Description Usage Arguments Value References Examples
The function estimates confidence bands for the sample return time extremogram using the stationary bootstrap.
1 2 |
x |
Univariate time series (a vector). |
R |
Number of bootstrap replications (an integer). |
l |
Mean block size for stationary bootstrap or mean of the geometric distribution used to generate resampling blocks (an integer that is not longer than the length of the time series). |
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). |
type |
Extremogram type (see function |
par |
If par = 1, the bootstrap replication procedure will be parallelized. If par = 0, no parallelization will be used. |
start |
The lag that the extremogram plots starts at (an integer not greater than |
cutoff |
The cutoff of the y-axis on the plot (a number between 0 and 1, default is 1). |
alpha |
Significance level for the confidence bands (a number between 0 and 1, default is 0.05). |
Returns a plot of the confidence bands for the sample return time extremogram.
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 17 | # generate a GARCH(1,1) process
omega = 1
alpha = 0.1
beta = 0.6
n = 1000
uplevel = 0.95
lowlevel = 0.05
type = 3
maxlag = 70
df = 3
R = 10
l = 30
par = 0
G = extremogram:::garchsim(omega,alpha,beta,n,df)
extremogramr(G, type, maxlag, uplevel, lowlevel, 1, 1)
bootconfr(G, R, l, maxlag, uplevel, lowlevel, type, par, 1, 1, 0.05)
|
[[1]]
[1] 0.36363636 0.13131313 0.05050505 0.05050505 0.02020202 0.02020202
[7] 0.04040404 0.01010101 0.02020202 0.02020202 0.01010101 0.01010101
[13] 0.01010101 0.01010101 0.01010101 0.02020202 0.01010101 0.01010101
[19] 0.02020202 0.02020202 0.03030303 0.01010101 0.01010101 0.01010101
[25] 0.02020202 0.01010101 0.01010101 0.01010101 0.01010101 0.01010101
[31] 0.01010101 0.00000000 0.00000000 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] 14 2 1 1 2 22 35 11 1 8 1 35 3 3 1 2 16 2 4 23 4 2 1 1 1
[26] 1 1 1 1 1 1 2 1 3 5 1 53 4 1 2 1 1 1 2 1 1 1 1 1 1
[51] 1 1 7 21 40 2 1 19 34 1 1 12 1 17 11 2 20 2 15 10 3 8 43 9 4
[76] 5 1 26 7 10 21 1 13 23 28 23 8 46 8 3 17 2 54 52 4 22 1 1 2
[[3]]
[1] 9.575758
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