"year"`. By default monthly blocks from daily data are assumed.
`doplot`	a logical value. Should the results be plotted? By default `TRUE`.
`n`	a numeric value or vector giving number of extremes above the threshold. By default, `n` is set to an integer representing 5% of the data from the whole data set `x`.
`run`	parameter to be used in the runs method; any two consecutive threshold exceedances separated by more than this number of observations/days are considered to belong to different clusters.
`u`	a numeric value at which level the data are to be truncated. By default the threshold value which belongs to the 95% quantile, `u=quantile(x,0.95)`.
`x`	a numeric data vector from which `findThreshold` and `blockMaxima` determine the threshold values and block maxima values. For the function `deCluster` the argument `x` represents a numeric vector of threshold exceedances with a `times` attribute which should be a numeric vector containing either the indices or the times/dates of each exceedance (if times/dates, the attribute should be an object of class `"POSIXct"` or an object that can be converted to that class; see `as.POSIXct`).

Details

Computing Block Maxima:

The function blockMaxima calculates block maxima from a vector or a time series, whereas the function blocks is more general and allows for the calculation of an arbitrary function FUN on blocks.

Finding Thresholds:

The function findThreshold finds a threshold so that a given number of extremes lie above. When the data are tied a threshold is found so that at least the specified number of extremes lie above.

De-Clustering Point Processes:

The function deCluster declusters clustered point process data so that Poisson assumption is more tenable over a high threshold.

Value

blockMaxima
returns a timeSeries object or a numeric vector of block maxima data.

findThreshold
returns a numeric value or vector of suitable thresholds.

pointProcess
returns a timeSeries object or a numeric vector of peaks over a threshold.

deCluster
returns a timeSeries object or a numeric vector for the declustered point process.

Author(s)

Some of the functions were implemented from Alec Stephenson's R-package evir ported from Alexander McNeil's S library EVIS, Extreme Values in S, some from Alec Stephenson's R-package ismev based on Stuart Coles code from his book, Introduction to Statistical Modeling of Extreme Values and some were written by Diethelm Wuertz.

References

Coles S. (2001); Introduction to Statistical Modelling of Extreme Values, Springer.

Embrechts, P., Klueppelberg, C., Mikosch, T. (1997); Modelling Extremal Events, Springer.

Examples

 
## findThreshold -
# Threshold giving (at least) fifty exceedances for Danish data:
library(timeSeries)
x <- as.timeSeries(data(danishClaims))
findThreshold(x, n = c(10, 50, 100))    

## blockMaxima -
# Block Maxima (Minima) for left tail of BMW log returns:
BMW <- as.timeSeries(data(bmwRet))
colnames(BMW) <- "BMW.RET"
head(BMW)
x <- blockMaxima( BMW, block = 65)
head(x)
## Not run: 
y <- blockMaxima(-BMW, block = 65)    
head(y) 
y <- blockMaxima(-BMW, block = "monthly")    
head(y)
## End(Not run)

## pointProcess -
# Return Values above threshold in negative BMW log-return data:
PP = pointProcess(x = -BMW, u = quantile(as.vector(x), 0.75))
PP
nrow(PP)

## deCluster -
# Decluster the 200 exceedances of a particular  
DC = deCluster(x = PP, run = 15, doplot = TRUE) 
DC
nrow(DC)
## findThreshold -
# Threshold giving (at least) fifty exceedances for Danish data:
library(timeSeries)
x <- as.timeSeries(data(danishClaims))
findThreshold(x, n = c(10, 50, 100))    

## blockMaxima -
# Block Maxima (Minima) for left tail of BMW log returns:
BMW <- as.timeSeries(data(bmwRet))
colnames(BMW) <- "BMW.RET"
head(BMW)
x <- blockMaxima( BMW, block = 65)
head(x)
## Not run: 
y <- blockMaxima(-BMW, block = 65)    
head(y) 
y <- blockMaxima(-BMW, block = "monthly")    
head(y)
## End(Not run)

## pointProcess -
# Return Values above threshold in negative BMW log-return data:
PP = pointProcess(x = -BMW, u = quantile(as.vector(x), 0.75))
PP
nrow(PP)

## deCluster -
# Decluster the 200 exceedances of a particular  
DC = deCluster(x = PP, run = 15, doplot = TRUE) 
DC
nrow(DC)

Extremal Index Estimation

Description

A collection and description of functions to simulate time series with a known extremal index, and to estimate the extremal index by four different kind of methods, the blocks method, the reciprocal mean cluster size method, the runs method, and the method of Ferro and Segers.

The functions are:

`thetaSim`	Simulates a time Series with known theta,
`blockTheta`	Computes theta from Block Method,
`clusterTheta`	Computes theta from Reciprocal Cluster Method,
`runTheta`	Computes theta from Run Method,
`ferrosegersTheta`	Computes Theta according to Ferro and Segers,
`exindexPlot`	Calculate and Plot Theta(1,2,3),
`exindexesPlot`	Calculate Theta(1,2) and Plot Theta(1).

Usage

## S4 method for signature 'fTHETA'
show(object)

thetaSim(model = c("max", "pair"), n = 1000, theta = 0.5)

blockTheta(x, block = 22, quantiles = seq(0.950, 0.995, length = 10),
    title = NULL, description = NULL)
clusterTheta(x, block = 22, quantiles = seq(0.950, 0.995, length = 10),
    title = NULL, description = NULL)
runTheta(x, block = 22, quantiles = seq(0.950, 0.995, length = 10),
    title = NULL, description = NULL)
ferrosegersTheta(x, quantiles = seq(0.950, 0.995, length = 10),
    title = NULL, description = NULL)
    
exindexPlot(x, block = c("monthly", "quarterly"), start = 5, end = NA, 
    doplot = TRUE, plottype = c("thresh", "K"), labels = TRUE, ...)
    
exindexesPlot(x, block = 22, quantiles = seq(0.950, 0.995, length = 10), 
    doplot = TRUE, labels = TRUE, ...)
## S4 method for signature 'fTHETA'
show(object)

thetaSim(model = c("max", "pair"), n = 1000, theta = 0.5)

blockTheta(x, block = 22, quantiles = seq(0.950, 0.995, length = 10),
    title = NULL, description = NULL)
clusterTheta(x, block = 22, quantiles = seq(0.950, 0.995, length = 10),
    title = NULL, description = NULL)
runTheta(x, block = 22, quantiles = seq(0.950, 0.995, length = 10),
    title = NULL, description = NULL)
ferrosegersTheta(x, quantiles = seq(0.950, 0.995, length = 10),
    title = NULL, description = NULL)
    
exindexPlot(x, block = c("monthly", "quarterly"), start = 5, end = NA, 
    doplot = TRUE, plottype = c("thresh", "K"), labels = TRUE, ...)
    
exindexesPlot(x, block = 22, quantiles = seq(0.950, 0.995, length = 10), 
    doplot = TRUE, labels = TRUE, ...)

Arguments

`block`	[Theta] - an integer value, the block size. Currently only integer specified block sizes are supported. [exindexPlot] - the block size. Either `"monthly"`, `"quarterly"` or an integer value. An integer value is interpreted as the number of data values in each successive block. The default value is `"monthly"` which corresponds for daily data to an approximately 22-day periods.
`description`	a character string which allows for a brief description.
`doplot`	a logical, should the results be plotted?
`labels`	whether or not axes should be labelled. If set to `FALSE` then user specified labels can be passed through the `"..."` argument.
`model`	[thetaSim] - a character string denoting the name of the model. Either `"max"` or `"pair"`, the first representing the maximum Frechet series, and the second the paired exponential series.
`n`	[thetaSim] - an integer value, the length of the time series to be generated.
`object`	an object of class `"fTHETA"` as returned by the functions `*Theta`.
`plottype`	[exindexPlot] - whether plot is to be by increasing threshold (`thresh`) or increasing K value (`K`).
`quantiles`	[exindexesPlot] - a numeric vector of quantile values.
`start`, `end`	[exindexPlot] - `start` is the lowest value of `K` at which to plot a point, and `end` the highest value; `K` is the number of blocks in which a specified threshold is exceeded.
`theta`	[thetaSim] - a numeric value between 0 and 1 setting the value of the extremal index for the maximum Frechet time series. (Not used in the case of the paired exponential series.)
`title`	a character string which allows for a project title.
`x`	a 'timeSeries' object or any other object which can be transformed by the function `as.vector` into a numeric vector. `"monthly"` and `"quarterly"` blocks require `x` to be an object of class `"timeSeries"`.
`...`	additional arguments passed to the plot function.

Value

exindexPlot
returns a data frame of results with the following columns: N, K, un, theta2, and theta. A plot with K on the lower x-axis and threshold Values on the upper x-axis versus the extremal index is displayed.

exindexesPlot
returns a data.frame with four columns: thresholds, theta1, theta2, and theta3. A plot with quantiles on the x-axis and versus the extremal indexes is displayed.

Author(s)

Alexander McNeil, for parts of the exindexPlot function, and
Diethelm Wuertz for the exindexesPlot function.

References

Embrechts, P., Klueppelberg, C., Mikosch, T. (1997); Modelling Extremal Events, Springer. Chapter 8, 413–429.

Examples

## Extremal Index for the right and left tails 
## of the BMW log returns:
   data(bmwRet)
   par(mfrow = c(2, 2), cex = 0.7)
   library(timeSeries)
   exindexPlot( as.timeSeries(bmwRet), block = "quarterly")
   exindexPlot(-as.timeSeries(bmwRet), block = "quarterly")   
   
## Extremal Index for the right and left tails 
## of the BMW log returns:
   exindexesPlot( as.timeSeries(bmwRet), block = 65)
   exindexesPlot(-as.timeSeries(bmwRet), block = 65)   
## Extremal Index for the right and left tails 
## of the BMW log returns:
   data(bmwRet)
   par(mfrow = c(2, 2), cex = 0.7)
   library(timeSeries)
   exindexPlot( as.timeSeries(bmwRet), block = "quarterly")
   exindexPlot(-as.timeSeries(bmwRet), block = "quarterly")   
   
## Extremal Index for the right and left tails 
## of the BMW log returns:
   exindexesPlot( as.timeSeries(bmwRet), block = 65)
   exindexesPlot(-as.timeSeries(bmwRet), block = 65)

Explorative Data Analysis

Description

A collection and description of functions for explorative data analysis. The tools include plot functions for empirical distributions, quantile plots, graphs exploring the properties of exceedances over a threshold, plots for mean/sum ratio and for the development of records.

The functions are:

`emdPlot`	Plot of empirical distribution function,
`qqparetoPlot`	Exponential/Pareto quantile plot,
`mePlot`	Plot of mean excesses over a threshold,
`mrlPlot`	another variant, mean residual life plot,
`mxfPlot`	another variant, with confidence intervals,
`msratioPlot`	Plot of the ratio of maximum and sum,
`recordsPlot`	Record development compared with iid data,
`ssrecordsPlot`	another variant, investigates subsamples,
`sllnPlot`	verifies Kolmogorov's strong law of large numbers,
`lilPlot`	verifies Hartman-Wintner's law of the iterated logarithm,
`xacfPlot`	ACF of exceedances over a threshold,
`normMeanExcessFit`	fits mean excesses with a normal density,
`ghMeanExcessFit`	fits mean excesses with a GH density,
`hypMeanExcessFit`	fits mean excesses with a HYP density,
`nigMeanExcessFit`	fits mean excesses with a NIG density,
`ghtMeanExcessFit`	fits mean excesses with a GHT density.

Usage

emdPlot(x, doplot = TRUE, plottype = c("xy", "x", "y", " "), 
    labels = TRUE, ...)

qqparetoPlot(x, xi = 0, trim = NULL, threshold = NULL, doplot = TRUE, 
    labels = TRUE, ...)

mePlot(x, doplot = TRUE, labels = TRUE, ...)
mrlPlot(x, ci = 0.95, umin = mean(x), umax = max(x), nint = 100, doplot = TRUE, 
     plottype = c("autoscale", ""), labels = TRUE, ...)  
mxfPlot(x, u = quantile(x, 0.05), doplot = TRUE, labels = TRUE, ...)  
   
msratioPlot(x, p = 1:4, doplot = TRUE, labels = TRUE, ...) 
   
recordsPlot(x, ci = 0.95, doplot = TRUE, labels = TRUE, ...)
ssrecordsPlot(x, subsamples = 10, doplot = TRUE, plottype = c("lin", "log"),
    labels = TRUE, ...)
    
sllnPlot(x, doplot = TRUE, labels = TRUE, ...)
lilPlot(x, doplot = TRUE, labels = TRUE, ...)

xacfPlot(x, u = quantile(x, 0.95), lag.max = 15, doplot = TRUE, 
    which = c("all", 1, 2, 3, 4), labels = TRUE, ...)
    
normMeanExcessFit(x, doplot = TRUE, trace = TRUE, ...)
ghMeanExcessFit(x, doplot = TRUE, trace = TRUE, ...)
hypMeanExcessFit(x, doplot = TRUE, trace = TRUE, ...)
nigMeanExcessFit(x, doplot = TRUE, trace = TRUE, ...)
ghtMeanExcessFit(x, doplot = TRUE, trace = TRUE, ...)
emdPlot(x, doplot = TRUE, plottype = c("xy", "x", "y", " "), 
    labels = TRUE, ...)

qqparetoPlot(x, xi = 0, trim = NULL, threshold = NULL, doplot = TRUE, 
    labels = TRUE, ...)

mePlot(x, doplot = TRUE, labels = TRUE, ...)
mrlPlot(x, ci = 0.95, umin = mean(x), umax = max(x), nint = 100, doplot = TRUE, 
     plottype = c("autoscale", ""), labels = TRUE, ...)  
mxfPlot(x, u = quantile(x, 0.05), doplot = TRUE, labels = TRUE, ...)  
   
msratioPlot(x, p = 1:4, doplot = TRUE, labels = TRUE, ...) 
   
recordsPlot(x, ci = 0.95, doplot = TRUE, labels = TRUE, ...)
ssrecordsPlot(x, subsamples = 10, doplot = TRUE, plottype = c("lin", "log"),
    labels = TRUE, ...)
    
sllnPlot(x, doplot = TRUE, labels = TRUE, ...)
lilPlot(x, doplot = TRUE, labels = TRUE, ...)

xacfPlot(x, u = quantile(x, 0.95), lag.max = 15, doplot = TRUE, 
    which = c("all", 1, 2, 3, 4), labels = TRUE, ...)
    
normMeanExcessFit(x, doplot = TRUE, trace = TRUE, ...)
ghMeanExcessFit(x, doplot = TRUE, trace = TRUE, ...)
hypMeanExcessFit(x, doplot = TRUE, trace = TRUE, ...)
nigMeanExcessFit(x, doplot = TRUE, trace = TRUE, ...)
ghtMeanExcessFit(x, doplot = TRUE, trace = TRUE, ...)

Arguments

`ci`	[recordsPlot] - a confidence level. By default 0.95, i.e. 95%.
`doplot`	a logical value. Should the results be plotted? By default `TRUE`.
`labels`	a logical value. Whether or not x- and y-axes should be automatically labelled and a default main title should be added to the plot. By default `TRUE`.
`lag.max`	[xacfPlot] - maximum number of lags at which to calculate the autocorrelation functions. The default value is 15.
`nint`	[mrlPlot] - the number of intervals, see `umin` and `umax`. The default value is 100.
`p`	[msratioPlot] - the power exponents, a numeric vector. By default a sequence from 1 to 4 in unit integer steps.
`plottype`	[emdPlot] - which axes should be on a log scale: `"x"` x-axis only; `"y"` y-axis only; `"xy"` both axes; `""` neither axis. [msratioPlot] - a logical, if set to `"autoscale"`, then the scale of the plots are automatically determined, any other string allows user specified scale information through the `...` argument. [ssrecordsPlot] - one from two options can be select either `"lin"` or `"log"`. The default creates a linear plot.
`subsamples`	[ssrecordsPlot] - the number of subsamples, by default 10, an integer value.
`threshold`, `trim`	[qPlot][xacfPlot] - a numeric value at which data are to be left-truncated, value at which data are to be right-truncated or the threshold value, by default 95%.
`trace`	a logical flag, by default `TRUE`. Should the calculations be traced?
`u`	a numeric value at which level the data are to be truncated. By default the threshold value which belongs to the 95% quantile, `u=quantile(x,0.95)`.
`umin`, `umax`	[mrlPlot] - range of threshold values. If `umin` and/or `umax` are not available, then by default they are set to the following values: `umin=mean(x)` and `umax=max(x)`.
`which`	[xacfPlot] - a numeric or character value, if `which="all"` then all four plots are displayed, if `which` is an integer between one and four, then the first, second, third or fourth plot will be displayed.
`x`, `y`	numeric data vectors or in the case of x an object to be plotted.
`xi`	the shape parameter of the generalized Pareto distribution.
`...`	additional arguments passed to the FUN or plot function.

Details

Empirical Distribution Function:

The function emdPlot is a simple explanatory function. A straight line on the double log scale indicates Pareto tail behaviour.

Quantile–Quantile Pareto Plot:

qqparetoPlot creates a quantile-quantile plot for threshold data. If xi is zero the reference distribution is the exponential; if xi is non-zero the reference distribution is the generalized Pareto with that parameter value expressed by xi. In the case of the exponential, the plot is interpreted as follows: Concave departures from a straight line are a sign of heavy-tailed behaviour, convex departures show thin-tailed behaviour.

Mean Excess Function Plot:

Three variants to plot the mean excess function are available: A sample mean excess plot over increasing thresholds, and two mean excess function plots with confidence intervals for discrimination in the tails of a distribution. In general, an upward trend in a mean excess function plot shows heavy-tailed behaviour. In particular, a straight line with positive gradient above some threshold is a sign of Pareto behaviour in tail. A downward trend shows thin-tailed behaviour whereas a line with zero gradient shows an exponential tail. Here are some hints: Because upper plotting points are the average of a handful of extreme excesses, these may be omitted for a prettier plot. For mrlPlot and mxfPlot the upper tail is investigated; for the lower tail reverse the sign of the data vector.

Plot of the Maximum/Sum Ratio:

The ratio of maximum and sum is a simple tool for detecting heavy tails of a distribution and for giving a rough estimate of the order of its finite moments. Sharp increases in the curves of a msratioPlot are a sign for heavy tail behaviour.

Plot of the Development of Records:

These are functions that investigate the development of records in a dataset and calculate the expected behaviour for iid data. recordsPlot counts records and reports the observations at which they occur. In addition subsamples can be investigated with the help of the function ssrecordsPlot.

Plot of Kolmogorov's and Hartman-Wintner's Laws:

The function sllnPlot verifies Kolmogorov's strong law of large numbers, and the function lilPlot verifies Hartman-Wintner's law of the iterated logarithm.

ACF Plot of Exceedances over a Threshold:

This function plots the autocorrelation functions of heights and distances of exceedances over a threshold.

Value

The functions return a plot.

Note

The plots are labeled by default with a x-label, a y-label and a main title. If the argument labels is set to FALSE neither a x-label, a y-label nor a main title will be added to the graph. To add user defined label strings just use the function title(xlab="\dots", ylab="\dots", main="\dots").

Author(s)

References

Coles S. (2001); Introduction to Statistical Modelling of Extreme Values, Springer.

Embrechts, P., Klueppelberg, C., Mikosch, T. (1997); Modelling Extremal Events, Springer.

Examples

 
## Danish fire insurance data:
   data(danishClaims)
   library(timeSeries)
   danishClaims = as.timeSeries(danishClaims)
   
## emdPlot -
   # Show Pareto tail behaviour:
   par(mfrow = c(2, 2), cex = 0.7)
   emdPlot(danishClaims) 
   
## qqparetoPlot -
   # QQ-Plot of heavy-tailed Danish fire insurance data:
   qqparetoPlot(danishClaims, xi = 0.7) 
 
## mePlot -
   # Sample mean excess plot of heavy-tailed Danish fire:
   mePlot(danishClaims)
      
## ssrecordsPlot -
   # Record fire insurance losses in Denmark:
   ssrecordsPlot(danishClaims, subsamples = 10) 
## Danish fire insurance data:
   data(danishClaims)
   library(timeSeries)
   danishClaims = as.timeSeries(danishClaims)
   
## emdPlot -
   # Show Pareto tail behaviour:
   par(mfrow = c(2, 2), cex = 0.7)
   emdPlot(danishClaims) 
   
## qqparetoPlot -
   # QQ-Plot of heavy-tailed Danish fire insurance data:
   qqparetoPlot(danishClaims, xi = 0.7) 
 
## mePlot -
   # Sample mean excess plot of heavy-tailed Danish fire:
   mePlot(danishClaims)
      
## ssrecordsPlot -
   # Record fire insurance losses in Denmark:
   ssrecordsPlot(danishClaims, subsamples = 10)

Generalized Extreme Value Distribution

Description

Density, distribution function, quantile function, random number generation, and true moments for the GEV including the Frechet, Gumbel, and Weibull distributions.

The GEV distribution functions are:

`dgev`	density of the GEV distribution,
`pgev`	probability function of the GEV distribution,
`qgev`	quantile function of the GEV distribution,
`rgev`	random variates from the GEV distribution,
`gevMoments`	computes true mean and variance,
`gevSlider`	displays density or rvs from a GEV.

Usage

dgev(x, xi = 1, mu = 0, beta = 1, log = FALSE)
pgev(q, xi = 1, mu = 0, beta = 1, lower.tail = TRUE)
qgev(p, xi = 1, mu = 0, beta = 1, lower.tail = TRUE)
rgev(n, xi = 1, mu = 0, beta = 1)

gevMoments(xi = 0, mu = 0, beta = 1)

gevSlider(method = c("dist", "rvs"))
dgev(x, xi = 1, mu = 0, beta = 1, log = FALSE)
pgev(q, xi = 1, mu = 0, beta = 1, lower.tail = TRUE)
qgev(p, xi = 1, mu = 0, beta = 1, lower.tail = TRUE)
rgev(n, xi = 1, mu = 0, beta = 1)

gevMoments(xi = 0, mu = 0, beta = 1)

gevSlider(method = c("dist", "rvs"))

Arguments

`log`	a logical, if `TRUE`, the log density is returned.
`lower.tail`	a logical, if `TRUE`, the default, then probabilities are `P[X <= x]`, otherwise, `P[X > x]`.
`method`	a character string denoting what should be displayed. Either the density and `"dist"` or random variates `"rvs"`.
`n`	the number of observations.
`p`	a numeric vector of probabilities. [hillPlot] - probability required when option `quantile` is chosen.
`q`	a numeric vector of quantiles.
`x`	a numeric vector of quantiles.
`xi`, `mu`, `beta`	`xi` is the shape parameter, `mu` the location parameter, and `beta` is the scale parameter. The default values are `xi=1`, `mu=0`, and `beta=1`. Note, if `xi=0` the distribution is of type Gumbel.

Value

d* returns the density,
p* returns the probability,
q* returns the quantiles, and
r* generates random variates.

All values are numeric vectors.

Author(s)

Alec Stephenson for R's evd and evir package, and
Diethelm Wuertz for this R-port.

References

Coles S. (2001); Introduction to Statistical Modelling of Extreme Values, Springer.

Embrechts, P., Klueppelberg, C., Mikosch, T. (1997); Modelling Extremal Events, Springer.

Examples

## rgev -
   # Create and plot 1000 Weibull distributed rdv:
   r = rgev(n = 1000, xi = -1)
   plot(r, type = "l", col = "steelblue", main = "Weibull Series")
   grid()
   
## dgev - 
   # Plot empirical density and compare with true density:
   hist(r[abs(r)<10], nclass = 25, freq = FALSE, xlab = "r", 
     xlim = c(-5,5), ylim = c(0,1.1), main = "Density")
   box()
   x = seq(-5, 5, by = 0.01)
   lines(x, dgev(x, xi = -1), col = "steelblue")
   
## pgev -
   # Plot df and compare with true df:
   plot(sort(r), (1:length(r)/length(r)), 
     xlim = c(-3, 6), ylim = c(0, 1.1),
     cex = 0.5, ylab = "p", xlab = "q", main = "Probability")
   grid()
   q = seq(-5, 5, by = 0.1)
   lines(q, pgev(q, xi = -1), col = "steelblue")
 
## qgev -   
   # Compute quantiles, a test:
   qgev(pgev(seq(-5, 5, 0.25), xi = -1), xi = -1)   

## gevMoments:
   # Returns true mean and variance:
   gevMoments(xi = 0, mu = 0, beta = 1)
   
## Slider:
   # gevSlider(method = "dist")
   # gevSlider(method = "rvs")
## rgev -
   # Create and plot 1000 Weibull distributed rdv:
   r = rgev(n = 1000, xi = -1)
   plot(r, type = "l", col = "steelblue", main = "Weibull Series")
   grid()
   
## dgev - 
   # Plot empirical density and compare with true density:
   hist(r[abs(r)<10], nclass = 25, freq = FALSE, xlab = "r", 
     xlim = c(-5,5), ylim = c(0,1.1), main = "Density")
   box()
   x = seq(-5, 5, by = 0.01)
   lines(x, dgev(x, xi = -1), col = "steelblue")
   
## pgev -
   # Plot df and compare with true df:
   plot(sort(r), (1:length(r)/length(r)), 
     xlim = c(-3, 6), ylim = c(0, 1.1),
     cex = 0.5, ylab = "p", xlab = "q", main = "Probability")
   grid()
   q = seq(-5, 5, by = 0.1)
   lines(q, pgev(q, xi = -1), col = "steelblue")
 
## qgev -   
   # Compute quantiles, a test:
   qgev(pgev(seq(-5, 5, 0.25), xi = -1), xi = -1)   

## gevMoments:
   # Returns true mean and variance:
   gevMoments(xi = 0, mu = 0, beta = 1)
   
## Slider:
   # gevSlider(method = "dist")
   # gevSlider(method = "rvs")

Generalized Extreme Value Modelling

Description

A collection and description functions to estimate the parameters of the GEV distribution. To model the GEV three types of approaches for parameter estimation are provided: Maximum likelihood estimation, probability weighted moment method, and estimation by the MDA approach. MDA includes functions for the Pickands, Einmal-Decker-deHaan, and Hill estimators together with several plot variants.

Maximum Domain of Attraction estimators:

`hillPlot`	shape parameter and Hill estimate of the tail index,
`shaparmPlot`	variation of shape parameter with tail depth.

Usage

hillPlot(x, start = 15, ci = 0.95, 
    doplot = TRUE, plottype = c("alpha", "xi"), labels = TRUE, ...)
shaparmPlot(x, p = 0.01*(1:10), xiRange = NULL, alphaRange = NULL,
    doplot = TRUE, plottype = c("both", "upper"))
    
shaparmPickands(x, p = 0.05, xiRange = NULL,  
    doplot = TRUE, plottype = c("both", "upper"), labels = TRUE, ...) 
shaparmHill(x, p = 0.05, xiRange = NULL,  
    doplot = TRUE, plottype = c("both", "upper"), labels = TRUE, ...)
shaparmDEHaan(x, p = 0.05, xiRange = NULL,  
    doplot = TRUE, plottype = c("both", "upper"), labels = TRUE, ...)
hillPlot(x, start = 15, ci = 0.95, 
    doplot = TRUE, plottype = c("alpha", "xi"), labels = TRUE, ...)
shaparmPlot(x, p = 0.01*(1:10), xiRange = NULL, alphaRange = NULL,
    doplot = TRUE, plottype = c("both", "upper"))
    
shaparmPickands(x, p = 0.05, xiRange = NULL,  
    doplot = TRUE, plottype = c("both", "upper"), labels = TRUE, ...) 
shaparmHill(x, p = 0.05, xiRange = NULL,  
    doplot = TRUE, plottype = c("both", "upper"), labels = TRUE, ...)
shaparmDEHaan(x, p = 0.05, xiRange = NULL,  
    doplot = TRUE, plottype = c("both", "upper"), labels = TRUE, ...)

Arguments

`alphaRange`, `xiRange`	[saparmPlot] - plotting ranges for `alpha` and `xi`. By default the values are automatically selected.
`ci`	[hillPlot] - probability for asymptotic confidence band; for no confidence band set `ci` to zero.
`doplot`	a logical. Should the results be plotted? [shaparmPlot] - a vector of logicals of the same lengths as tails defining for which tail depths plots should be created, by default plots will be generated for a tail depth of 5 percent. By default `c(FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE)`.
`labels`	[hillPlot] - whether or not axes should be labelled.
`plottype`	[hillPlot] - whether `alpha`, `xi` (1/alpha) or `quantile` (a quantile estimate) should be plotted.
`p`	[qgev] - a numeric vector of probabilities. [hillPlot] - probability required when option `quantile` is chosen.
`start`	[hillPlot] - lowest number of order statistics at which to plot a point.
`x`	[dgev][devd] - a numeric vector of quantiles. [gevFit] - data vector. In the case of `method="mle"` the interpretation depends on the value of block: if no block size is specified then data are interpreted as block maxima; if block size is set, then data are interpreted as raw data and block maxima are calculated. [hillPlot][shaparmPlot] - the data from which to calculate the shape parameter, a numeric vector. [print][plot] - a fitted object of class `"gevFit"`.
`...`	[gevFit] - control parameters optionally passed to the optimization function. Parameters for the optimization function are passed to components of the `control` argument of `optim`. [hillPlot] - other graphics parameters. [plot][summary] - arguments passed to the plot function.

Details

Parameter Estimation:

gevFit and gumbelFit estimate the parameters either by the probability weighted moment method, method="pwm" or by maximum log likelihood estimation method="mle". The summary method produces diagnostic plots for fitted GEV or Gumbel models.

Methods:

print.gev, plot.gev and summary.gev are print, plot, and summary methods for a fitted object of class gev. Concerning the summary method, the data are converted to unit exponentially distributed residuals under null hypothesis that GEV fits. Two diagnostics for iid exponential data are offered. The plot method provides two different residual plots for assessing the fitted GEV model. Two diagnostics for iid exponential data are offered.

Return Level Plot:

gevrlevelPlot calculates and plots the k-block return level and 95% confidence interval based on a GEV model for block maxima, where k is specified by the user. The k-block return level is that level exceeded once every k blocks, on average. The GEV likelihood is reparameterized in terms of the unknown return level and profile likelihood arguments are used to construct a confidence interval.

Hill Plot:

The function hillPlot investigates the shape parameter and plots the Hill estimate of the tail index of heavy-tailed data, or of an associated quantile estimate. This plot is usually calculated from the alpha perspective. For a generalized Pareto analysis of heavy-tailed data using the gpdFit function, it helps to plot the Hill estimates for xi.

Shape Parameter Plot:

The function shaparmPlot investigates the shape parameter and plots for the upper and lower tails the shape parameter as a function of the taildepth. Three approaches are considered, the Pickands estimator, the Hill estimator, and the Decker-Einmal-deHaan estimator.

Value

gevSim
returns a vector of data points from the simulated series.

gevFit
returns an object of class gev describing the fit.

print.summary
prints a report of the parameter fit.

summary
performs diagnostic analysis. The method provides two different residual plots for assessing the fitted GEV model.

gevrlevelPlot
returns a vector containing the lower 95% bound of the confidence interval, the estimated return level and the upper 95% bound.

hillPlot
displays a plot.

shaparmPlot
returns a list with one or two entries, depending on the selection of the input variable both.tails. The two entries upper and lower determine the position of the tail. Each of the two variables is again a list with entries pickands, hill, and dehaan. If one of the three methods will be discarded the printout will display zeroes.

Note

GEV Parameter Estimation:

If method "mle" is selected the parameter fitting in gevFit is passed to the internal function gev.mle or gumbel.mle depending on the value of gumbel, FALSE or TRUE. On the other hand, if method "pwm" is selected the parameter fitting in gevFit is passed to the internal function gev.pwm or gumbel.pwm again depending on the value of gumbel, FALSE or TRUE.

Author(s)

Alec Stephenson for R's evd and evir package, and
Diethelm Wuertz for this R-port.

References

Coles S. (2001); Introduction to Statistical Modelling of Extreme Values, Springer.

Embrechts, P., Klueppelberg, C., Mikosch, T. (1997); Modelling Extremal Events, Springer.

Examples

  
## Load Data:
   library(timeSeries)
   x = as.timeSeries(data(danishClaims))
   colnames(x) <- "Danish"
   head(x)
   
## hillPlot -
   # Hill plot of heavy-tailed Danish fire insurance data 
   par(mfrow = c(1, 1))
   hillPlot(x, plottype = "xi")
   grid()
## Load Data:
   library(timeSeries)
   x = as.timeSeries(data(danishClaims))
   colnames(x) <- "Danish"
   head(x)
   
## hillPlot -
   # Hill plot of heavy-tailed Danish fire insurance data 
   par(mfrow = c(1, 1))
   hillPlot(x, plottype = "xi")
   grid()

Generalized Extreme Value Modelling

Description

The GEV modelling functions are:

`gevSim`	generates data from the GEV distribution,
`gumbelSim`	generates data from the Gumbel distribution,
`gevFit`	fits data to the GEV distribution,
`gumbelFit`	fits data to the Gumbel distribution,
`print`	print method for a fitted GEV object,
`plot`	plot method for a fitted GEV object,
`summary`	summary method for a fitted GEV object,
`gevrlevelPlot`	k-block return level with confidence intervals.

Usage

gevSim(model = list(xi = -0.25, mu = 0, beta = 1), n = 1000, seed = NULL)
gumbelSim(model = list(mu = 0, beta = 1), n = 1000, seed = NULL)

gevFit(x, block = 1, type = c("mle", "pwm"), title = NULL, description = NULL, ...)
gumbelFit(x, block = 1, type = c("mle", "pwm"), title = NULL, description = NULL, ...)

## S4 method for signature 'fGEVFIT'
show(object)
## S3 method for class 'fGEVFIT'
plot(x, which = "ask", ...)
## S3 method for class 'fGEVFIT'
summary(object, doplot = TRUE, which = "all", ...)
gevSim(model = list(xi = -0.25, mu = 0, beta = 1), n = 1000, seed = NULL)
gumbelSim(model = list(mu = 0, beta = 1), n = 1000, seed = NULL)

gevFit(x, block = 1, type = c("mle", "pwm"), title = NULL, description = NULL, ...)
gumbelFit(x, block = 1, type = c("mle", "pwm"), title = NULL, description = NULL, ...)

## S4 method for signature 'fGEVFIT'
show(object)
## S3 method for class 'fGEVFIT'
plot(x, which = "ask", ...)
## S3 method for class 'fGEVFIT'
summary(object, doplot = TRUE, which = "all", ...)

Arguments

`block`	block size.
`description`	a character string which allows for a brief description.
`doplot`	a logical. Should the results be plotted? [shaparmPlot] - a vector of logicals of the same lengths as tails defining for which tail depths plots should be created, by default plots will be generated for a tail depth of 5 percent. By default `c(FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE)`.
`model`	[gevSim][gumbelSim] - a list with components `shape`, `location` and `scale` giving the parameters of the GEV distribution. By default the shape parameter has the value -0.25, the location is zero and the scale is one. To fit random deviates from a Gumbel distribution set `shape=0`.
`n`	[gevSim][gumbelSim] - number of generated data points, an integer value. [rgev] - the number of observations.
`object`	[summary][grlevelPlot] - a fitted object of class `"gevFit"`.
`seed`	[gevSim] - an integer value to set the seed for the random number generator.
`title`	[gevFit] - a character string which allows for a project title.
`type`	a character string denoting the type of parameter estimation, either by maximum likelihood estimation `"mle"`, the default value, or by the probability weighted moment method `"pwm"`.
`which`	[plot][summary] - a vector of logicals, one for each plot, denoting which plot should be displayed. Alternatively if `which="ask"` the user will be interactively asked which of the plots should be displayed. By default `which="all"`.
`x`	[dgev][devd] - a numeric vector of quantiles. [gevFit] - data vector. In the case of `method="mle"` the interpretation depends on the value of block: if no block size is specified then data are interpreted as block maxima; if block size is set, then data are interpreted as raw data and block maxima are calculated. [hillPlot][shaparmPlot] - the data from which to calculate the shape parameter, a numeric vector. [print][plot] - a fitted object of class `"gevFit"`.
`xi`, `mu`, `beta`	[*gev] - `xi` is the shape parameter, `mu` the location parameter, and `beta` is the scale parameter. The default values are `xi=1`, `mu=0`, and `beta=1`. Note, if `xi=0` the distribution is of type Gumbel.
`...`	[gevFit] - control parameters optionally passed to the optimization function. Parameters for the optimization function are passed to components of the `control` argument of `optim`. [hillPlot] - other graphics parameters. [plot][summary] - arguments passed to the plot function.

Details

Value

gevSim
returns a vector of data points from the simulated series.

gevFit
returns an object of class gev describing the fit.

print.summary
prints a report of the parameter fit.

summary
performs diagnostic analysis. The method provides two different residual plots for assessing the fitted GEV model.

gevrlevelPlot
returns a vector containing the lower 95% bound of the confidence interval, the estimated return level and the upper 95% bound.

hillPlot
displays a plot.

Note

Author(s)

Alec Stephenson for R's evd and evir package, and
Diethelm Wuertz for this R-port.

References

Coles S. (2001); Introduction to Statistical Modelling of Extreme Values, Springer.

Embrechts, P., Klueppelberg, C., Mikosch, T. (1997); Modelling Extremal Events, Springer.

Examples

## gevSim -
   # Simulate GEV Data, use default length n=1000
   x = gevSim(model = list(xi = 0.25, mu = 0 , beta = 1), n = 1000)
   head(x)

## gumbelSim -
   # Simulate GEV Data, use default length n=1000
   x = gumbelSim(model = list(xi = 0.25, mu = 0 , beta = 1))
     
## gevFit -
   # Fit GEV Data by Probability Weighted Moments:
   fit = gevFit(x, type = "pwm") 
   print(fit)
   
## summary -
   # Summarize Results:
   par(mfcol = c(2, 2))
   summary(fit)
## gevSim -
   # Simulate GEV Data, use default length n=1000
   x = gevSim(model = list(xi = 0.25, mu = 0 , beta = 1), n = 1000)
   head(x)

## gumbelSim -
   # Simulate GEV Data, use default length n=1000
   x = gumbelSim(model = list(xi = 0.25, mu = 0 , beta = 1))
     
## gevFit -
   # Fit GEV Data by Probability Weighted Moments:
   fit = gevFit(x, type = "pwm") 
   print(fit)
   
## summary -
   # Summarize Results:
   par(mfcol = c(2, 2))
   summary(fit)

Generalized Extreme Value Modelling

Description

The GEV modelling functions are:

gevrlevelPlot k-block return level with confidence intervals.

Usage

gevrlevelPlot(object, kBlocks = 20,  ci = c(0.90, 0.95, 0.99), 
    plottype = c("plot", "add"), labels = TRUE,...)
gevrlevelPlot(object, kBlocks = 20,  ci = c(0.90, 0.95, 0.99), 
    plottype = c("plot", "add"), labels = TRUE,...)

Arguments

`add`	[gevrlevelPlot] - whether the return level should be added graphically to a time series plot; if `FALSE` a graph of the profile likelihood curve showing the return level and its confidence interval is produced.
`ci`	[hillPlot] - probability for asymptotic confidence band; for no confidence band set `ci` to zero.
`kBlocks`	[gevrlevelPlot] - specifies the particular return level to be estimated; default set arbitrarily to 20.
`labels`	[hillPlot] - whether or not axes should be labelled.
`object`	[summary][grlevelPlot] - a fitted object of class `"gevFit"`.
`plottype`	[hillPlot] - whether `alpha`, `xi` (1/alpha) or `quantile` (a quantile estimate) should be plotted.
`...`	arguments passed to the plot function.

Details

Value

gevSim
returns a vector of data points from the simulated series.

gevFit
returns an object of class gev describing the fit.

print.summary
prints a report of the parameter fit.

summary
performs diagnostic analysis. The method provides two different residual plots for assessing the fitted GEV model.

gevrlevelPlot
returns a vector containing the lower 95% bound of the confidence interval, the estimated return level and the upper 95% bound.

hillPlot
displays a plot.

Note

Author(s)

Alec Stephenson for R's evd and evir package, and
Diethelm Wuertz for this R-port.

References

Coles S. (2001); Introduction to Statistical Modelling of Extreme Values, Springer.

Embrechts, P., Klueppelberg, C., Mikosch, T. (1997); Modelling Extremal Events, Springer.

Examples

  
## Load Data:
   # BMW Stock Data - negative returns
   library(timeSeries)
   x = -as.timeSeries(data(bmwRet))
   colnames(x)<-"BMW"
   head(x)
   
## gevFit -
   # Fit GEV to monthly Block Maxima:
   fit = gevFit(x, block = "month")  
   print(fit)
   
## gevrlevelPlot -
   # Return Level Plot:
   gevrlevelPlot(fit)
## Load Data:
   # BMW Stock Data - negative returns
   library(timeSeries)
   x = -as.timeSeries(data(bmwRet))
   colnames(x)<-"BMW"
   head(x)
   
## gevFit -
   # Fit GEV to monthly Block Maxima:
   fit = gevFit(x, block = "month")  
   print(fit)
   
## gevrlevelPlot -
   # Return Level Plot:
   gevrlevelPlot(fit)

Generalized Pareto Distribution

Description

A collection and description of functions to compute the generalized Pareto distribution. The functions compute density, distribution function, quantile function and generate random deviates for the GPD. In addition functions to compute the true moments and to display the distribution and random variates changing parameters interactively are available.

The GPD distribution functions are:

`dgpd`	Density of the GPD Distribution,
`pgpd`	Probability function of the GPD Distribution,
`qgpd`	Quantile function of the GPD Distribution,
`rgpd`	random variates from the GPD distribution,
`gpdMoments`	computes true mean and variance,
`gpdSlider`	displays density or rvs from a GPD.

Usage

dgpd(x, xi = 1, mu = 0, beta = 1, log = FALSE) 
pgpd(q, xi = 1, mu = 0, beta = 1, lower.tail = TRUE) 
qgpd(p, xi = 1, mu = 0, beta = 1, lower.tail = TRUE) 
rgpd(n, xi = 1, mu = 0, beta = 1)

gpdMoments(xi = 1, mu = 0, beta = 1)
gpdSlider(method = c("dist", "rvs"))
dgpd(x, xi = 1, mu = 0, beta = 1, log = FALSE) 
pgpd(q, xi = 1, mu = 0, beta = 1, lower.tail = TRUE) 
qgpd(p, xi = 1, mu = 0, beta = 1, lower.tail = TRUE) 
rgpd(n, xi = 1, mu = 0, beta = 1)

gpdMoments(xi = 1, mu = 0, beta = 1)
gpdSlider(method = c("dist", "rvs"))

Arguments

`log`	a logical, if `TRUE`, the log density is returned.
`lower.tail`	a logical, if `TRUE`, the default, then probabilities are `P[X <= x]`, otherwise, `P[X > x]`.
`method`	[gpdSlider] - a character string denoting what should be displayed. Either the density and `"dist"` or random variates `"rvs"`.
`n`	[rgpd][gpdSim\ - the number of observations to be generated.
`p`	a vector of probability levels, the desired probability for the quantile estimate (e.g. 0.99 for the 99th percentile).
`q`	[pgpd] - a numeric vector of quantiles.
`x`	[dgpd] - a numeric vector of quantiles.
`xi`, `mu`, `beta`	`xi` is the shape parameter, `mu` the location parameter, and `beta` is the scale parameter.

Value

All values are numeric vectors:
d* returns the density,
p* returns the probability,
q* returns the quantiles, and
r* generates random deviates.

Author(s)

Alec Stephenson for the functions from R's evd package,
Alec Stephenson for the functions from R's evir package,
Alexander McNeil for the EVIS functions underlying the evir package,
Diethelm Wuertz for this R-port.

References

Embrechts, P., Klueppelberg, C., Mikosch, T. (1997); Modelling Extremal Events, Springer.

Examples

## rgpd  -
   par(mfrow = c(2, 2), cex = 0.7)
   r = rgpd(n = 1000, xi = 1/4)
   plot(r, type = "l", col = "steelblue", main = "GPD Series")
   grid()
   
## dgpd -
   # Plot empirical density and compare with true density:
   # Omit values greater than 500 from plot
   hist(r, n = 50, probability = TRUE, xlab = "r", 
     col = "steelblue", border = "white",
     xlim = c(-1, 5), ylim = c(0, 1.1), main = "Density")
   box()
   x = seq(-5, 5, by = 0.01)
   lines(x, dgpd(x, xi = 1/4), col = "orange")
   
## pgpd -
   # Plot df and compare with true df:
   plot(sort(r), (1:length(r)/length(r)), 
     xlim = c(-3, 6), ylim = c(0, 1.1), pch = 19, 
     cex = 0.5, ylab = "p", xlab = "q", main = "Probability")
   grid()
   q = seq(-5, 5, by = 0.1)
   lines(q, pgpd(q, xi = 1/4), col = "steelblue")
   
## qgpd -
   # Compute quantiles, a test:
   qgpd(pgpd(seq(-1, 5, 0.25), xi = 1/4 ), xi = 1/4) 
## rgpd  -
   par(mfrow = c(2, 2), cex = 0.7)
   r = rgpd(n = 1000, xi = 1/4)
   plot(r, type = "l", col = "steelblue", main = "GPD Series")
   grid()
   
## dgpd -
   # Plot empirical density and compare with true density:
   # Omit values greater than 500 from plot
   hist(r, n = 50, probability = TRUE, xlab = "r", 
     col = "steelblue", border = "white",
     xlim = c(-1, 5), ylim = c(0, 1.1), main = "Density")
   box()
   x = seq(-5, 5, by = 0.01)
   lines(x, dgpd(x, xi = 1/4), col = "orange")
   
## pgpd -
   # Plot df and compare with true df:
   plot(sort(r), (1:length(r)/length(r)), 
     xlim = c(-3, 6), ylim = c(0, 1.1), pch = 19, 
     cex = 0.5, ylab = "p", xlab = "q", main = "Probability")
   grid()
   q = seq(-5, 5, by = 0.1)
   lines(q, pgpd(q, xi = 1/4), col = "steelblue")
   
## qgpd -
   # Compute quantiles, a test:
   qgpd(pgpd(seq(-1, 5, 0.25), xi = 1/4 ), xi = 1/4)

GPD Distributions for Extreme Value Theory

Description

A collection and description to functions to fit and to simulate processes that are generated from the generalized Pareto distribution. Two approaches for parameter estimation are provided: Maximum likelihood estimation and the probability weighted moment method.

The GPD modelling functions are:

`gpdSim`	generates data from the GPD,
`gpdFit`	fits empirical or simulated data to the distribution,
`print`	print method for a fitted GPD object of class ...,
`plot`	plot method for a fitted GPD object,
`summary`	summary method for a fitted GPD object.

Usage

gpdSim(model = list(xi = 0.25, mu = 0, beta = 1), n = 1000,
    seed = NULL)
gpdFit(x, u = quantile(x, 0.95), type = c("mle", "pwm"), information = 
    c("observed", "expected"), title = NULL, description = NULL, ...)

## S4 method for signature 'fGPDFIT'
show(object)
## S3 method for class 'fGPDFIT'
plot(x, which = "ask", ...)
## S3 method for class 'fGPDFIT'
summary(object, doplot = TRUE, which = "all", ...)
gpdSim(model = list(xi = 0.25, mu = 0, beta = 1), n = 1000,
    seed = NULL)
gpdFit(x, u = quantile(x, 0.95), type = c("mle", "pwm"), information = 
    c("observed", "expected"), title = NULL, description = NULL, ...)

## S4 method for signature 'fGPDFIT'
show(object)
## S3 method for class 'fGPDFIT'
plot(x, which = "ask", ...)
## S3 method for class 'fGPDFIT'
summary(object, doplot = TRUE, which = "all", ...)

Arguments

`description`	a character string which allows for a brief description.
`doplot`	a logical. Should the results be plotted?
`information`	whether standard errors should be calculated with `"observed"` or `"expected"` information. This only applies to the maximum likelihood method; for the probability-weighted moments method `"expected"` information is used if possible.
`model`	[gpdSim] - a list with components `shape`, `location` and `scale` giving the parameters of the GPD distribution. By default the shape parameter has the value 0.25, the location is zero and the scale is one.
`n`	[rgpd][gpdSim\ - the number of observations to be generated.
`object`	[summary] - a fitted object of class `"gpdFit"`.
`seed`	[gpdSim] - an integer value to set the seed for the random number generator.
`title`	a character string which allows for a project title.
`type`	a character string selecting the desired estimation method, either `"mle"` for the maximum likelihood method or `"pwm"` for the probability weighted moment method. By default, the first will be selected. Note, the function `gpd` uses `"ml"`.
`u`	the threshold value.
`which`	if `which` is set to `"ask"` the function will interactively ask which plot should be displayed. By default this value is set to `FALSE` and then those plots will be displayed for which the elements in the logical vector `which` ar set to `TRUE`; by default all four elements are set to `"all"`.
`x`	[dgpd] - a numeric vector of quantiles. [gpdFit] - the data vector. Note, there are two different names for the first argument `x` and `data` depending which function name is used, either `gpdFit` or the EVIS synonym `gpd`. [print][plot] - a fitted object of class `"gpdFit"`.
`xi`, `mu`, `beta`	`xi` is the shape parameter, `mu` the location parameter, and `beta` is the scale parameter.
`...`	control parameters and plot parameters optionally passed to the optimization and/or plot function. Parameters for the optimization function are passed to components of the `control` argument of `optim`.

Details

Generalized Pareto Distribution:

Compute density, distribution function, quantile function and generates random variates for the Generalized Pareto Distribution.

Simulation:

gpdSim simulates data from a Generalized Pareto distribution.

Parameter Estimation:

gpdFit fits the model parameters either by the probability weighted moment method or the maxim log likelihood method. The function returns an object of class "gpd" representing the fit of a generalized Pareto model to excesses over a high threshold. The fitting functions use the probability weighted moment method, if method method="pwm" was selected, and the the general purpose optimization function optim when the maximum likelihood estimation, method="mle" or method="ml" is chosen.

Methods:

print.gpd, plot.gpd and summary.gpd are print, plot, and summary methods for a fitted object of class gpdFit. The plot method provides four different plots for assessing fitted GPD model.

gpd* Functions:

gpdqPlot calculates quantile estimates and confidence intervals for high quantiles above the threshold in a GPD analysis, and adds a graphical representation to an existing plot. The GPD approximation in the tail is used to estimate quantile. The "wald" method uses the observed Fisher information matrix to calculate confidence interval. The "likelihood" method reparametrizes the likelihood in terms of the unknown quantile and uses profile likelihood arguments to construct a confidence interval.

gpdquantPlot creates a plot showing how the estimate of a high quantile in the tail of a dataset based on the GPD approximation varies with threshold or number of extremes. For every model gpdFit is called. Evaluation may be slow. Confidence intervals by the Wald method may be fastest.

gpdriskmeasures makes a rapid calculation of point estimates of prescribed quantiles and expected shortfalls using the output of the function gpdFit. This function simply calculates point estimates and (at present) makes no attempt to calculate confidence intervals for the risk measures. If confidence levels are required use gpdqPlot and gpdsfallPlot which interact with graphs of the tail of a loss distribution and are much slower.

gpdsfallPlot calculates expected shortfall estimates, in other words tail conditional expectation and confidence intervals for high quantiles above the threshold in a GPD analysis. A graphical representation to an existing plot is added. Expected shortfall is the expected size of the loss, given that a particular quantile of the loss distribution is exceeded. The GPD approximation in the tail is used to estimate expected shortfall. The likelihood is reparametrized in terms of the unknown expected shortfall and profile likelihood arguments are used to construct a confidence interval.

gpdshapePlot creates a plot showing how the estimate of shape varies with threshold or number of extremes. For every model gpdFit is called. Evaluation may be slow.

gpdtailPlot produces a plot of the tail of the underlying distribution of the data.

Value

gpdSim
returns a vector of datapoints from the simulated series.

gpdFit
returns an object of class "gpd" describing the fit including parameter estimates and standard errors.

gpdQuantPlot
returns invisible a table of results.

gpdShapePlot
returns invisible a table of results.

gpdTailPlot
returns invisible a list object containing details of the plot is returned invisibly. This object should be used as the first argument of gpdqPlot or gpdsfallPlot to add quantile estimates or expected shortfall estimates to the plot.

Author(s)

References

Embrechts, P., Klueppelberg, C., Mikosch, T. (1997); Modelling Extremal Events, Springer.

Hosking J.R.M., Wallis J.R., (1987); Parameter and quantile estimation for the generalized Pareto distribution, Technometrics 29, 339–349.

Examples

## gpdSim  -
   x = gpdSim(model = list(xi = 0.25, mu = 0, beta = 1), n = 1000)
## gpdFit - 
   par(mfrow = c(2, 2), cex = 0.7)  
   fit = gpdFit(x, u = min(x), type = "pwm") 
   print(fit)
   summary(fit)   
## gpdSim  -
   x = gpdSim(model = list(xi = 0.25, mu = 0, beta = 1), n = 1000)
## gpdFit - 
   par(mfrow = c(2, 2), cex = 0.7)  
   fit = gpdFit(x, u = min(x), type = "pwm") 
   print(fit)
   summary(fit)

GPD Distributions for Extreme Value Theory

Description

A collection and description to functions to compute tail risk under the GPD approach.

The GPD modelling functions are:

`gpdQPlot`	estimation of high quantiles,
`gpdQuantPlot`	variation of high quantiles with threshold,
`gpdRiskMeasures`	prescribed quantiles and expected shortfalls,
`gpdSfallPlot`	expected shortfall with confidence intervals,
`gpdShapePlot`	variation of shape with threshold,
`gpdTailPlot`	plot of the tail,
`tailPlot`	,
`tailSlider`	,
`tailRisk`	.

Usage


gpdQPlot(x, p = 0.99, ci = 0.95, type = c("likelihood", "wald"),  
    like.num = 50)
gpdQuantPlot(x, p = 0.99, ci = 0.95, models = 30, start = 15, end = 500,
    doplot = TRUE, plottype = c("normal", "reverse"), labels = TRUE,
    ...) 
gpdSfallPlot(x, p = 0.99, ci = 0.95, like.num = 50)
gpdShapePlot(x, ci = 0.95, models = 30, start = 15, end = 500,
    doplot = TRUE, plottype = c("normal", "reverse"), labels = TRUE,
    ...) 
gpdTailPlot(object, plottype = c("xy", "x", "y", ""), doplot = TRUE, 
    extend = 1.5, labels = TRUE, ...)

gpdRiskMeasures(object, prob = c(0.99, 0.995, 0.999, 0.9995, 0.9999))

tailPlot(object, p = 0.99, ci = 0.95, nLLH = 25, extend = 1.5, grid =
    TRUE, labels = TRUE, ...) 
tailSlider(x)
tailRisk(object, prob = c(0.99, 0.995, 0.999, 0.9995, 0.9999), ...)
gpdQPlot(x, p = 0.99, ci = 0.95, type = c("likelihood", "wald"),  
    like.num = 50)
gpdQuantPlot(x, p = 0.99, ci = 0.95, models = 30, start = 15, end = 500,
    doplot = TRUE, plottype = c("normal", "reverse"), labels = TRUE,
    ...) 
gpdSfallPlot(x, p = 0.99, ci = 0.95, like.num = 50)
gpdShapePlot(x, ci = 0.95, models = 30, start = 15, end = 500,
    doplot = TRUE, plottype = c("normal", "reverse"), labels = TRUE,
    ...) 
gpdTailPlot(object, plottype = c("xy", "x", "y", ""), doplot = TRUE, 
    extend = 1.5, labels = TRUE, ...)

gpdRiskMeasures(object, prob = c(0.99, 0.995, 0.999, 0.9995, 0.9999))

tailPlot(object, p = 0.99, ci = 0.95, nLLH = 25, extend = 1.5, grid =
    TRUE, labels = TRUE, ...) 
tailSlider(x)
tailRisk(object, prob = c(0.99, 0.995, 0.999, 0.9995, 0.9999), ...)

Arguments

`ci`	the probability for asymptotic confidence band; for no confidence band set to zero.
`doplot`	a logical. Should the results be plotted?
`extend`	optional argument for plots 1 and 2 expressing how far x-axis should extend as a multiple of the largest data value. This argument must take values greater than 1 and is useful for showing estimated quantiles beyond data.
`grid`	...
`labels`	optional argument for plots 1 and 2 specifying whether or not axes should be labelled.
`like.num`	the number of times to evaluate profile likelihood.
`models`	the number of consecutive gpd models to be fitted.
`nLLH`	...
`object`	[summary] - a fitted object of class `"gpdFit"`.
`p`	a vector of probability levels, the desired probability for the quantile estimate (e.g. 0.99 for the 99th percentile).
`reverse`	should plot be by increasing threshold (`TRUE`) or number of extremes (`FALSE`).
`prob`	a numeric value.
`plottype`	a character string.
`start`, `end`	the lowest and maximum number of exceedances to be considered.
`type`	a character string selecting the desired estimation method, either `"mle"` for the maximum likelihood method or `"pwm"` for the probability weighted moment method. By default, the first will be selected. Note, the function `gpd` uses `"ml"`.
`x`	[dgpd] - a numeric vector of quantiles. [gpdFit] - the data vector. Note, there are two different names for the first argument `x` and `data` depending which function name is used, either `gpdFit` or the EVIS synonym `gpd`. [print][plot] - a fitted object of class `"gpdFit"`.
`...`	control parameters and plot parameters optionally passed to the optimization and/or plot function. Parameters for the optimization function are passed to components of the `control` argument of `optim`.

Details

Generalized Pareto Distribution:

Compute density, distribution function, quantile function and generates random variates for the Generalized Pareto Distribution.

Simulation:

gpdSim simulates data from a Generalized Pareto distribution.

gpdsfallPlot calculates expected shortfall estimates, in other words tail conditional expectation and confidence intervals for high quantiles above the threshold in a GPD analysis. A graphicalx representation to an existing plot is added. Expected shortfall is the expected size of the loss, given that a particular quantile of the loss distribution is exceeded. The GPD approximation in the tail is used to estimate expected shortfall. The likelihood is reparametrized in terms of the unknown expected shortfall and profile likelihood arguments are used to construct a confidence interval.

gpdshapePlot creates a plot showing how the estimate of shape varies with threshold or number of extremes. For every model gpdFit is called. Evaluation may be slow.

gpdtailPlot produces a plot of the tail of the underlying distribution of the data.

Value

gpdSim
returns a vector of datapoints from the simulated series.

gpdFit
returns an object of class "gpd" describing the fit including parameter estimates and standard errors.

gpdQuantPlot
returns invisible a table of results.

gpdShapePlot
returns invisible a table of results.

Author(s)

References

Embrechts, P., Klueppelberg, C., Mikosch, T. (1997); Modelling Extremal Events, Springer.

Hosking J.R.M., Wallis J.R., (1987); Parameter and quantile estimation for the generalized Pareto distribution, Technometrics 29, 339–349.

Examples

## Load Data:
   library(timeSeries)
   danish = as.timeSeries(data(danishClaims))

## Tail Plot:
   x = as.timeSeries(data(danishClaims))
   fit = gpdFit(x, u = 10)
   tailPlot(fit)

## Try Tail Slider:
   # tailSlider(x)   

## Tail Risk:
   tailRisk(fit)
## Load Data:
   library(timeSeries)
   danish = as.timeSeries(data(danishClaims))

## Tail Plot:
   x = as.timeSeries(data(danishClaims))
   fit = gpdFit(x, u = 10)
   tailPlot(fit)

## Try Tail Slider:
   # tailSlider(x)   

## Tail Risk:
   tailRisk(fit)

Time Series Data Sets

Description

Data sets used in the examples of the fExtremes packages.

Usage

bmwRet
danishClaims
bmwRet
danishClaims

Format

bmwRet. A data frame with 6146 observations on 2 variables. The first column contains dates (Tuesday 2nd January 1973 until Tuesday 23rd July 1996) and the second column contains the respective value of daily log returns on the BMW share price made on each of those dates. These data are an irregular time series because there is no trading at weekends.

danishClaims. A data frame with 2167 observations on 2 variables. The first column contains dates and the second column contains the respective value of a fire insurance claim in Denmark made on each of those dates. These data are an irregular time series.

Examples

head(bmwRet)
head(danishClaims)
head(bmwRet)
head(danishClaims)

Value-at-Risk

Description

A collection and description of functions to compute Value-at-Risk and conditional Value-at-Risk

The functions are:

`VaR`	Computes Value-at-Risk,
`CVaR`	Computes conditional Value-at-Risk.

Usage

VaR(x, alpha = 0.05, type = "sample", tail = c("lower", "upper"))
CVaR(x, alpha = 0.05, type = "sample", tail = c("lower", "upper"))
VaR(x, alpha = 0.05, type = "sample", tail = c("lower", "upper"))
CVaR(x, alpha = 0.05, type = "sample", tail = c("lower", "upper"))

Arguments

`x`	an uni- or multivariate timeSeries object
`alpha`	a numeric value, the confidence interval.
`type`	a character string, the type to calculate the value-at-risk.
`tail`	a character string denoting which tail will be considered, either `"lower"` or `"upper"`. If `tail="lower"`, then alpha will be converted to `alpha=1-alpha`.

Value

VaR
CVaR

returns a numeric vector or value with the (conditional) value-at-risk for each time series column.

Author(s)

Diethelm Wuertz for this R-port.

Package 'fExtremes'

Help Index

Modelling Extreme Events in Finance

Description

Details

1 Introduction

2 Data and their Preprocessing

2 Explorative Data Analysis of Extremes

3 GPD Peak over Threshold Modeling

4 GEV Block Maxima Modeling

4 Value at Risk

5 Extreme Index

About Rmetrics

Extremes Data Preprocessing

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Extremal Index Estimation

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Explorative Data Analysis

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

Examples

Generalized Extreme Value Distribution

Description

Usage

Arguments

Value

Author(s)

References

Examples

Generalized Extreme Value Modelling

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

Examples

Generalized Extreme Value Modelling

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

Examples

Generalized Extreme Value Modelling

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

Examples

Generalized Pareto Distribution