1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
|
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Library General Public License for more details.
#
# You should have received a copy of the GNU Library General
# Public License along with this library; if not, write to the
# Free Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA 02111-1307 USA
# Copyrights (C)
# for this R-port:
# 1999 - 2007, Diethelm Wuertz, GPL
# Diethelm Wuertz <wuertz@itp.phys.ethz.ch>
# info@rmetrics.org
# www.rmetrics.org
# for the code accessed (or partly included) from other R-ports:
# see R's copyright and license files
# for the code accessed (or partly included) from contributed R-ports
# and other sources
# see Rmetrics's copyright file
################################################################################
# FUNCTION DATA PREPROCESSING:
# blockMaxima Returns block maxima from a time series
# findThreshold Upper threshold for a given number of extremes
# pointProcess Returns peaks over a threshold from a time series
# deCluster Declusters a point process
################################################################################
test.blockMaxima =
function()
{
# blockMaxima - Returns block maxima from a time series
# blockMaxima(x, block = c("monthly", "quarterly"), doplot = FALSE)
# Time Series Data:
x = MSFT[, "Close"]
x.ret = 100*returns(x)
head(x.ret)
class(x.ret)
# Monthly Block Maxima:
ans = blockMaxima(x.ret, block = "monthly", doplot = TRUE)
print(ans)
# Quarterly Block Maxima:
ans = blockMaxima(x.ret, block = "q", doplot = TRUE)
print(ans)
# 20-Days Block Maxima:
ans = blockMaxima(x.ret, block = 20, doplot = TRUE)
print(ans)
# Numerical Data Vector:
x.ret = as.vector(x.ret)
head(x.ret)
ans = blockMaxima(x.ret, block = 20, doplot = TRUE)
print(ans)
# Stops by stopifnot() - Check:
# blockMaxima(x.ret, block = "month", doplot = TRUE)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.findThreshold =
function()
{
# findThreshold - Upper threshold for a given number of extremes
# findThreshold(x, n = floor(0.05*length(as.vector(x))), doplot = FALSE)
# Time Series Data:
x = MSFT[, "Close"]
x.ret = 100*returns(x)
head(x.ret)
class(x.ret)
# Find 99% Threshold:
par(mfrow = c(2, 2), cex = 0.7)
par(ask = FALSE)
findThreshold(x.ret, n = floor(0.01*length(as.vector(x))), doplot = TRUE)
# Remark - Alternative use ...
quantile(x.ret, probs = 1 - 0.01)
quantile(x.ret, probs = 1 - 0.01, type = 1)
# Find 95% Threshold:
findThreshold(x.ret, doplot = TRUE)
# Find 90% Threshold:
findThreshold(x.ret, n = floor(0.1*length(as.vector(x))), doplot = TRUE)
# Try if x is a numeric vector:
findThreshold(as.vector(x.ret), doplot = TRUE)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.pointProcess =
function()
{
# pointProcess - Returns peaks over a threshold from a time series
# pointProcess(x, u = quantile(x, 0.95), doplot = FALSE)
# Time Series Data:
x = MSFT[, "Close"]
x.ret = 100*returns(x)
head(x.ret)
class(x.ret)
# Plot Series:
par(mfrow = c(2, 1), cex = 0.7)
par(ask = FALSE)
# plot(x.ret, type = "l", main = "Series")
# abline(h = 0, col = "red", lty = 3)
# or use ...
seriesPlot(x.ret)
# Point Process:
pp = pointProcess(x.ret, u = quantile(x.ret, 0.8))
pp
plot(pp, type = "b", main = "Point Process")
abline(h = 0, col = "red", lty = 3)
# Try seriesPlot(pp)
# ... add points in graph
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.deCluster =
function()
{
# deCluster - Declusters a point process
# deCluster(x, run = 20, doplot = TRUE)
# Time Series Data:
x = MSFT[, "Close"]
x.ret = 100*returns(x)
head(x.ret)
class(x.ret)
# Decluster Time Series:
tS = deCluster(x = x.ret, run = 3)
print(tS)
# Return Value:
return()
}
################################################################################
|
