Commit 203f757834018b083cbaac341f10d0bc4c43ad8a - fextremes

New upstream version 4021.83 Dirk Eddelbuettel 1 year, 8 months ago

23 changed file(s) with 679 addition(s) and 570 deletion(s). Raw diff Collapse all Expand all

+19

-11

DESCRIPTION less more

0	0	Package: fExtremes
1	1	Title: Rmetrics - Modelling Extreme Events in Finance
2		Date: 2017-11-12
3		Version: 3042.82
4		Author: Diethelm Wuertz [aut],
5		Tobias Setz [cre],
6		Yohan Chalabi [ctb]
7		Maintainer: Tobias Setz <tobias.setz@live.com>
	2	Date: 2022-08-06
	3	Version: 4021.83
	4	Authors@R: c(person("Diethelm", "Wuertz", role = "aut")
	5	, person("Tobias", "Setz", role = "aut")
	6	, person("Yohan", "Chalabi", role = "aut")
	7	, person(given = c("Paul", "J."), family = "Northrop",
	8	role = c("cre", "ctb"), email = "p.northrop@ucl.ac.uk") )
8	9	Description: Provides functions for analysing
9	10	and modelling extreme events in financial time Series. The
10	11	topics include: (i) data pre-processing, (ii) explorative
11	12	data analysis, (iii) peak over threshold modelling, (iv) block
12	13	maxima modelling, (v) estimation of VaR and CVaR, and (vi) the
13	14	computation of the extreme index.
14		Depends: R (>= 2.15.1), timeDate, timeSeries, fBasics, fGarch
15		Imports: methods, graphics, stats
	15	Depends: R (>= 2.15.1)
	16	Imports: fBasics, fGarch, graphics, methods, stats, timeDate,
	17	timeSeries
16	18	Suggests: RUnit, tcltk
17	19	LazyData: yes
18	20	License: GPL (>= 2)
19		URL: http://www.rmetrics.org
	21	URL: https://www.rmetrics.org
	22	BugReports: https://r-forge.r-project.org/projects/rmetrics
20	23	NeedsCompilation: no
21		Packaged: 2017-11-17 06:59:49 UTC; Tobias Setz
	24	Packaged: 2022-08-06 07:26:34 UTC; paul
	25	Author: Diethelm Wuertz [aut],
	26	Tobias Setz [aut],
	27	Yohan Chalabi [aut],
	28	Paul J. Northrop [cre, ctb]
	29	Maintainer: Paul J. Northrop <p.northrop@ucl.ac.uk>
22	30	Repository: CRAN
23		Date/Publication: 2017-11-17 08:38:27 UTC
	31	Date/Publication: 2022-08-06 14:10:02 UTC

+22

-20

MD5 less more

0	0	8f23146c12b8b6957d6b2ddbc2ee30c3 *ChangeLog
1		0ba152f26445dbe7cd65b4a2f60ad8da *DESCRIPTION
	1	86bf4b368df08fff7262189df2ca5f2e *DESCRIPTION
2	2	f4b3f22b776f1bfe7de2a986067df082 *NAMESPACE
3		b0496cc5388d91d3b6135eb66a1965d1 *R/DataPreprocessing.R
4		14d35601f9078cd7b60b3b0e83dbfa25 *R/ExtremeIndex.R
5		110122200f4fece7acd844e27248a8dc *R/ExtremesData.R
	3	64db85d4ec6f74f05e0f3e5226079fae *NEWS.md
	4	c75a4e9fbabd7ccd62050c4b931f0e41 *R/DataPreprocessing.R
	5	4288b214c0cfd8759f5fb92984e519cf *R/ExtremeIndex.R
	6	90b0b96c0c47d0ebaddda5de70e06ecb *R/ExtremesData.R
6	7	6a13bc0742419999232390871e92d301 *R/GevDistribution.R
7	8	c31e7ed7d662bc6510ce3767599d58d3 *R/GevFit.R
8	9	723138ddd14a272c6c6eb03cf4cca5de *R/GevMdaEstimation.R

12	13	512ce05b3576ca3abedf8ca2dd55b4f2 *R/GpdDistribution.R
13	14	7ec085102d4bf59dadf279a1ec57545e *R/GpdFit.R
14	15	c2de8f0daa283e46cded29f6752561b1 *R/GpdPlot.R
15		4bdc9bdc1763fc063c768e5f73772218 *R/GpdRisk.R
	16	99b148a01d755b9dc530fb6705cf884f *R/GpdRisk.R
16	17	1386d919c59b76dc9ec8ffab0eb3acc8 *R/GpdShow.R
17	18	b790b17afe8a9fc642263d9d226723c7 *R/GpdSim.R
18	19	11aba9bc6b7051e535c17b1629559dc1 *R/GpdSow.R

22	23	2717fc29227f4154e68b1baf36c2fe3c *R/meanExcessPlot.R
23	24	e3faa8aba12c0b0d6a1e64c39ff3095b *R/metrics.R
24	25	1e141bc51586b3c49fea4c3627dd4034 *R/zzz.R
	26	140447df49241762b6153cf9722077f8 *README.md
25	27	1ee0fad00bab85f51ec7945e1d5ce861 *data/bmwRet.csv
26	28	9666f10bfd745b92382bacc25f0a6e5f *data/danishClaims.csv
27	29	6042b9c5e5bec3ecc1b6959cd2858b64 *inst/COPYRIGHT.html
28	30	e278deadbe366f8e25fb878bf5c05c7c *inst/unitTests/Makefile
29	31	ef6862244177aa0c9690e037b2823bbd *inst/unitTests/runTests.R
30	32	f0e530000f96bc971e3fb89a25272c17 *inst/unitTests/runit.DataPreprocessing.R
31		3d8fa2217c7cdf803edc8cb3f531ad9c *inst/unitTests/runit.ExtremeIndex.R
32		5c4d0101ecf88db0530fd53f5c2f7b32 *inst/unitTests/runit.ExtremesData.R
	33	85029d526149a5366b3967f8d50b06e7 *inst/unitTests/runit.ExtremeIndex.R
	34	ccfde54505edd42248e3b8220fd00c27 *inst/unitTests/runit.ExtremesData.R
33	35	df54978979460d89018b2c7e8d9ed82e *inst/unitTests/runit.GevDistribution.R
34	36	aeed009908a914715e5d4947d8c4e098 *inst/unitTests/runit.GevMdaEstimation.R
35	37	2cb05c2f27012611715b74e9daf89ba0 *inst/unitTests/runit.GevModelling.R

37	39	9f0952a7794071690346f36d017c8c7f *inst/unitTests/runit.GpdDistribution.R
38	40	9ccbeecb2cd8580869caac1b8dae64c6 *inst/unitTests/runit.GpdModelling.R
39	41	69a26b861bd2d71074818864bd74029a *inst/unitTests/runit.GpdRisk.R
40		63e7f709080902a953c5eafd95f4ec8b *man/00Extremes-package.Rd
41		e6e5619fd05d40ead76567d3db8c6afc *man/DataPreprocessing.Rd
42		889ac9f50477b81bd1e5f26e54f08e47 *man/ExtremeIndex.Rd
43		053bd8de255d74a0c1dfd70121968a33 *man/ExtremesData.Rd
44		92957faa1992251453a248bca84052bc *man/GevDistribution.Rd
45		45054a04d74c5dc6bb8fe4d063716779 *man/GevMdaEstimation.Rd
46		2cb16bcd6748e1b232bde82f6dc7ae0a *man/GevModelling.Rd
47		ba2f4fb3503eca13e674108cfe8634f3 *man/GevRisk.Rd
	42	4dee6aa74016e1e9cb6b79e9388fd53a *man/00Extremes-package.Rd
	43	a345e9b2187113736f912e452d1cc036 *man/DataPreprocessing.Rd
	44	6c9628e192a19805b0803b6719c37cd5 *man/ExtremeIndex.Rd
	45	2a9d03b7c9439c4d79bb6ec87344f279 *man/ExtremesData.Rd
	46	30f9c2985227e9b102f244885d2257f3 *man/GevDistribution.Rd
	47	ea3e15acda0d721fb5cfd6281f67d6c2 *man/GevMdaEstimation.Rd
	48	3ee334becfa1b4fde8dbf240c2855a80 *man/GevModelling.Rd
	49	99f29b46f5fbc2e854ba236eb570ca43 *man/GevRisk.Rd
48	50	bf840a931840784b022298a7f530bc4c *man/GpdDistribution.Rd
49		ece6d6aaabd8a777f0ccce399af7c7aa *man/GpdModelling.Rd
50		1828c67556c2c7ebf111064d91126782 *man/GpdRisk.Rd
51		79605e94b04f8ac2e743bd30d4d44e58 *man/ValueAtRisk.Rd
52		246ce117b00d8707bef1634cb9768a5a *man/data.Rd
53		ca566e590ec30abd0718c5375e1a446f *tests/doRUnit.R
	51	267a78a6f044014c4f9c3d9ae8944fc5 *man/GpdModelling.Rd
	52	13a7bca308c9370bbdc6e7e51cf046db *man/GpdRisk.Rd
	53	e7cc9d24a47e39163a2d9b04fdefa147 *man/ValueAtRisk.Rd
	54	c2fab3230bb9e5352ff5b284547ebb97 *man/data.Rd
	55	ab7ff992a1a19f94a9f6d2a69de573ff *tests/doRUnit.R

+10

-0

NEWS.md less more

	0	# fExtremes 4021.83
	1
	2	* New maintainer: Paul Northrop
	3	* Updated DESCRIPTION with links and moved timeDate, timeSeries, fBasics and fGarch from Depends: to Imports:.
	4	* Corrected some minor typos.
	5	* Added documentation in `TimeSeriesData` to describe the `bmwRet` and `danishClaims` datasets.
	6
	7	# fExtremes 3042.82 and older versions
	8
	9	See file ChangeLog on [the CRAN fExtremes page](https://CRAN.R-project.org/package=fExtremes) for changes before 4021.83.

-2

R/DataPreprocessing.R less more

50	50	# FUNCTION:
51	51
52	52	# Check Type:
53		if (class(x) == "timeSeries") {
	53	if (inherits(x, "timeSeries")) {
54	54	stopifnot(isUnivariate(x))
55	55	} else {
56	56	x = as.vector(x)

135	135	# FUNCTION:
136	136
137	137	# Check Type:
138		if (class(x) == "timeSeries") {
	138	if (inherits(x, "timeSeries")) {
139	139	stopifnot(isUnivariate(x))
140	140	} else {
141	141	x = as.vector(x)

-6

R/ExtremeIndex.R less more

23	23	# blockTheta Computes theta from Block Method
24	24	# clusterTheta Computes theta from Reciprocal Cluster Method
25	25	# runTheta Computes theta from Run Method
26		# ferrosegersTheta Computes Theta according to Ferro and Seegers
	26	# ferrosegersTheta Computes Theta according to Ferro and Segers
27	27	# FUNCTION: DESCRIPTION:
28	28	# exindexesPlot Computes and Plot Theta(1,2,3)
29	29	# exindexPlot Computes Theta(1,2) and Plot Theta(1)

156	156	keepK = keepN = NULL
157	157	for ( u in thresholds ) {
158	158	run = run + 1
159		# N # of exceedences \| K # of blocks with exceedences:
	159	# N # of exceedances \| K # of blocks with exceedances:
160	160	N = length(X[X > u])
161	161	K = floor(sum(sign(apply(X, 1, max) - u) + 1) / 2)
162	162	if (K/k < 1) {

228	228	keepK = keepN = NULL
229	229	for ( u in thresholds ) {
230	230	run = run + 1
231		# N # of exceedences \| K # of blocks with exceedences:
	231	# N # of exceedances \| K # of blocks with exceedances:
232	232	N = length(X[X > u])
233	233	K = floor(sum(sign(apply(X, 1, max) - u) + 1) / 2)
234	234	theta2[run] = K/N

298	298	keepN = NULL
299	299	for ( u in thresholds ) {
300	300	run = run + 1
301		# N # of exceedences \| K # of blocks with exceedences:
	301	# N # of exceedances \| K # of blocks with exceedances:
302	302	N = length(X[X > u])
303	303	Y = diff(Count[X > u])
304	304	Y = Y[Y > block]

471	471	run = 0
472	472	for ( z in z0 ) {
473	473	run = run + 1
474		# N - number of exceedences:
	474	# N - number of exceedances:
475	475	N = length(resid1[resid1 > z])
476		# K - number of blocks with exceedences:
	476	# K - number of blocks with exceedances:
477	477	# DW: floor()
478	478	K = floor(sum(sign(apply(resid1, 1, max)-z)+1) / 2)
479	479	if (K/k < 1) {

-2

R/ExtremesData.R less more

26	26	# msratioPlot Plots ratio of maximums and sums
27	27	# sllnPlot Verifies Kolmogorov's Strong Law of large numbers
28	28	# lilPlot Verifies Hartman-Wintner's Law of the iterated logarithm
29		# xacfPlot Plots autocorrelations of exceedences
	29	# xacfPlot Plots autocorrelations of exceedances
30	30	################################################################################
31	31
32	32

720	720	{ # A function implemented by Diethelm Wuertz
721	721
722	722	# Description:
723		# Creates plots of exceedences, one for the
	723	# Creates plots of exceedances, one for the
724	724	# heights and one for the distances.
725	725
726	726	# Arguments:

-2

R/GpdRisk.R less more

166	166	# if (q < x$plotmax) abline(v = q, lty = 2)
167	167	out = as.numeric(q)
168	168	if (ci.type == "wald") {
169		if (class(x$lastfit) != "gpd")
	169	if (!inherits(x$lastfit, "gpd"))
170	170	stop("Wald method requires model be fitted with gpd (not pot)")
171	171	scaling = threshold
172	172	betahat = betahat/scaling

861	861	abline(v = u, lty = 3, col = "red")
862	862	abline(v = U, lty = 3, col = "red")
863	863
864		# Fit line to mean excess within thresolds:
	864	# Fit line to mean excess within thresholds:
865	865	X = as.vector(ans[, 1])
866	866	Y = as.vector(ans[, 2])
867	867	Y = Y[X > u & X < U]

+63

-0

README.md less more

	0
	1	<!-- README.md is generated from README.Rmd. Please edit that file -->
	2
	3	# fExtremes
	4
	5	[![AppVeyor Build
	6	Status](https://ci.appveyor.com/api/projects/status/github/paulnorthrop/fExtremes?branch=main&svg=true)](https://ci.appveyor.com/project/paulnorthrop/fExtremes)
	7	[![R-CMD-check](https://github.com/paulnorthrop/fExtremes/workflows/R-CMD-check/badge.svg)](https://github.com/paulnorthrop/fExtremes/actions)
	8	[![Coverage
	9	Status](https://codecov.io/github/paulnorthrop/fExtremes/coverage.svg?branch=main)](https://codecov.io/github/paulnorthrop/fExtremes?branch=main)
	10	[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/fExtremes)](https://cran.r-project.org/package=fExtremes)
	11	[![Downloads
	12	(monthly)](https://cranlogs.r-pkg.org/badges/fExtremes?color=brightgreen)](https://cran.r-project.org/package=fExtremes)
	13	[![Downloads
	14	(total)](https://cranlogs.r-pkg.org/badges/grand-total/fExtremes?color=brightgreen)](https://cran.r-project.org/package=fExtremes)
	15
	16	## Rmetrics - Modelling Extreme Events in Finance
	17
	18	The fExtremes package provides functions for analysing and modelling
	19	extreme events in financial time Series. The topics include: (i) data
	20	pre-processing, (ii) explorative data analysis, (iii) peak over
	21	threshold modelling, (iv) block maxima modelling, (v) estimation of VaR
	22	and CVaR, and (vi) the computation of the extreme index. It is part of
	23	the [Rmetrics software project](https://www.rmetrics.org/).
	24
	25	### An example
	26
	27	The following code simulates data from a GEV distribution, fits a GEV
	28	distribution to these data and creates model diagnostic plots.
	29
	30	``` r
	31	library(fExtremes)
	32	# Simulate GEV Data, use default length n=1000
	33	x <- gevSim(model = list(xi = 0.25, mu = 0 , beta = 1), n = 1000)
	34
	35	# Fit GEV data using maximum likelihood estimation
	36	fit <- gevFit(x, type = "mle")
	37	fit
	38	#>
	39	#> Title:
	40	#> GEV Parameter Estimation
	41	#>
	42	#> Call:
	43	#> gevFit(x = x, type = "mle")
	44	#>
	45	#> Estimation Type:
	46	#> gev mle
	47	#>
	48	#> Estimated Parameters:
	49	#> xi mu beta
	50	#> 0.18304217 0.04548892 0.99014748
	51	#>
	52	#> Description
	53	#> Fri Aug 5 17:30:44 2022
	54	```
	55
	56	### Installation
	57
	58	To get the current released version from CRAN:
	59
	60	``` r
	61	install.packages("fExtremes")
	62	```

-1

inst/unitTests/runit.ExtremeIndex.R less more

35	35	# blockTheta Computes theta from Block Method
36	36	# clusterTheta Computes theta from Reciprocal Cluster Method
37	37	# runTheta Computes theta from Run Method
38		# ferrosegersTheta Computes Theta according to Ferro and Seegers
	38	# ferrosegersTheta Computes Theta according to Ferro and Segers
39	39	# FUNCTION: DESCRIPTION:
40	40	# exindexesPlot Computes and Plot Theta(1,2,3)
41	41	# exindexPlot Computes Theta(1,2) and Plot Theta(1)

-2

inst/unitTests/runit.ExtremesData.R less more

38	38	# msratioPlot Plots ratio of maximums and sums
39	39	# sllnPlot Verifies Kolmogorov's Strong Law of large numbers
40	40	# lilPlot Verifies Hartman-Wintner's Law of the iterated logarithm
41		# xacfPlot Plots autocorrelations of exceedences
	41	# xacfPlot Plots autocorrelations of exceedances
42	42	################################################################################
43	43
44	44

260	260	test.xacf =
261	261	function()
262	262	{
263		# xacfPlot - Plots autocorrelations of exceedences
	263	# xacfPlot - Plots autocorrelations of exceedances
264	264
265	265	# Create an Artificial Data Set:
266	266	RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")

+296

-296

man/00Extremes-package.Rd less more

0		\name{fExtremes-package}
1
2
3		\alias{fExtremes-package}
4		\alias{fExtremes}
5
6
7		\docType{package}
8
9
10		\title{Modelling Extreme Events in Finance}
11
12
13		\description{
14
15		The Rmetrics "fExtemes" package is a collection of functions to
16		analyze and model extreme events in Finance and Insurance.
17
18		}
19
20
21		\details{
22
23		\preformatted{
24		Package: \tab fExtremes\cr
25		Type: \tab Package\cr
26		%Version: \tab R 3.0.1\cr
27		%Date: \tab 2014\cr
28		License: \tab GPL Version 2 or later\cr
29		Copyright: \tab (c) 1999-2014 Rmetrics Assiciation\cr
30		URL: \tab \url{https://www.rmetrics.org}
31		}
32
33		}
34
35
36		\section{1 Introduction}{
37
38		The \code{fExtremes} package provides functions for analyzing
39		and modeling extreme events in financial time Series. The
40		topics include: (i) data proeprocessing, (ii) explorative
41		data analysis, (iii) peak over threshold modeling, (iv) block
42		maxima modeling, (v) estimation of VaR and CVaR, and (vi) the
43		computation of the extreme index.
44
45		}
46
47
48		\section{2 Data and their Preprocessing}{
49
50		\emph{Data Sets:}
51
52		Data sets used in the examples of the timeSeries packages.
53
54		\emph{Data Preprocessing:}
55
56		These are tools for data preprocessing, including functions to
57		separate data beyond a threshold value, to compute blockwise data
58		like block maxima, and to decluster point process data.
59
60		\preformatted{
61		blockMaxima extracts block maxima from a vector or a time series
62		findThreshold finds upper threshold for a given number of extremes
63		pointProcess extracts peaks over Threshold from a vector or a time series
64		deCluster de-clusters clustered point process data
65		}
66
67		}
68
69
70		\section{2 Explorative Data Analysis of Extremes}{
71
72		This section contains a collection of functions for explorative data
73		analysis of extreme values in financial time series. The tools include
74		plot functions for emprical distributions, quantile plots, graphs
75		exploring the properties of exceedences over a threshold, plots for
76		mean/sum ratio and for the development of records. The functions are:
77
78		\preformatted{
79		emdPlot plots of empirical distribution function
80		qqparetoPlot exponential/Pareto quantile plot
81		mePlot plot of mean excesses over a threshold
82		mrlPlot another variant, mean residual life plot
83		mxfPlot another variant, with confidence intervals
84		msratioPlot plot of the ratio of maximum and sum
85		}
86
87		\preformatted{
88		recordsPlot Record development compared with iid data
89		ssrecordsPlot another variant, investigates subsamples
90		sllnPlot verifies Kolmogorov's strong law of large numbers
91		lilPlot verifies Hartman-Wintner's law of the iterated logarithm
92		}
93
94		\preformatted{
95		xacfPlot plots ACF of exceedences over a threshold
96		}
97
98
99		\emph{Parameter Fitting of Mean Excesses:}
100
101		\preformatted{
102		normMeanExcessFit fits mean excesses with a normal density
103		ghMeanExcessFit fits mean excesses with a GH density
104		hypMeanExcessFit fits mean excesses with a HYP density
105		nigMeanExcessFit fits mean excesses with a NIG density
106		ghtMeanExcessFit fits mean excesses with a GHT density
107		}
108
109		}
110
111
112		\section{3 GPD Peak over Threshold Modeling}{
113
114		\emph{GPD Distribution:}
115
116		A collection of functions to compute the generalized Pareto
117		distribution. The functions compute density, distribution
118		function, quantile function and generate random deviates
119		for the GPD. In addition functions to compute the true
120		moments and to display the distribution and random variates
121		changing parameters interactively are available.
122
123		\preformatted{
124		dgpd returns the density of the GPD distribution
125		pgpd returns the probability function of the GPD
126		qgpd returns quantile function of the GPD distribution
127		rgpd generates random variates from the GPD distribution
128		gpdSlider displays density or rvs from a GPD
129		}
130
131
132		\emph{GPD Moments:}
133
134		\preformatted{
135		gpdMoments computes true mean and variance of GDP
136		}
137
138
139		\emph{GPD Parameter Estimation:}
140
141		This section contains functions to fit and to simulate processes that
142		are generated from the generalized Pareto distribution. Two
143		approaches for parameter estimation are provided: Maximum likelihood
144		estimation and the probability weighted moment method.
145
146		\preformatted{
147		gpdSim generates data from the GPD distribution
148		gpdFit fits data to the GPD istribution
149		}
150
151
152		\emph{GPD print, plot and summary methods:}
153
154		\preformatted{
155		print print method for a fitted GPD object
156		plot plot method for a fitted GPD object
157		summary summary method for a fitted GPD object
158		}
159
160
161		\emph{GDP Tail Risk:}
162
163		The following functions compute tail risk under the GPD approach.
164
165		\preformatted{
166		gpdQPlot estimation of high quantiles
167		gpdQuantPlot variation of high quantiles with threshold
168		gpdRiskMeasures prescribed quantiles and expected shortfalls
169		gpdSfallPlot expected shortfall with confidence intervals
170		gpdShapePlot variation of GPD shape with threshold
171		gpdTailPlot plot of the GPD tail
172		}
173
174		% \preformatted{
175		% tailPlot
176		% tailSlider
177		% tailRisk
178		% }
179
180		}
181
182
183		\section{4 GEV Block Maxima Modeling}{
184
185		\emph{GEV Distribution:}
186
187		This section contains functions to fit and to simulate processes
188		that are generated from the generalized extreme value distribution
189		including the Frechet, Gumbel, and Weibull distributions.
190
191		\preformatted{
192		dgev returns density of the GEV distribution
193		pgev returns probability function of the GEV
194		qgev returns quantile function of the GEV distribution
195		rgev generates random variates from the GEV distribution
196		gevSlider displays density or rvs from a GEV
197		}
198
199
200		\emph{GEV Moments:}
201
202		\preformatted{
203		gevMoments computes true mean and variance
204		}
205
206
207		\emph{GEV Parameter Estimation:}
208
209
210		A collection to simulate and to estimate the parameters of
211		processes generated from GEV distribution.
212
213		\preformatted{
214		gevSim generates data from the GEV distribution
215		gumbelSim generates data from the Gumbel distribution
216		gevFit fits data to the GEV distribution
217		gumbelFit fits data to the Gumbel distribution
218		}
219
220		\preformatted{
221		print print method for a fitted GEV object
222		plot plot method for a fitted GEV object
223		summary summary method for a fitted GEV object
224		}
225
226
227		\emph{GEV MDA Estimation:}
228
229		Here we provide Maximum Domain of Attraction estimators and visualize
230		the results by a Hill plot and a common shape parameter plot from
231		the Pickands, Einmal-Decker-deHaan, and Hill estimators.
232
233		\preformatted{
234		hillPlot shape parameter and Hill estimate of the tail index
235		shaparmPlot variation of shape parameter with tail depth
236		}
237
238
239		\emph{GEV Risk Estimation:}
240
241		\preformatted{
242		gevrlevelPlot k-block return level with confidence intervals
243		}
244
245		}
246
247
248		\section{4 Value at Risk}{
249
250		Two functions to compute Value-at-Risk and conditional
251		Value-at-Risk.
252
253		\preformatted{
254		VaR computes Value-at-Risk
255		CVaR computes conditional Value-at-Risk
256		}
257
258		}
259
260
261		\section{5 Extreme Index}{
262
263		A collection of functions to simulate time series with a known extremal
264		index, and to estimate the extremal index by four different kind of
265		methods, the blocks method, the reciprocal mean cluster size method,
266		the runs method, and the method of Ferro and Segers.
267
268		\preformatted{
269		thetaSim simulates a time Series with known theta
270		blockTheta computes theta from Block Method
271		clusterTheta computes theta from Reciprocal Cluster Method
272		runTheta computes theta from Run Method
273		ferrosegersTheta computes theta according to Ferro and Seegers
274		}
275
276		\preformatted{
277		exindexPlot calculatess and plots Theta(1,2,3)
278		exindexesPlot calculates Theta(1,2) and plots Theta(1)
279		}
280
281		}
282
283
284		\section{About Rmetrics}{
285
286		The \code{fExtremes} Rmetrics package is written for educational
287		support in teaching "Computational Finance and Financial Engineering"
288		and licensed under the GPL.
289
290		}
291
292
293		\keyword{package}
294
295
	0	\name{fExtremes-package}
	1
	2
	3	\alias{fExtremes-package}
	4	\alias{fExtremes}
	5
	6
	7	\docType{package}
	8
	9
	10	\title{Modelling Extreme Events in Finance}
	11
	12
	13	\description{
	14
	15	The Rmetrics "fExtremes" package is a collection of functions to
	16	analyze and model extreme events in Finance and Insurance.
	17
	18	}
	19
	20
	21	\details{
	22
	23	\preformatted{
	24	Package: \tab fExtremes\cr
	25	Type: \tab Package\cr
	26	%Version: \tab R 3.0.1\cr
	27	%Date: \tab 2014\cr
	28	License: \tab GPL Version 2 or later\cr
	29	Copyright: \tab (c) 1999-2014 Rmetrics Assiciation\cr
	30	URL: \tab \url{https://www.rmetrics.org}
	31	}
	32
	33	}
	34
	35
	36	\section{1 Introduction}{
	37
	38	The \code{fExtremes} package provides functions for analyzing
	39	and modeling extreme events in financial time Series. The
	40	topics include: (i) data pre-processing, (ii) explorative
	41	data analysis, (iii) peak over threshold modeling, (iv) block
	42	maxima modeling, (v) estimation of VaR and CVaR, and (vi) the
	43	computation of the extreme index.
	44
	45	}
	46
	47
	48	\section{2 Data and their Preprocessing}{
	49
	50	\emph{Data Sets:}
	51
	52	Data sets used in the examples of the timeSeries packages.
	53
	54	\emph{Data Preprocessing:}
	55
	56	These are tools for data preprocessing, including functions to
	57	separate data beyond a threshold value, to compute blockwise data
	58	like block maxima, and to decluster point process data.
	59
	60	\preformatted{
	61	blockMaxima extracts block maxima from a vector or a time series
	62	findThreshold finds upper threshold for a given number of extremes
	63	pointProcess extracts peaks over Threshold from a vector or a time series
	64	deCluster de-clusters clustered point process data
	65	}
	66
	67	}
	68
	69
	70	\section{2 Explorative Data Analysis of Extremes}{
	71
	72	This section contains a collection of functions for explorative data
	73	analysis of extreme values in financial time series. The tools include
	74	plot functions for emprical distributions, quantile plots, graphs
	75	exploring the properties of exceedances over a threshold, plots for
	76	mean/sum ratio and for the development of records. The functions are:
	77
	78	\preformatted{
	79	emdPlot plots of empirical distribution function
	80	qqparetoPlot exponential/Pareto quantile plot
	81	mePlot plot of mean excesses over a threshold
	82	mrlPlot another variant, mean residual life plot
	83	mxfPlot another variant, with confidence intervals
	84	msratioPlot plot of the ratio of maximum and sum
	85	}
	86
	87	\preformatted{
	88	recordsPlot Record development compared with iid data
	89	ssrecordsPlot another variant, investigates subsamples
	90	sllnPlot verifies Kolmogorov's strong law of large numbers
	91	lilPlot verifies Hartman-Wintner's law of the iterated logarithm
	92	}
	93
	94	\preformatted{
	95	xacfPlot plots ACF of exceedances over a threshold
	96	}
	97
	98
	99	\emph{Parameter Fitting of Mean Excesses:}
	100
	101	\preformatted{
	102	normMeanExcessFit fits mean excesses with a normal density
	103	ghMeanExcessFit fits mean excesses with a GH density
	104	hypMeanExcessFit fits mean excesses with a HYP density
	105	nigMeanExcessFit fits mean excesses with a NIG density
	106	ghtMeanExcessFit fits mean excesses with a GHT density
	107	}
	108
	109	}
	110
	111
	112	\section{3 GPD Peak over Threshold Modeling}{
	113
	114	\emph{GPD Distribution:}
	115
	116	A collection of functions to compute the generalized Pareto
	117	distribution. The functions compute density, distribution
	118	function, quantile function and generate random deviates
	119	for the GPD. In addition functions to compute the true
	120	moments and to display the distribution and random variates
	121	changing parameters interactively are available.
	122
	123	\preformatted{
	124	dgpd returns the density of the GPD distribution
	125	pgpd returns the probability function of the GPD
	126	qgpd returns quantile function of the GPD distribution
	127	rgpd generates random variates from the GPD distribution
	128	gpdSlider displays density or rvs from a GPD
	129	}
	130
	131
	132	\emph{GPD Moments:}
	133
	134	\preformatted{
	135	gpdMoments computes true mean and variance of GDP
	136	}
	137
	138
	139	\emph{GPD Parameter Estimation:}
	140
	141	This section contains functions to fit and to simulate processes that
	142	are generated from the generalized Pareto distribution. Two
	143	approaches for parameter estimation are provided: Maximum likelihood
	144	estimation and the probability weighted moment method.
	145
	146	\preformatted{
	147	gpdSim generates data from the GPD distribution
	148	gpdFit fits data to the GPD istribution
	149	}
	150
	151
	152	\emph{GPD print, plot and summary methods:}
	153
	154	\preformatted{
	155	print print method for a fitted GPD object
	156	plot plot method for a fitted GPD object
	157	summary summary method for a fitted GPD object
	158	}
	159
	160
	161	\emph{GDP Tail Risk:}
	162
	163	The following functions compute tail risk under the GPD approach.
	164
	165	\preformatted{
	166	gpdQPlot estimation of high quantiles
	167	gpdQuantPlot variation of high quantiles with threshold
	168	gpdRiskMeasures prescribed quantiles and expected shortfalls
	169	gpdSfallPlot expected shortfall with confidence intervals
	170	gpdShapePlot variation of GPD shape with threshold
	171	gpdTailPlot plot of the GPD tail
	172	}
	173
	174	% \preformatted{
	175	% tailPlot
	176	% tailSlider
	177	% tailRisk
	178	% }
	179
	180	}
	181
	182
	183	\section{4 GEV Block Maxima Modeling}{
	184
	185	\emph{GEV Distribution:}
	186
	187	This section contains functions to fit and to simulate processes
	188	that are generated from the generalized extreme value distribution
	189	including the Frechet, Gumbel, and Weibull distributions.
	190
	191	\preformatted{
	192	dgev returns density of the GEV distribution
	193	pgev returns probability function of the GEV
	194	qgev returns quantile function of the GEV distribution
	195	rgev generates random variates from the GEV distribution
	196	gevSlider displays density or rvs from a GEV
	197	}
	198
	199
	200	\emph{GEV Moments:}
	201
	202	\preformatted{
	203	gevMoments computes true mean and variance
	204	}
	205
	206
	207	\emph{GEV Parameter Estimation:}
	208
	209
	210	A collection to simulate and to estimate the parameters of
	211	processes generated from GEV distribution.
	212
	213	\preformatted{
	214	gevSim generates data from the GEV distribution
	215	gumbelSim generates data from the Gumbel distribution
	216	gevFit fits data to the GEV distribution
	217	gumbelFit fits data to the Gumbel distribution
	218	}
	219
	220	\preformatted{
	221	print print method for a fitted GEV object
	222	plot plot method for a fitted GEV object
	223	summary summary method for a fitted GEV object
	224	}
	225
	226
	227	\emph{GEV MDA Estimation:}
	228
	229	Here we provide Maximum Domain of Attraction estimators and visualize
	230	the results by a Hill plot and a common shape parameter plot from
	231	the Pickands, Einmal-Decker-deHaan, and Hill estimators.
	232
	233	\preformatted{
	234	hillPlot shape parameter and Hill estimate of the tail index
	235	shaparmPlot variation of shape parameter with tail depth
	236	}
	237
	238
	239	\emph{GEV Risk Estimation:}
	240
	241	\preformatted{
	242	gevrlevelPlot k-block return level with confidence intervals
	243	}
	244
	245	}
	246
	247
	248	\section{4 Value at Risk}{
	249
	250	Two functions to compute Value-at-Risk and conditional
	251	Value-at-Risk.
	252
	253	\preformatted{
	254	VaR computes Value-at-Risk
	255	CVaR computes conditional Value-at-Risk
	256	}
	257
	258	}
	259
	260
	261	\section{5 Extreme Index}{
	262
	263	A collection of functions to simulate time series with a known extremal
	264	index, and to estimate the extremal index by four different kind of
	265	methods, the blocks method, the reciprocal mean cluster size method,
	266	the runs method, and the method of Ferro and Segers.
	267
	268	\preformatted{
	269	thetaSim simulates a time Series with known theta
	270	blockTheta computes theta from Block Method
	271	clusterTheta computes theta from Reciprocal Cluster Method
	272	runTheta computes theta from Run Method
	273	ferrosegersTheta computes theta according to Ferro and Segers
	274	}
	275
	276	\preformatted{
	277	exindexPlot calculatess and plots Theta(1,2,3)
	278	exindexesPlot calculates Theta(1,2) and plots Theta(1)
	279	}
	280
	281	}
	282
	283
	284	\section{About Rmetrics}{
	285
	286	The \code{fExtremes} Rmetrics package is written for educational
	287	support in teaching "Computational Finance and Financial Engineering"
	288	and licensed under the GPL.
	289
	290	}
	291
	292
	293	\keyword{package}
	294
	295

+201

-200

man/DataPreprocessing.Rd less more

0		\name{DataPreprocessing}
1
2		\alias{DataPreprocessing}
3
4		\alias{blockMaxima}
5		\alias{findThreshold}
6		\alias{pointProcess}
7		\alias{deCluster}
8
9
10		\title{Extremes Data Preprocessing}
11
12
13		\description{
14
15		A collection and description of functions for data
16		preprocessing of extreme values. This includes tools
17		to separate data beyond a threshold value, to compute
18		blockwise data like block maxima, and to decluster
19		point process data.
20		\cr
21
22		The functions are:
23
24		\tabular{ll}{
25		\code{blockMaxima} \tab Block Maxima from a vector or a time series, \cr
26		\code{findThreshold} \tab Upper threshold for a given number of extremes, \cr
27		\code{pointProcess} \tab Peaks over Threshold from a vector or a time series, \cr
28		\code{deCluster} \tab Declusters clustered point process data. }
29
30		}
31
32
33		\usage{
34		blockMaxima(x, block = c("monthly", "quarterly"), doplot = FALSE)
35		findThreshold(x, n = floor(0.05*length(as.vector(x))), doplot = FALSE)
36		pointProcess(x, u = quantile(x, 0.95), doplot = FALSE)
37		deCluster(x, run = 20, doplot = TRUE)
38		}
39
40
41		\arguments{
42
43		\item{block}{
44		the block size. A numeric value is interpreted as the number
45		of data values in each successive block. All the data is used,
46		so the last block may not contain \code{block} observations.
47		If the \code{data} has a \code{times} attribute containing (in
48		an object of class \code{"POSIXct"}, or an object that can be
49		converted to that class, see \code{\link{as.POSIXct}}) the
50		times/dates of each observation, then \code{block} may instead
51		take the character values \code{"month"}, \code{"quarter"},
52		\code{"semester"} or \code{"year"}. By default monthly blocks
53		from daily data are assumed.
54		}
55		\item{doplot}{
56		a logical value. Should the results be plotted? By
57		default \code{TRUE}.
58		}
59		\item{n}{
60		a numeric value or vector giving number of extremes above
61		the threshold. By default, \code{n} is
62		set to an integer representing 5\% of the data from the
63		whole data set \code{x}.
64		}
65		\item{run}{
66		parameter to be used in the runs method; any two consecutive
67		threshold exceedances separated by more than this number of
68		observations/days are considered to belong to different clusters.
69		}
70		\item{u}{
71		a numeric value at which level the data are to be truncated. By
72		default the threshold value which belongs to the 95\% quantile,
73		\code{u=quantile(x,0.95)}.
74		}
75		\item{x}{
76		a numeric data vector from which \code{findThreshold} and
77		\code{blockMaxima} determine the threshold values and block
78		maxima values.
79		For the function \code{deCluster} the argument
80		\code{x} represents a numeric vector of threshold exceedances
81		with a \code{times} attribute which should be a numeric
82		vector containing either the indices or the times/dates
83		of each exceedance (if times/dates, the attribute should
84		be an object of class \code{"POSIXct"} or an object that
85		can be converted to that class; see \code{\link{as.POSIXct}}).
86		}
87
88		}
89
90
91		\details{
92
93		\bold{Computing Block Maxima:}
94		\cr\cr
95		The function \code{blockMaxima} calculates block maxima from a vector
96		or a time series, whereas the function
97		\code{blocks} is more general and allows for the calculation of
98		an arbitrary function \code{FUN} on blocks.
99		\cr
100
101
102		\bold{Finding Thresholds:}
103		\cr\cr
104		The function \code{findThreshold} finds a threshold so that a given
105		number of extremes lie above. When the data are tied a threshold is
106		found so that at least the specified number of extremes lie above.
107		\cr
108
109
110		\bold{De-Clustering Point Processes:}
111		\cr\cr
112		The function \code{deCluster} declusters clustered point process
113		data so that Poisson assumption is more tenable over a high threshold.
114
115		}
116
117
118		\value{
119
120		\code{blockMaxima}
121		\cr
122		returns a timeSeries object or a numeric vector of block
123		maxima data.
124
125		\code{findThreshold}
126		\cr
127		returns a numeric value or vector of suitable thresholds.
128
129		\code{pointProcess}
130		\cr
131		returns a timeSeries object or a numeric vector of peaks over
132		a threshold.
133
134		\code{deCluster}
135		\cr
136		returns a timeSeries object or a numeric vector for the
137		declustered point process.
138
139		}
140
141
142		\references{
143
144		Coles S. (2001);
145		\emph{Introduction to Statistical Modelling of Extreme Values},
146		Springer.
147
148		Embrechts, P., Klueppelberg, C., Mikosch, T. (1997);
149		\emph{Modelling Extremal Events}, Springer.
150
151		}
152
153
154		\author{
155
156		Some of the functions were implemented from Alec Stephenson's
157		R-package \code{evir} ported from Alexander McNeil's S library
158		\code{EVIS}, \emph{Extreme Values in S}, some from Alec Stephenson's
159		R-package \code{ismev} based on Stuart Coles code from his book,
160		\emph{Introduction to Statistical Modeling of Extreme Values} and
161		some were written by Diethelm Wuertz.
162
163		}
164
165		\examples{
166		## findThreshold -
167		# Threshold giving (at least) fifty exceedances for Danish data:
168		x <- as.timeSeries(data(danishClaims))
169		findThreshold(x, n = c(10, 50, 100))
170
171		## blockMaxima -
172		# Block Maxima (Minima) for left tail of BMW log returns:
173		BMW <- as.timeSeries(data(bmwRet))
174		colnames(BMW) <- "BMW.RET"
175		head(BMW)
176		x <- blockMaxima( BMW, block = 65)
177		head(x)
178		\dontrun{
179		y <- blockMaxima(-BMW, block = 65)
180		head(y)
181		y <- blockMaxima(-BMW, block = "monthly")
182		head(y)}
183
184		## pointProcess -
185		# Return Values above threshold in negative BMW log-return data:
186		PP = pointProcess(x = -BMW, u = quantile(as.vector(x), 0.75))
187		PP
188		nrow(PP)
189
190		## deCluster -
191		# Decluster the 200 exceedances of a particular
192		DC = deCluster(x = PP, run = 15, doplot = TRUE)
193		DC
194		nrow(DC)
195		}
196
197
198		\keyword{programming}
199
	0	\name{DataPreprocessing}
	1
	2	\alias{DataPreprocessing}
	3
	4	\alias{blockMaxima}
	5	\alias{findThreshold}
	6	\alias{pointProcess}
	7	\alias{deCluster}
	8
	9
	10	\title{Extremes Data Preprocessing}
	11
	12
	13	\description{
	14
	15	A collection and description of functions for data
	16	preprocessing of extreme values. This includes tools
	17	to separate data beyond a threshold value, to compute
	18	blockwise data like block maxima, and to decluster
	19	point process data.
	20	\cr
	21
	22	The functions are:
	23
	24	\tabular{ll}{
	25	\code{blockMaxima} \tab Block Maxima from a vector or a time series, \cr
	26	\code{findThreshold} \tab Upper threshold for a given number of extremes, \cr
	27	\code{pointProcess} \tab Peaks over Threshold from a vector or a time series, \cr
	28	\code{deCluster} \tab Declusters clustered point process data. }
	29
	30	}
	31
	32
	33	\usage{
	34	blockMaxima(x, block = c("monthly", "quarterly"), doplot = FALSE)
	35	findThreshold(x, n = floor(0.05*length(as.vector(x))), doplot = FALSE)
	36	pointProcess(x, u = quantile(x, 0.95), doplot = FALSE)
	37	deCluster(x, run = 20, doplot = TRUE)
	38	}
	39
	40
	41	\arguments{
	42
	43	\item{block}{
	44	the block size. A numeric value is interpreted as the number
	45	of data values in each successive block. All the data is used,
	46	so the last block may not contain \code{block} observations.
	47	If the \code{data} has a \code{times} attribute containing (in
	48	an object of class \code{"POSIXct"}, or an object that can be
	49	converted to that class, see \code{\link{as.POSIXct}}) the
	50	times/dates of each observation, then \code{block} may instead
	51	take the character values \code{"month"}, \code{"quarter"},
	52	\code{"semester"} or \code{"year"}. By default monthly blocks
	53	from daily data are assumed.
	54	}
	55	\item{doplot}{
	56	a logical value. Should the results be plotted? By
	57	default \code{TRUE}.
	58	}
	59	\item{n}{
	60	a numeric value or vector giving number of extremes above
	61	the threshold. By default, \code{n} is
	62	set to an integer representing 5\% of the data from the
	63	whole data set \code{x}.
	64	}
	65	\item{run}{
	66	parameter to be used in the runs method; any two consecutive
	67	threshold exceedances separated by more than this number of
	68	observations/days are considered to belong to different clusters.
	69	}
	70	\item{u}{
	71	a numeric value at which level the data are to be truncated. By
	72	default the threshold value which belongs to the 95\% quantile,
	73	\code{u=quantile(x,0.95)}.
	74	}
	75	\item{x}{
	76	a numeric data vector from which \code{findThreshold} and
	77	\code{blockMaxima} determine the threshold values and block
	78	maxima values.
	79	For the function \code{deCluster} the argument
	80	\code{x} represents a numeric vector of threshold exceedances
	81	with a \code{times} attribute which should be a numeric
	82	vector containing either the indices or the times/dates
	83	of each exceedance (if times/dates, the attribute should
	84	be an object of class \code{"POSIXct"} or an object that
	85	can be converted to that class; see \code{\link{as.POSIXct}}).
	86	}
	87
	88	}
	89
	90
	91	\details{
	92
	93	\bold{Computing Block Maxima:}
	94	\cr\cr
	95	The function \code{blockMaxima} calculates block maxima from a vector
	96	or a time series, whereas the function
	97	\code{blocks} is more general and allows for the calculation of
	98	an arbitrary function \code{FUN} on blocks.
	99	\cr
	100
	101
	102	\bold{Finding Thresholds:}
	103	\cr\cr
	104	The function \code{findThreshold} finds a threshold so that a given
	105	number of extremes lie above. When the data are tied a threshold is
	106	found so that at least the specified number of extremes lie above.
	107	\cr
	108
	109
	110	\bold{De-Clustering Point Processes:}
	111	\cr\cr
	112	The function \code{deCluster} declusters clustered point process
	113	data so that Poisson assumption is more tenable over a high threshold.
	114
	115	}
	116
	117
	118	\value{
	119
	120	\code{blockMaxima}
	121	\cr
	122	returns a timeSeries object or a numeric vector of block
	123	maxima data.
	124
	125	\code{findThreshold}
	126	\cr
	127	returns a numeric value or vector of suitable thresholds.
	128
	129	\code{pointProcess}
	130	\cr
	131	returns a timeSeries object or a numeric vector of peaks over
	132	a threshold.
	133
	134	\code{deCluster}
	135	\cr
	136	returns a timeSeries object or a numeric vector for the
	137	declustered point process.
	138
	139	}
	140
	141
	142	\references{
	143
	144	Coles S. (2001);
	145	\emph{Introduction to Statistical Modelling of Extreme Values},
	146	Springer.
	147
	148	Embrechts, P., Klueppelberg, C., Mikosch, T. (1997);
	149	\emph{Modelling Extremal Events}, Springer.
	150
	151	}
	152
	153
	154	\author{
	155
	156	Some of the functions were implemented from Alec Stephenson's
	157	R-package \code{evir} ported from Alexander McNeil's S library
	158	\code{EVIS}, \emph{Extreme Values in S}, some from Alec Stephenson's
	159	R-package \code{ismev} based on Stuart Coles code from his book,
	160	\emph{Introduction to Statistical Modeling of Extreme Values} and
	161	some were written by Diethelm Wuertz.
	162
	163	}
	164
	165	\examples{
	166	## findThreshold -
	167	# Threshold giving (at least) fifty exceedances for Danish data:
	168	library(timeSeries)
	169	x <- as.timeSeries(data(danishClaims))
	170	findThreshold(x, n = c(10, 50, 100))
	171
	172	## blockMaxima -
	173	# Block Maxima (Minima) for left tail of BMW log returns:
	174	BMW <- as.timeSeries(data(bmwRet))
	175	colnames(BMW) <- "BMW.RET"
	176	head(BMW)
	177	x <- blockMaxima( BMW, block = 65)
	178	head(x)
	179	\dontrun{
	180	y <- blockMaxima(-BMW, block = 65)
	181	head(y)
	182	y <- blockMaxima(-BMW, block = "monthly")
	183	head(y)}
	184
	185	## pointProcess -
	186	# Return Values above threshold in negative BMW log-return data:
	187	PP = pointProcess(x = -BMW, u = quantile(as.vector(x), 0.75))
	188	PP
	189	nrow(PP)
	190
	191	## deCluster -
	192	# Decluster the 200 exceedances of a particular
	193	DC = deCluster(x = PP, run = 15, doplot = TRUE)
	194	DC
	195	nrow(DC)
	196	}
	197
	198
	199	\keyword{programming}
	200

-4

man/ExtremeIndex.Rd less more

29	29	Segers.
30	30	\cr
31	31
32		The functiona are:
	32	The functions are:
33	33
34	34	\tabular{ll}{
35	35	\code{thetaSim} \tab Simulates a time Series with known theta, \cr
36	36	\code{blockTheta} \tab Computes theta from Block Method, \cr
37	37	\code{clusterTheta} \tab Computes theta from Reciprocal Cluster Method, \cr
38	38	\code{runTheta} \tab Computes theta from Run Method, \cr
39		\code{ferrosegersTheta} \tab Computes Theta according to Ferro and Seegers, \cr
	39	\code{ferrosegersTheta} \tab Computes Theta according to Ferro and Segers, \cr
40	40	\code{exindexPlot} \tab Calculate and Plot Theta(1,2,3), \cr
41	41	\code{exindexesPlot} \tab Calculate Theta(1,2) and Plot Theta(1). }
42	42

76	76	the block size. Either \code{"monthly"}, \code{"quarterly"} or
77	77	an integer value. An integer value is interpreted as the number
78	78	of data values in each successive block. The default value is
79		\code{"monthly"} which correpsond for daily data to an approximately
	79	\code{"monthly"} which corresponds for daily data to an approximately
80	80	22-day periods.
81	81	}
82	82	\item{description}{

87	87	}
88	88	\item{labels}{
89	89	whether or not axes should be labelled. If set to \code{FALSE}
90		then user specified lables can be passed through the \code{"..."}
	90	then user specified labels can be passed through the \code{"..."}
91	91	argument.
92	92	}
93	93	\item{model}{

192	192	## of the BMW log returns:
193	193	data(bmwRet)
194	194	par(mfrow = c(2, 2), cex = 0.7)
	195	library(timeSeries)
195	196	exindexPlot( as.timeSeries(bmwRet), block = "quarterly")
196	197	exindexPlot(-as.timeSeries(bmwRet), block = "quarterly")
197	198

-6

man/ExtremesData.Rd less more

33	33	A collection and description of functions for
34	34	explorative data analysis. The tools include
35	35	plot functions for emprical distributions, quantile
36		plots, graphs exploring the properties of exceedences
	36	plots, graphs exploring the properties of exceedances
37	37	over a threshold, plots for mean/sum ratio and for
38	38	the development of records.
39	39	\cr

51	51	\code{ssrecordsPlot} \tab another variant, investigates subsamples, \cr
52	52	\code{sllnPlot} \tab verifies Kolmogorov's strong law of large numbers, \cr
53	53	\code{lilPlot} \tab verifies Hartman-Wintner's law of the iterated logarithm, \cr
54		\code{xacfPlot} \tab ACF of exceedences over a threshold, \cr
	54	\code{xacfPlot} \tab ACF of exceedances over a threshold, \cr
55	55	\code{normMeanExcessFit} \tab fits mean excesses with a normal density, \cr
56	56	\code{ghMeanExcessFit} \tab fits mean excesses with a GH density, \cr
57	57	\code{hypMeanExcessFit} \tab fits mean excesses with a HYP density, \cr

145	145	\item{threshold, trim}{
146	146	[qPlot][xacfPlot] - \cr
147	147	a numeric value at which data are to be left-truncated, value
148		at which data are to be right-truncated or the thresold value,
	148	at which data are to be right-truncated or the threshold value,
149	149	by default 95\%.
150	150	}
151	151	\item{trace}{

241	241	with the help of the function \code{ssrecordsPlot}.
242	242	\cr
243	243
244		\bold{Plot of Kolmogorov's and Hartman-Wintern's Laws:}
	244	\bold{Plot of Kolmogorov's and Hartman-Wintner's Laws:}
245	245	\cr\cr
246	246	The function \code{sllnPlot} verifies Kolmogorov's strong law of
247	247	large numbers, and the function \code{lilPlot} verifies
248	248	Hartman-Wintner's law of the iterated logarithm.
249	249	\cr
250	250
251		\bold{ACF Plot of Exceedences over a Thresold:}
	251	\bold{ACF Plot of Exceedances over a Threshold:}
252	252	\cr\cr
253	253	This function plots the autocorrelation functions of heights and
254		distances of exceedences over a threshold.
	254	distances of exceedances over a threshold.
255	255	\cr
256	256	}
257	257

301	301	\examples{
302	302	## Danish fire insurance data:
303	303	data(danishClaims)
	304	library(timeSeries)
304	305	danishClaims = as.timeSeries(danishClaims)
305	306
306	307	## emdPlot -

-1

man/GevDistribution.Rd less more

55	55	probabilities are \code{P[X <= x]}, otherwise, \code{P[X > x]}.
56	56	}
57	57	\item{method}{
58		a character sgtring denoting what should be displayed. Either
	58	a character string denoting what should be displayed. Either
59	59	the density and \code{"dist"} or random variates \code{"rvs"}.
60	60	}
61	61	\item{n}{

-1

man/GevMdaEstimation.Rd less more

65	65	\cr
66	66	[shaparmPlot] - \cr
67	67	a vector of logicals of the same lengths as tails
68		defining for wich tail depths plots should be created,
	68	defining for which tail depths plots should be created,
69	69	by default plots will be generated for a tail depth of 5
70	70	percent. By default \code{c(FALSE, FALSE, FALSE, FALSE,
71	71	TRUE, FALSE, FALSE, FALSE, FALSE, FALSE)}.

264	264
265	265	\examples{
266	266	## Load Data:
	267	library(timeSeries)
267	268	x = as.timeSeries(data(danishClaims))
268	269	colnames(x) <- "Danish"
269	270	head(x)

-5

man/GevModelling.Rd less more

73	73	\cr
74	74	[shaparmPlot] - \cr
75	75	a vector of logicals of the same lengths as tails
76		defining for wich tail depths plots should be created,
	76	defining for which tail depths plots should be created,
77	77	by default plots will be generated for a tail depth of 5
78	78	percent. By default \code{c(FALSE, FALSE, FALSE, FALSE,
79	79	TRUE, FALSE, FALSE, FALSE, FALSE, FALSE)}.

109	109	\item{type}{
110	110	a character string denoting the type of parameter estimation,
111	111	either by maximum likelihood estimation \code{"mle"}, the
112		default value, or by the probability weighted moment menthod
	112	default value, or by the probability weighted moment method
113	113	\code{"pwm"}.
114	114	}
115	115	\item{which}{
116	116	[plot][summary] - \cr
117	117	a vector of logicals, one for each plot, denoting which plot
118		should be displayed. Alkternatively if \code{which="ask"} the
	118	should be displayed. Alternatively if \code{which="ask"} the
119	119	user will be interactively asked which of the plots should be
120		desplayed. By default \code{which="all"}.
	120	displayed. By default \code{which="all"}.
121	121	}
122	122	\item{x}{
123	123	[dgev][devd] - \cr

139	139	\item{xi, mu, beta}{
140	140	[*gev] - \cr
141	141	\code{xi} is the shape parameter, \code{mu} the location parameter,
142		and \code{sigma} is the scale parameter. The default values are
	142	and \code{beta} is the scale parameter. The default values are
143	143	\code{xi=1}, \code{mu=0}, and \code{beta=1}. Note, if \code{xi=0}
144	144	the distribution is of type Gumbel.
145	145	}

-0

man/GevRisk.Rd less more

213	213	\examples{
214	214	## Load Data:
215	215	# BMW Stock Data - negative returns
	216	library(timeSeries)
216	217	x = -as.timeSeries(data(bmwRet))
217	218	colnames(x)<-"BMW"
218	219	head(x)

-4

man/GpdModelling.Rd less more

84	84	a character string which allows for a project title.
85	85	}
86	86	\item{type}{
87		a character string selecting the desired estimation mehtod, either
88		\code{"mle"} for the maximum likelihood mehtod or \code{"pwm"} for
	87	a character string selecting the desired estimation method, either
	88	\code{"mle"} for the maximum likelihood method or \code{"pwm"} for
89	89	the probability weighted moment method. By default, the first will
90	90	be selected. Note, the function \code{gpd} uses \code{"ml"}.
91	91	}

108	108	the data vector. Note, there are two different names
109	109	for the first argument \code{x} and \code{data} depending
110	110	which function name is used, either \code{gpdFit} or the
111		EVIS synonyme \code{gpd}.
	111	EVIS synonym \code{gpd}.
112	112	\cr
113	113	[print][plot] - \cr
114	114	a fitted object of class \code{"gpdFit"}.

227	227	representation to an existing plot is added. Expected shortfall is
228	228	the expected size of the loss, given that a particular quantile of the
229	229	loss distribution is exceeded. The GPD approximation in the tail is used
230		to estimate expected shortfall. The likelihood is reparametrised in
	230	to estimate expected shortfall. The likelihood is reparametrized in
231	231	terms of the unknown expected shortfall and profile likelihood arguments
232	232	are used to construct a confidence interval.
233	233	\cr

-4

man/GpdRisk.Rd less more

116	116	the lowest and maximum number of exceedances to be considered.
117	117	}
118	118	\item{type}{
119		a character string selecting the desired estimation mehtod, either
120		\code{"mle"} for the maximum likelihood mehtod or \code{"pwm"} for
	119	a character string selecting the desired estimation method, either
	120	\code{"mle"} for the maximum likelihood method or \code{"pwm"} for
121	121	the probability weighted moment method. By default, the first will
122	122	be selected. Note, the function \code{gpd} uses \code{"ml"}.
123	123	}

129	129	the data vector. Note, there are two different names
130	130	for the first argument \code{x} and \code{data} depending
131	131	which function name is used, either \code{gpdFit} or the
132		EVIS synonyme \code{gpd}.
	132	EVIS synonym \code{gpd}.
133	133	\cr
134	134	[print][plot] - \cr
135	135	a fitted object of class \code{"gpdFit"}.

243	243	representation to an existing plot is added. Expected shortfall is
244	244	the expected size of the loss, given that a particular quantile of the
245	245	loss distribution is exceeded. The GPD approximation in the tail is used
246		to estimate expected shortfall. The likelihood is reparametrised in
	246	to estimate expected shortfall. The likelihood is reparametrized in
247	247	terms of the unknown expected shortfall and profile likelihood arguments
248	248	are used to construct a confidence interval.
249	249	\cr

285	285
286	286	\examples{
287	287	## Load Data:
	288	library(timeSeries)
288	289	danish = as.timeSeries(data(danishClaims))
289	290
290	291	## Tail Plot:

-1

man/ValueAtRisk.Rd less more

14	14	Value-at-Risk and conditional Value-at-Risk
15	15	\cr
16	16
17		The functiona are:
	17	The functions are:
18	18
19	19	\tabular{ll}{
20	20	\code{VaR} \tab Computes Value-at-Risk, \cr

+16

-1

man/data.Rd less more

7	7
8	8	\title{Time Series Data Sets}
9	9
	10	\usage{
	11	bmwRet
	12	danishClaims
	13	}
10	14
11	15	\description{
12		Data sets used in the examples of the timeSeries packages.
	16	Data sets used in the examples of the fExtremes packages.
13	17	}
	18
	19	\format{
	20	\code{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.
	21
	22	\code{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.
	23	}
	24
	25	\examples{
	26	head(bmwRet)
	27	head(danishClaims)
	28	}

-1

tests/doRUnit.R less more

0	0	#### doRUnit.R --- Run RUnit tests
1	1	####------------------------------------------------------------------------
2	2
3		### Origianlly follows Gregor Gojanc's example in CRAN package 'gdata'
	3	### Originally follows Gregor Gojanc's example in CRAN package 'gdata'
4	4	### and the corresponding section in the R Wiki:
5	5	### http://wiki.r-project.org/rwiki/doku.php?id=developers:runit
6	6

21	21
22	22	stopifnot(file.exists(path), file.info(path.expand(path))$isdir)
23	23
	24	# Make sure that required packages are loaded
	25	library(fBasics)
	26	library(timeDate)
	27	library(timeSeries)
	28
24	29	source(file.path(path, "runTests.R"), echo = TRUE)
25	30	}