Description Usage Arguments References Examples
The function estimates empirical confidence bands for the sample returt time extremogram via a permutation procedure under the assumption that the data are independent.
1 2 |
x |
Univariate time series (a vector). |
m |
Number of permutations (an integer). |
type |
Type of confidence bands. If type=1, it adds all permutations to the sample
extremogram plot. If type=2, it adds the |
exttype |
Extremogram type (see |
maxlag |
Number of lags to include in the extremogram (an integer). |
uplevel |
Quantile of the time series to indicate a upper tail extreme event (a number between 0 and 1, default is 1). |
lowlevel |
Quantile of the time series to indicate a lower tail extreme event (a number between 0 and 1, default is 0). |
start |
The lag that the extremogram plots starts at (an integer not greater than |
alpha |
Significance level for the confidence bands (a number between 0 and 1, default is 0.05). |
Davis, R. A., Mikosch, T., & Cribben, I. (2012). Towards estimating extremal serial dependence via the bootstrapped extremogram. Journal of Econometrics,170(1), 142-152.
Davis, R. A., Mikosch, T., & Cribben, I. (2011). Estimating extremal dependence in univariate and multivariate time series via the extremogram.arXiv preprint arXiv:1107.5592.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | # generate a GARCH(1,1) process
omega = 1
alpha = 0.1
beta = 0.6
n = 1000
uplevel = 0.95
lowlevel = 0.05
exttype = 3
maxlag = 70
type = 3
m = 10
df = 3
G = extremogram:::garchsim(omega,alpha,beta,n,df)
extremogramr(G, type, maxlag, uplevel, lowlevel, 1, 1)
permfnr(G, m, type, exttype, maxlag, uplevel, lowlevel, 1, 0.05)
|
[[1]]
[1] 0.30303030 0.08080808 0.07070707 0.01010101 0.03030303 0.10101010
[7] 0.03030303 0.03030303 0.02020202 0.02020202 0.01010101 0.01010101
[13] 0.01010101 0.03030303 0.02020202 0.02020202 0.01010101 0.01010101
[19] 0.01010101 0.01010101 0.03030303 0.01010101 0.02020202 0.01010101
[25] 0.01010101 0.01010101 0.01010101 0.01010101 0.01010101 0.01010101
[31] 0.01010101 0.01010101 0.01010101 0.00000000 0.00000000 0.00000000
[37] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[43] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[49] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[55] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[61] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[67] 0.00000000 0.00000000 0.00000000 0.00000000
[[2]]
[1] 3 59 15 39 20 10 7 9 1 2 1 2 9 6 15 3 25 1 6 43 4 2 31 17 1
[26] 1 6 1 3 1 1 14 25 44 1 16 22 6 1 1 1 7 17 36 30 3 8 24 3 1
[51] 5 1 32 16 2 5 6 5 1 1 1 2 23 8 1 1 1 1 6 1 1 6 6 29 11
[76] 10 7 18 1 1 28 1 3 15 8 2 6 23 1 19 1 2 3 12 23 1 2 1 6
[[3]]
[1] 9.79798
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