(Un-)conditional rolling risk estimation using vine copulas
Source:R/estimate_risk_roll.R
estimate_risk_roll.RdAs this is the main workhorse function with a lot going on under the hood it is advised to have a look at the vignettes or even better the package website as they provide a detailed hands on and theoretical documentation of what this function is doing and how it is intended to be used. For a short summarized explanation have a look at the Details section below.
Usage
estimate_risk_roll(
data,
weights = NULL,
marginal_settings,
vine_settings,
alpha = 0.05,
risk_measures = c("VaR", "ES_mean"),
n_samples = 1000,
cond_vars = NULL,
cond_u = 0.05,
n_mc_samples = 1000,
trace = FALSE,
cutoff_depth = NULL,
prior_resid_strategy = FALSE
)Arguments
- data
Matrix, data.frame or other object coercible to a data.table storing the numeric asset returns in the named columns (at least 3). Moreover missing values must be imputed beforehand.
- weights
Corresponding named non-negative weights of the assets (conditioning variables must have weight 0). Default
NULLgives equal weight of 1 to each non conditional asset. Alternatively one can use a matrix with as many rows as vine windows for changing weights. The matrix must have column names corresponding to the assets and conditional assets have to have weight 0.- marginal_settings
marginal_settingsS4 object containing the needed information for the ARMA-GARCH i.e. marginal models fitting. Note that themarginal_settingsandvine_settingsobjects have to match as described further below.- vine_settings
vine_settingsS4 object containing the needed information for the vine copula model fitting. Note that themarginal_settingsandvine_settingsobjects have to match as described further below.- alpha
Numeric vector specifying the confidence levels in (0,1) at which the risk measures should be calculated.
- risk_measures
Character vector with valid choices for risk measures to estimate. Currently available are the Value at Risk
VaRwhich is implemented inest_var()and 3 estimation methods of the Expected ShortfallES_mean,ES_medianandES_mcall implemented inest_es().- n_samples
Positive count of samples to be used at the base of the risk measure estimation.
- cond_vars
Names of the variables to sample conditionally from (currently \(\le 2\) variables).
- cond_u
Numeric vector specifying the corresponding quantiles in (0,1) of the conditional variable(s) conditioned on which the conditional risk measures should be calculated. Additionally always the conditioning values corresponding to the residual of one time unit prior are used as conditional variables (
cond_u= 'prior_resid' in the risk measure output) if the flagprior_residis set to TRUE, otherwise the conditioning values corresponding to the realized residual are used (cond_u= 'resid' in the risk measure output). The latter case corresponds to the default.- n_mc_samples
Positive count of samples for the Monte Carlo integration if the risk measure
ES_mcis used. (Seeest_es())- trace
If set to TRUE the algorithm will print a little information while running.
- cutoff_depth
Positive count that specifies the depth up to which the edges of the to be constructed D-vine copula are considered in the algorithm that determines the ordering for the D-vine fitting using partial correlations. The default
NULLconsiders all edges and seems in most use cases reasonable. This argument is only relevant if D-vines are used.- prior_resid_strategy
Logical flag that indicates whether as the additionally used conditioning values the prior day residual (if this flag is TRUE) or the realized residuals are used. The default are the realized residuals. Note that the resulting conditional risk measures use realized data so they are only for comparisons as they suffer from information leakage.
Value
In the unconditional case an S4 object of class portvine_roll and
in the conditional case its child class cond_portvine_roll. For details
see portvine_roll.
Details
Roughly speaking the function performs the following steps for the unconditional risk measure estimation:
Fit for each asset marginal time series models i.e. ARMA-GARCH models in a rolling window fashion. The models as well as the rolling window size and training size are specified via the
marginal_settingsargument.Model the dependence between the assets with a vine copula model trained on the standardized residuals transformed to the copula scale via the probability integral transform. This is also performed in a rolling window fashion where one can use the same window size for the vine windows as used for the marginal ones or a smaller window size. This window size, the training size for the vine copula as well as the copula fitting arguments are specified via the
vine_settingsargument.Using the copula and the forecasted means and volatilities of the assets one simulates
n_samplesmany forecasted portfolio level log returns for every time unit in every specified rolling window.Based on these samples one estimates portfolio level risk measures.
Additionally one can perform conditional risk measure estimation with up
to two conditional log return series like market indices. Using this approach
does not change the marginal models part but for the copula a D-vine with a
special ordering i.e. the index or the indices are fixed as the rightmost
leafs is fitted. One then simulates conditional forecasted portfolio log
returns which then
results in conditional risk measure estimates that can be particularly
interesting in stress testing like situations. One conditions on a
pre-specified quantile level (cond_u) of the conditioning assets
(cond_vars) and for comparison one also conditions either on the behavior
of the conditioning asset one time unit before
(prior_resid_strategy = TRUE) or the realized behavior of the
conditioning asset (prior_resid_strategy = FALSE).
Matching marginal and vine settings
First of all there must be at least 2 marginal windows. Thus train_size +
refit_size slot in the marginal_settings class object must be smaller
than the overall input data size. Moreover the refit_size of the marginal
models must be dividable by the refit_size of the vine copula models e.g.
possible combinations are 50 and 50, 50 and 25, 50 and 10. Furthermore the
train_size of the vines must be smaller or equal to the train_size of
the marginal models.
Parallel processing
This function uses the future
framework for parallelization that allows maximum flexibility for the user
while having safe speedups for example regarding random number generation.
The default is of course the standard non parallel sequential evaluation.
The user has to do nothing in order for this default to work. If the user
wants to run the code in parallel there are many options from parallel on a
single machine up to a high performance compute (HPC) cluster, all of this
with just one setting switch i.e. by calling the function future::plan()
with the respective argument before the function call. Common options are
future::plan("multisession") which works on all major operating systems
and uses all available cores to run the code in parallel local R sessions.
To specify the number of workers use
future::plan("multisession", workers = 2). To go back to sequential
processing and to shut down the parallel sessions use
future::plan("sequential").
For more information have a look at future::plan(). The two following
loops are processed in parallel by default if a parallel future::plan()
is set:
The marginal model fitting i.e. all assets individually in parallel.
The vine windows i.e. the risk estimates and the corresponding vine copula models are computed in parallel for each rolling vine window.
In addition the function allows for nested parallelization which has to
be done with care. So in addition to the 2 loops above one can further
run each computation for each time unit in the vine windows in parallel which
might be especially interesting if the n_samples argument is large. Then
the default parallelization has to be tweaked to not only parallelize the
first level of parallelization which are the 2 loops above. This can be
achieved e.g. via future::plan(list(future::tweak(future::multisession, workers = 4), future::tweak(future::multisession, workers = 2))). This
setting would run the 2 primary loops in 4 parallel R sessions and in
addition each of the 4 primary parallel sessions would itself use 2 sessions
within the nested parallel loop over the time units in the vine window. This
results in a need for at least 2 times 4 so 8 threads on the hardware side.
More details can be found in the extensive documentation of the
future framework.
Examples
# For better illustrated examples have a look at the vignettes
# and/or the package website.
# \donttest{
data("sample_returns_small")
ex_marg_settings <- marginal_settings(
train_size = 900,
refit_size = 50
)
ex_vine_settings <- vine_settings(
train_size = 100,
refit_size = 50,
family_set = c("gaussian", "gumbel"),
vine_type = "dvine"
)
# unconditionally
risk_roll <- estimate_risk_roll(
sample_returns_small,
weights = NULL,
marginal_settings = ex_marg_settings,
vine_settings = ex_vine_settings,
alpha = c(0.01, 0.05),
risk_measures = c("VaR", "ES_mean"),
n_samples = 10,
trace = FALSE
)
# conditional on one asset
risk_roll_cond <- estimate_risk_roll(
sample_returns_small,
weights = NULL,
marginal_settings = ex_marg_settings,
vine_settings = ex_vine_settings,
alpha = c(0.01, 0.05),
risk_measures = c("VaR", "ES_mean"),
n_samples = 10,
cond_vars = "GOOG",
cond_u = c(0.05, 0.5),
trace = FALSE,
prior_resid_strategy = TRUE
)
# have a superficial look
risk_roll_cond
#> An object of class <cond_portvine_roll>
#> Conditional variable(s): GOOG
#> Number of ARMA-GARCH/ marginal windows: 2
#> Number of vine windows: 2
#> Risk measures estimated: VaR ES_mean
#> Alpha levels used: 0.01 0.05
#>
#> Time taken: 0.1372 minutes
# a slightly more detailed look
summary(risk_roll_cond)
#> An object of class <cond_portvine_roll>
#>
#> --- Conditional settings ---
#> Conditional variable(s): GOOG
#> Number of conditional estimated risk measures: 1200
#> Conditioning quantiles: 0.05 0.5
#>
#> --- Marginal models ---
#> Number of ARMA-GARCH/ marginal windows: 2
#> Train size: 900
#> Refit size: 50
#>
#> --- Vine copula models ---
#> Number of vine windows: 2
#> Train size: 100
#> Refit size: 50
#> Vine copula type: dvine
#> Vine family set: gaussian gumbel
#>
#> --- Risk estimation ---
#> Risk measures estimated: VaR ES_mean
#> Alpha levels used: 0.01 0.05
#> Number of estimated risk measures: 400
#> Number of samples for each risk estimation: 10
#>
#> Time taken: 0.1372 minutes.
# actually use the results by extracting important fitted quantities
fitted_vines(risk_roll_cond)
#> [[1]]
#> 3-dimensional vine copula fit ('vinecop')
#> nobs = 100 logLik = 54.82 npars = 3 AIC = -103.64 BIC = -95.82
#>
#> [[2]]
#> 3-dimensional vine copula fit ('vinecop')
#> nobs = 100 logLik = 67.96 npars = 3 AIC = -129.93 BIC = -122.11
#>
fitted_marginals(risk_roll_cond)
#> $AAPL
#>
#> *-------------------------------------*
#> * GARCH Roll *
#> *-------------------------------------*
#> No.Refits : 2
#> Refit Horizon : 50
#> No.Forecasts : 100
#> GARCH Model : sGARCH(1,1)
#> Distribution : sstd
#>
#> Forecast Density:
#> Mu Sigma Skew Shape Shape(GIG) Realized
#> 1972-06-20 0.0016 0.0139 1.0245 3.4459 0 0.0061
#> 1972-06-21 0.0016 0.0137 1.0245 3.4459 0 -0.0363
#> 1972-06-22 0.0009 0.0153 1.0245 3.4459 0 0.0138
#> 1972-06-23 0.0012 0.0152 1.0245 3.4459 0 0.0149
#> 1972-06-24 0.0014 0.0152 1.0245 3.4459 0 0.0109
#> 1972-06-25 0.0015 0.0151 1.0245 3.4459 0 -0.0040
#>
#> ..........................
#> Mu Sigma Skew Shape Shape(GIG) Realized
#> 1972-09-22 0.0012 0.0118 1.0413 3.5337 0 0.0038
#> 1972-09-23 0.0012 0.0116 1.0413 3.5337 0 0.0000
#> 1972-09-24 0.0012 0.0114 1.0413 3.5337 0 -0.0257
#> 1972-09-25 0.0007 0.0124 1.0413 3.5337 0 0.0002
#> 1972-09-26 0.0008 0.0122 1.0413 3.5337 0 0.0028
#> 1972-09-27 0.0009 0.0120 1.0413 3.5337 0 -0.0109
#>
#> Elapsed: 1.084382 secs
#>
#> $GOOG
#>
#> *-------------------------------------*
#> * GARCH Roll *
#> *-------------------------------------*
#> No.Refits : 2
#> Refit Horizon : 50
#> No.Forecasts : 100
#> GARCH Model : sGARCH(1,1)
#> Distribution : sstd
#>
#> Forecast Density:
#> Mu Sigma Skew Shape Shape(GIG) Realized
#> 1972-06-20 6e-04 0.0106 0.9752 4.1755 0 -0.0042
#> 1972-06-21 5e-04 0.0104 0.9752 4.1755 0 -0.0171
#> 1972-06-22 2e-04 0.0120 0.9752 4.1755 0 0.0079
#> 1972-06-23 7e-04 0.0117 0.9752 4.1755 0 0.0090
#> 1972-06-24 9e-04 0.0116 0.9752 4.1755 0 -0.0005
#> 1972-06-25 7e-04 0.0110 0.9752 4.1755 0 0.0051
#>
#> ..........................
#> Mu Sigma Skew Shape Shape(GIG) Realized
#> 1972-09-22 5e-04 0.0108 0.9821 4.2039 0 -0.0012
#> 1972-09-23 6e-04 0.0105 0.9821 4.2039 0 -0.0033
#> 1972-09-24 6e-04 0.0103 0.9821 4.2039 0 -0.0032
#> 1972-09-25 6e-04 0.0101 0.9821 4.2039 0 -0.0070
#> 1972-09-26 4e-04 0.0101 0.9821 4.2039 0 -0.0012
#> 1972-09-27 6e-04 0.0099 0.9821 4.2039 0 -0.0017
#>
#> Elapsed: 1.08075 secs
#>
#> $AMZN
#>
#> *-------------------------------------*
#> * GARCH Roll *
#> *-------------------------------------*
#> No.Refits : 2
#> Refit Horizon : 50
#> No.Forecasts : 100
#> GARCH Model : sGARCH(1,1)
#> Distribution : sstd
#>
#> Forecast Density:
#> Mu Sigma Skew Shape Shape(GIG) Realized
#> 1972-06-20 0.0011 0.0145 1.0226 3.2239 0 -0.0079
#> 1972-06-21 0.0012 0.0144 1.0226 3.2239 0 -0.0259
#> 1972-06-22 0.0004 0.0150 1.0226 3.2239 0 0.0115
#> 1972-06-23 0.0025 0.0149 1.0226 3.2239 0 0.0157
#> 1972-06-24 0.0012 0.0149 1.0226 3.2239 0 -0.0006
#> 1972-06-25 0.0014 0.0148 1.0226 3.2239 0 -0.0047
#>
#> ..........................
#> Mu Sigma Skew Shape Shape(GIG) Realized
#> 1972-09-22 0.0013 0.0153 1.0288 3.4609 0 -0.0024
#> 1972-09-23 0.0013 0.0151 1.0288 3.4609 0 -0.0055
#> 1972-09-24 0.0012 0.0149 1.0288 3.4609 0 0.0072
#> 1972-09-25 0.0018 0.0148 1.0288 3.4609 0 0.0047
#> 1972-09-26 0.0013 0.0146 1.0288 3.4609 0 0.0032
#> 1972-09-27 0.0016 0.0144 1.0288 3.4609 0 -0.0141
#>
#> Elapsed: 1.149987 secs
#>
# and of course most importantly the risk measure estimates
risk_estimates(
risk_roll,
risk_measures = "ES_mean",
alpha = 0.05, exceeded = TRUE
)
#> risk_measure risk_est alpha row_num vine_window realized exceeded
#> 1 ES_mean -0.07295805 0.05 901 1 -0.0060446548 FALSE
#> 2 ES_mean -0.03290882 0.05 902 1 -0.0792850109 TRUE
#> 3 ES_mean -0.06805028 0.05 903 1 0.0331630350 FALSE
#> 4 ES_mean -0.03583559 0.05 904 1 0.0396444178 FALSE
#> 5 ES_mean -0.01870834 0.05 905 1 0.0098307520 FALSE
#> 6 ES_mean -0.02142929 0.05 906 1 -0.0035546832 FALSE
#> 7 ES_mean -0.02547554 0.05 907 1 -0.0549415153 TRUE
#> 8 ES_mean -0.02449734 0.05 908 1 -0.0048120862 FALSE
#> 9 ES_mean -0.04201450 0.05 909 1 -0.0116750983 FALSE
#> 10 ES_mean -0.03853510 0.05 910 1 0.0500823533 FALSE
#> 11 ES_mean -0.02395931 0.05 911 1 -0.0055013938 FALSE
#> 12 ES_mean -0.03817386 0.05 912 1 -0.0164475619 FALSE
#> 13 ES_mean -0.02278573 0.05 913 1 -0.0097478129 FALSE
#> 14 ES_mean -0.02706067 0.05 914 1 0.0085510175 FALSE
#> 15 ES_mean -0.03796080 0.05 915 1 0.0254935553 FALSE
#> 16 ES_mean -0.06939725 0.05 916 1 0.0257264559 FALSE
#> 17 ES_mean -0.04560588 0.05 917 1 0.0277721259 FALSE
#> 18 ES_mean -0.06687216 0.05 918 1 -0.0042152555 FALSE
#> 19 ES_mean -0.06665762 0.05 919 1 -0.0349681922 FALSE
#> 20 ES_mean -0.02523827 0.05 920 1 0.0008786021 FALSE
#> 21 ES_mean -0.01767372 0.05 921 1 0.0166985327 FALSE
#> 22 ES_mean -0.02601986 0.05 922 1 -0.0405427416 TRUE
#> 23 ES_mean -0.04755827 0.05 923 1 0.0331199507 FALSE
#> 24 ES_mean -0.05056931 0.05 924 1 0.0039553287 FALSE
#> 25 ES_mean -0.03124714 0.05 925 1 0.0128577234 FALSE
#> 26 ES_mean -0.03343012 0.05 926 1 -0.0267688040 FALSE
#> 27 ES_mean -0.04297314 0.05 927 1 -0.0006434490 FALSE
#> 28 ES_mean -0.02859857 0.05 928 1 -0.0262126037 FALSE
#> 29 ES_mean -0.05233991 0.05 929 1 0.0033384649 FALSE
#> 30 ES_mean -0.09551624 0.05 930 1 -0.0029087903 FALSE
#> 31 ES_mean -0.03039656 0.05 931 1 -0.0252220647 FALSE
#> 32 ES_mean -0.03709922 0.05 932 1 -0.0239892893 FALSE
#> 33 ES_mean -0.04276227 0.05 933 1 -0.0331961832 FALSE
#> 34 ES_mean -0.05135546 0.05 934 1 0.0200051369 FALSE
#> 35 ES_mean -0.02613476 0.05 935 1 0.0410831353 FALSE
#> 36 ES_mean -0.03107721 0.05 936 1 0.0049106979 FALSE
#> 37 ES_mean -0.04841006 0.05 937 1 0.0206976658 FALSE
#> 38 ES_mean -0.02072873 0.05 938 1 -0.0103703417 FALSE
#> 39 ES_mean -0.07969336 0.05 939 1 0.0068955614 FALSE
#> 40 ES_mean -0.08728625 0.05 940 1 -0.0042075885 FALSE
#> 41 ES_mean -0.16836889 0.05 941 1 0.0472190581 FALSE
#> 42 ES_mean -0.01343368 0.05 942 1 0.0174462155 FALSE
#> 43 ES_mean -0.05246998 0.05 943 1 0.0029622239 FALSE
#> 44 ES_mean -0.02078698 0.05 944 1 -0.0079606363 FALSE
#> 45 ES_mean -0.03211513 0.05 945 1 0.0290050274 FALSE
#> 46 ES_mean -0.02411815 0.05 946 1 0.0009861917 FALSE
#> 47 ES_mean -0.02570387 0.05 947 1 0.0102032712 FALSE
#> 48 ES_mean -0.02198101 0.05 948 1 0.0239672051 FALSE
#> 49 ES_mean -0.01867714 0.05 949 1 0.0066334433 FALSE
#> 50 ES_mean -0.08972825 0.05 950 1 -0.0158926637 FALSE
#> 51 ES_mean -0.02820460 0.05 951 2 -0.0428770390 TRUE
#> 52 ES_mean -0.05405215 0.05 952 2 0.0017742398 FALSE
#> 53 ES_mean -0.03134242 0.05 953 2 -0.0377435814 TRUE
#> 54 ES_mean -0.01753723 0.05 954 2 0.0179789801 FALSE
#> 55 ES_mean -0.01815623 0.05 955 2 -0.0045997955 FALSE
#> 56 ES_mean -0.02319757 0.05 956 2 0.0050882039 FALSE
#> 57 ES_mean -0.01705137 0.05 957 2 0.2062438419 FALSE
#> 58 ES_mean -0.05289768 0.05 958 2 0.0290894526 FALSE
#> 59 ES_mean -0.05768324 0.05 959 2 0.0083306529 FALSE
#> 60 ES_mean -0.03701506 0.05 960 2 -0.0055718246 FALSE
#> 61 ES_mean -0.04675399 0.05 961 2 -0.0012467665 FALSE
#> 62 ES_mean -0.02561678 0.05 962 2 0.0482427451 FALSE
#> 63 ES_mean -0.03811243 0.05 963 2 0.0118178873 FALSE
#> 64 ES_mean -0.05271840 0.05 964 2 0.0126621081 FALSE
#> 65 ES_mean -0.09174228 0.05 965 2 0.0230449564 FALSE
#> 66 ES_mean -0.05700565 0.05 966 2 -0.0136554800 FALSE
#> 67 ES_mean -0.08416951 0.05 967 2 -0.0133549994 FALSE
#> 68 ES_mean -0.08171748 0.05 968 2 -0.0028859506 FALSE
#> 69 ES_mean -0.03203527 0.05 969 2 -0.0082197477 FALSE
#> 70 ES_mean -0.02316131 0.05 970 2 -0.0272196497 TRUE
#> 71 ES_mean -0.03431250 0.05 971 2 0.0325291332 FALSE
#> 72 ES_mean -0.05361914 0.05 972 2 -0.0251776577 FALSE
#> 73 ES_mean -0.05717397 0.05 973 2 -0.0048611406 FALSE
#> 74 ES_mean -0.03398768 0.05 974 2 0.0457490940 FALSE
#> 75 ES_mean -0.03939199 0.05 975 2 0.0264002730 FALSE
#> 76 ES_mean -0.04624017 0.05 976 2 0.0300177806 FALSE
#> 77 ES_mean -0.03287118 0.05 977 2 0.0161965898 FALSE
#> 78 ES_mean -0.05823796 0.05 978 2 -0.0142138901 FALSE
#> 79 ES_mean -0.10501685 0.05 979 2 -0.0733127141 FALSE
#> 80 ES_mean -0.03862044 0.05 980 2 0.0268845682 FALSE
#> 81 ES_mean -0.04268248 0.05 981 2 -0.0280441016 FALSE
#> 82 ES_mean -0.05399411 0.05 982 2 -0.0435108609 FALSE
#> 83 ES_mean -0.06319556 0.05 983 2 0.0122123253 FALSE
#> 84 ES_mean -0.03441865 0.05 984 2 0.0187544970 FALSE
#> 85 ES_mean -0.03526047 0.05 985 2 0.0205165005 FALSE
#> 86 ES_mean -0.05354182 0.05 986 2 0.0081176864 FALSE
#> 87 ES_mean -0.02339037 0.05 987 2 0.0291318680 FALSE
#> 88 ES_mean -0.08920903 0.05 988 2 -0.0095197782 FALSE
#> 89 ES_mean -0.09732615 0.05 989 2 0.0026235462 FALSE
#> 90 ES_mean -0.18264000 0.05 990 2 0.0165471020 FALSE
#> 91 ES_mean -0.01415583 0.05 991 2 0.0284908983 FALSE
#> 92 ES_mean -0.05580052 0.05 992 2 0.0357353231 FALSE
#> 93 ES_mean -0.02155877 0.05 993 2 -0.0194203462 FALSE
#> 94 ES_mean -0.03529106 0.05 994 2 -0.0147090394 FALSE
#> 95 ES_mean -0.02696400 0.05 995 2 0.0001064920 FALSE
#> 96 ES_mean -0.02730927 0.05 996 2 -0.0087683229 FALSE
#> 97 ES_mean -0.02436887 0.05 997 2 -0.0217268278 FALSE
#> 98 ES_mean -0.02125219 0.05 998 2 -0.0021598816 FALSE
#> 99 ES_mean -0.08957304 0.05 999 2 0.0048795256 FALSE
#> 100 ES_mean -0.02930573 0.05 1000 2 -0.0266539889 FALSE
risk_estimates(
risk_roll_cond,
risk_measures = "ES_mean",
alpha = 0.05, exceeded = TRUE,
cond_u = c("prior_resid", 0.5)
)
#> risk_measure risk_est alpha row_num GOOG cond_u
#> 1 ES_mean -0.0180482647 0.05 901 6.996830e-04 0.5
#> 2 ES_mean -0.0147439242 0.05 901 -2.757591e-03 prior_resid
#> 3 ES_mean -0.0160043933 0.05 902 6.496964e-04 0.5
#> 4 ES_mean -0.0281617699 0.05 902 -4.152955e-03 prior_resid
#> 5 ES_mean -0.0150526654 0.05 903 3.720456e-04 0.5
#> 6 ES_mean -0.0555518274 0.05 903 -2.015483e-02 prior_resid
#> 7 ES_mean -0.0101609966 0.05 904 8.844061e-04 0.5
#> 8 ES_mean -0.0042415930 0.05 904 8.204147e-03 prior_resid
#> 9 ES_mean -0.0034722846 0.05 905 9.861227e-04 0.5
#> 10 ES_mean -0.0056033661 0.05 905 9.014893e-03 prior_resid
#> 11 ES_mean -0.0072136010 0.05 906 7.823704e-04 0.5
#> 12 ES_mean -0.0249941630 0.05 906 -6.246060e-04 prior_resid
#> 13 ES_mean -0.0266902938 0.05 907 8.748416e-04 0.5
#> 14 ES_mean -0.0357758178 0.05 907 5.092075e-03 prior_resid
#> 15 ES_mean -0.0187264681 0.05 908 4.031562e-04 0.5
#> 16 ES_mean -0.1356154880 0.05 908 -2.058985e-02 prior_resid
#> 17 ES_mean -0.0344875643 0.05 909 7.042424e-04 0.5
#> 18 ES_mean -0.0174182457 0.05 909 8.373618e-08 prior_resid
#> 19 ES_mean -0.0260762539 0.05 910 6.545299e-04 0.5
#> 20 ES_mean -0.0207410466 0.05 910 -4.287226e-03 prior_resid
#> 21 ES_mean -0.0201921671 0.05 911 1.207662e-03 0.5
#> 22 ES_mean -0.0085715083 0.05 911 2.302470e-02 prior_resid
#> 23 ES_mean -0.0215618289 0.05 912 8.935230e-04 0.5
#> 24 ES_mean -0.0189021836 0.05 912 2.107924e-03 prior_resid
#> 25 ES_mean -0.0337139681 0.05 913 6.488192e-04 0.5
#> 26 ES_mean -0.0248922045 0.05 913 -6.231110e-03 prior_resid
#> 27 ES_mean -0.0077331722 0.05 914 6.160282e-04 0.5
#> 28 ES_mean -0.0253530277 0.05 914 -5.717352e-03 prior_resid
#> 29 ES_mean -0.0066226583 0.05 915 6.866722e-04 0.5
#> 30 ES_mean -0.0428447220 0.05 915 -2.082690e-03 prior_resid
#> 31 ES_mean -0.0257779826 0.05 916 9.309828e-04 0.5
#> 32 ES_mean -0.0172138094 0.05 916 8.342412e-03 prior_resid
#> 33 ES_mean -0.0108166937 0.05 917 9.854953e-04 0.5
#> 34 ES_mean -0.0026721969 0.05 917 8.994594e-03 prior_resid
#> 35 ES_mean -0.0209763803 0.05 918 1.029061e-03 0.5
#> 36 ES_mean -0.0133962478 0.05 918 1.061996e-02 prior_resid
#> 37 ES_mean -0.0067052409 0.05 919 7.488377e-04 0.5
#> 38 ES_mean -0.0452721510 0.05 919 -2.287261e-03 prior_resid
#> 39 ES_mean -0.0171274655 0.05 920 5.445785e-04 0.5
#> 40 ES_mean -0.0225841303 0.05 920 -9.998128e-03 prior_resid
#> 41 ES_mean -0.0244856309 0.05 921 7.066320e-04 0.5
#> 42 ES_mean -0.0108609694 0.05 921 -4.790284e-04 prior_resid
#> 43 ES_mean -0.0187403155 0.05 922 9.439261e-04 0.5
#> 44 ES_mean -0.1083388846 0.05 922 9.028625e-03 prior_resid
#> 45 ES_mean -0.0278874444 0.05 923 5.606415e-04 0.5
#> 46 ES_mean -0.0670353425 0.05 923 -1.095067e-02 prior_resid
#> 47 ES_mean -0.0046283968 0.05 924 7.875505e-04 0.5
#> 48 ES_mean -0.0084348573 0.05 924 2.925026e-03 prior_resid
#> 49 ES_mean -0.0128601101 0.05 925 8.280875e-04 0.5
#> 50 ES_mean -0.0105787832 0.05 925 3.177105e-03 prior_resid
#> 51 ES_mean -0.0217201010 0.05 926 8.319955e-04 0.5
#> 52 ES_mean -0.0006481027 0.05 926 3.173836e-03 prior_resid
#> 53 ES_mean -0.0488503744 0.05 927 5.271186e-04 0.5
#> 54 ES_mean -0.0447342686 0.05 927 -1.174745e-02 prior_resid
#> 55 ES_mean -0.0284889171 0.05 928 6.018082e-04 0.5
#> 56 ES_mean -0.0234654032 0.05 928 -5.058277e-03 prior_resid
#> 57 ES_mean -0.0114644053 0.05 929 6.001527e-04 0.5
#> 58 ES_mean -0.0419884078 0.05 929 -5.713073e-03 prior_resid
#> 59 ES_mean -0.0408070969 0.05 930 8.955109e-04 0.5
#> 60 ES_mean -0.0051209675 0.05 930 7.704057e-03 prior_resid
#> 61 ES_mean -0.0189377434 0.05 931 1.013548e-03 0.5
#> 62 ES_mean -0.0008550255 0.05 931 1.094392e-02 prior_resid
#> 63 ES_mean -0.0267808299 0.05 932 8.107030e-04 0.5
#> 64 ES_mean -0.0117045374 0.05 932 7.335099e-04 prior_resid
#> 65 ES_mean -0.0363727610 0.05 933 6.637248e-04 0.5
#> 66 ES_mean -0.0311653803 0.05 933 -4.284156e-03 prior_resid
#> 67 ES_mean -0.0277281772 0.05 934 5.557522e-04 0.5
#> 68 ES_mean -0.0293574302 0.05 934 -8.482179e-03 prior_resid
#> 69 ES_mean -0.0088010394 0.05 935 8.142603e-04 0.5
#> 70 ES_mean -0.0024224613 0.05 935 4.390451e-03 prior_resid
#> 71 ES_mean -0.0258364609 0.05 936 1.256606e-03 0.5
#> 72 ES_mean 0.0020177735 0.05 936 2.598793e-02 prior_resid
#> 73 ES_mean -0.0061874463 0.05 937 9.600635e-04 0.5
#> 74 ES_mean -0.0086882676 0.05 937 4.811866e-03 prior_resid
#> 75 ES_mean -0.0139367888 0.05 938 1.023025e-03 0.5
#> 76 ES_mean -0.0198790704 0.05 938 1.008598e-02 prior_resid
#> 77 ES_mean -0.0059272112 0.05 939 6.666450e-04 0.5
#> 78 ES_mean -0.0242258945 0.05 939 -6.301749e-03 prior_resid
#> 79 ES_mean -0.0189466316 0.05 940 8.518550e-04 0.5
#> 80 ES_mean -0.0093064113 0.05 940 4.769719e-03 prior_resid
#> 81 ES_mean -0.0143926868 0.05 941 6.292290e-04 0.5
#> 82 ES_mean -0.0334679358 0.05 941 -6.536229e-03 prior_resid
#> 83 ES_mean -0.0100686240 0.05 942 1.185231e-03 0.5
#> 84 ES_mean -0.0086190794 0.05 942 2.230214e-02 prior_resid
#> 85 ES_mean -0.0195489453 0.05 943 1.040997e-03 0.5
#> 86 ES_mean -0.0209929142 0.05 943 8.822328e-03 prior_resid
#> 87 ES_mean -0.0117160721 0.05 944 7.640722e-04 0.5
#> 88 ES_mean -0.0249871210 0.05 944 -2.065291e-03 prior_resid
#> 89 ES_mean -0.0217062277 0.05 945 6.611523e-04 0.5
#> 90 ES_mean -0.0236115133 0.05 945 -4.441767e-03 prior_resid
#> 91 ES_mean -0.0202874106 0.05 946 1.141445e-03 0.5
#> 92 ES_mean 0.0073428987 0.05 946 1.907970e-02 prior_resid
#> 93 ES_mean -0.0209606758 0.05 947 7.912832e-04 0.5
#> 94 ES_mean -0.0268454067 0.05 947 -1.655799e-03 prior_resid
#> 95 ES_mean -0.0109981794 0.05 948 8.074871e-04 0.5
#> 96 ES_mean -0.0195325908 0.05 948 1.836081e-03 prior_resid
#> 97 ES_mean -0.0081869167 0.05 949 8.160572e-04 0.5
#> 98 ES_mean -0.0153316174 0.05 949 2.287120e-03 prior_resid
#> 99 ES_mean -0.0237855303 0.05 950 7.650171e-04 0.5
#> 100 ES_mean -0.0415598128 0.05 950 1.540184e-04 prior_resid
#> 101 ES_mean -0.0102763091 0.05 951 8.066557e-04 0.5
#> 102 ES_mean -0.0144205036 0.05 951 6.499964e-04 prior_resid
#> 103 ES_mean -0.0100352031 0.05 952 4.679740e-04 0.5
#> 104 ES_mean -0.0262516106 0.05 952 -8.876000e-03 prior_resid
#> 105 ES_mean -0.0069841653 0.05 953 9.102856e-04 0.5
#> 106 ES_mean -0.0048744042 0.05 953 4.184574e-03 prior_resid
#> 107 ES_mean -0.0032404125 0.05 954 4.307925e-05 0.5
#> 108 ES_mean -0.0609469190 0.05 954 -2.383657e-02 prior_resid
#> 109 ES_mean -0.0037262896 0.05 955 8.384902e-04 0.5
#> 110 ES_mean -0.0129259455 0.05 955 2.897069e-03 prior_resid
#> 111 ES_mean -0.0216663768 0.05 956 8.973775e-04 0.5
#> 112 ES_mean -0.0236862964 0.05 956 2.879664e-03 prior_resid
#> 113 ES_mean -0.0105196246 0.05 957 7.609264e-04 0.5
#> 114 ES_mean -0.0386605499 0.05 957 -8.874099e-04 prior_resid
#> 115 ES_mean -0.0334862609 0.05 958 2.585922e-03 0.5
#> 116 ES_mean 0.0152144107 0.05 958 7.364423e-02 prior_resid
#> 117 ES_mean -0.0273391359 0.05 959 8.241888e-04 0.5
#> 118 ES_mean -0.0242348078 0.05 959 -3.719939e-03 prior_resid
#> 119 ES_mean -0.0188317423 0.05 960 8.057820e-04 0.5
#> 120 ES_mean -0.0280126351 0.05 960 -4.333052e-04 prior_resid
#> 121 ES_mean -0.0236465652 0.05 961 1.143842e-03 0.5
#> 122 ES_mean -0.0142544136 0.05 961 8.815115e-03 prior_resid
#> 123 ES_mean -0.0379054061 0.05 962 8.326733e-04 0.5
#> 124 ES_mean -0.0165234903 0.05 962 -1.955699e-04 prior_resid
#> 125 ES_mean -0.0065032822 0.05 963 1.064068e-03 0.5
#> 126 ES_mean -0.0077043602 0.05 963 6.773789e-03 prior_resid
#> 127 ES_mean -0.0074923782 0.05 964 5.816891e-04 0.5
#> 128 ES_mean -0.0567110118 0.05 964 -6.704181e-03 prior_resid
#> 129 ES_mean -0.0269261728 0.05 965 1.067387e-03 0.5
#> 130 ES_mean -0.0177663433 0.05 965 7.545544e-03 prior_resid
#> 131 ES_mean -0.0114296171 0.05 966 1.050961e-03 0.5
#> 132 ES_mean -0.0057364314 0.05 966 6.137076e-03 prior_resid
#> 133 ES_mean -0.0221632046 0.05 967 5.036534e-04 0.5
#> 134 ES_mean -0.0453840221 0.05 967 -8.731011e-03 prior_resid
#> 135 ES_mean -0.0074617716 0.05 968 6.715439e-04 0.5
#> 136 ES_mean -0.0475371409 0.05 968 -2.828705e-03 prior_resid
#> 137 ES_mean -0.0175429730 0.05 969 7.074412e-04 0.5
#> 138 ES_mean -0.0105520463 0.05 969 -2.140914e-03 prior_resid
#> 139 ES_mean -0.0258840217 0.05 970 7.998610e-04 0.5
#> 140 ES_mean -0.0107678071 0.05 970 3.495728e-04 prior_resid
#> 141 ES_mean -0.0209463229 0.05 971 6.070481e-04 0.5
#> 142 ES_mean -0.1451092588 0.05 971 -5.057103e-03 prior_resid
#> 143 ES_mean -0.0267943285 0.05 972 1.207581e-03 0.5
#> 144 ES_mean -0.0215237884 0.05 972 1.195502e-02 prior_resid
#> 145 ES_mean -0.0070007051 0.05 973 3.250650e-04 0.5
#> 146 ES_mean -0.0458486071 0.05 973 -1.436926e-02 prior_resid
#> 147 ES_mean -0.0124043806 0.05 974 7.470192e-04 0.5
#> 148 ES_mean -0.0144784291 0.05 974 -2.460918e-04 prior_resid
#> 149 ES_mean -0.0216709581 0.05 975 1.381758e-03 0.5
#> 150 ES_mean 0.0133254188 0.05 975 1.686522e-02 prior_resid
#> 151 ES_mean -0.0441307507 0.05 976 8.722693e-04 0.5
#> 152 ES_mean -0.0178152620 0.05 976 9.093661e-04 prior_resid
#> 153 ES_mean -0.0276060110 0.05 977 9.625718e-04 0.5
#> 154 ES_mean -0.0086784668 0.05 977 4.483286e-03 prior_resid
#> 155 ES_mean -0.0105756031 0.05 978 1.286182e-03 0.5
#> 156 ES_mean -0.0135137938 0.05 978 1.350357e-02 prior_resid
#> 157 ES_mean -0.0389105918 0.05 979 5.714532e-04 0.5
#> 158 ES_mean -0.0290365493 0.05 979 -7.104754e-03 prior_resid
#> 159 ES_mean -0.0204122755 0.05 980 -1.344821e-04 0.5
#> 160 ES_mean -0.0747705976 0.05 980 -2.935625e-02 prior_resid
#> 161 ES_mean -0.0240982725 0.05 981 7.528673e-04 0.5
#> 162 ES_mean -0.0098876525 0.05 981 6.461406e-04 prior_resid
#> 163 ES_mean -0.0352434364 0.05 982 3.867369e-04 0.5
#> 164 ES_mean -0.0466299965 0.05 982 -1.145012e-02 prior_resid
#> 165 ES_mean -0.0264262099 0.05 983 3.571106e-04 0.5
#> 166 ES_mean -0.0349413700 0.05 983 -1.148644e-02 prior_resid
#> 167 ES_mean -0.0089266105 0.05 984 1.022904e-03 0.5
#> 168 ES_mean 0.0006474839 0.05 984 6.990405e-03 prior_resid
#> 169 ES_mean -0.0249039280 0.05 985 1.299834e-03 0.5
#> 170 ES_mean 0.0005141923 0.05 985 1.342533e-02 prior_resid
#> 171 ES_mean -0.0061227099 0.05 986 1.280696e-03 0.5
#> 172 ES_mean -0.0005263463 0.05 986 1.222080e-02 prior_resid
#> 173 ES_mean -0.0137682532 0.05 987 1.040625e-03 0.5
#> 174 ES_mean -0.0209069829 0.05 987 5.563497e-03 prior_resid
#> 175 ES_mean -0.0053440312 0.05 988 9.520902e-04 0.5
#> 176 ES_mean -0.0078847687 0.05 988 3.731350e-03 prior_resid
#> 177 ES_mean -0.0187755949 0.05 989 7.776491e-04 0.5
#> 178 ES_mean -0.0163126281 0.05 989 -7.270245e-04 prior_resid
#> 179 ES_mean -0.0137803333 0.05 990 7.946389e-04 0.5
#> 180 ES_mean -0.0210755459 0.05 990 1.596895e-04 prior_resid
#> 181 ES_mean -0.0093988566 0.05 991 1.095637e-03 0.5
#> 182 ES_mean -0.0108880924 0.05 991 8.406134e-03 prior_resid
#> 183 ES_mean -0.0182852402 0.05 992 1.337768e-03 0.5
#> 184 ES_mean -0.0128645745 0.05 992 1.483391e-02 prior_resid
#> 185 ES_mean -0.0107694931 0.05 993 1.268812e-03 0.5
#> 186 ES_mean -0.0069571723 0.05 993 1.210468e-02 prior_resid
#> 187 ES_mean -0.0212550718 0.05 994 5.873287e-04 0.5
#> 188 ES_mean -0.0282778585 0.05 994 -6.607571e-03 prior_resid
#> 189 ES_mean -0.0202663819 0.05 995 5.807587e-04 0.5
#> 190 ES_mean -0.0137904064 0.05 995 -5.274630e-03 prior_resid
#> 191 ES_mean -0.0190500167 0.05 996 7.325688e-04 0.5
#> 192 ES_mean -0.0231945518 0.05 996 -1.042817e-03 prior_resid
#> 193 ES_mean -0.0109141812 0.05 997 6.602126e-04 0.5
#> 194 ES_mean -0.0269940321 0.05 997 -3.297956e-03 prior_resid
#> 195 ES_mean -0.0078487886 0.05 998 6.596963e-04 0.5
#> 196 ES_mean -0.0257047085 0.05 998 -3.120260e-03 prior_resid
#> 197 ES_mean -0.0232479493 0.05 999 5.171659e-04 0.5
#> 198 ES_mean -0.0569212308 0.05 999 -7.117465e-03 prior_resid
#> 199 ES_mean -0.0149730683 0.05 1000 7.270554e-04 0.5
#> 200 ES_mean -0.0334356704 0.05 1000 -9.259374e-04 prior_resid
#> vine_window realized exceeded
#> 1 1 -0.0018385881 FALSE
#> 2 1 -0.0018385881 FALSE
#> 3 1 -0.0621711113 TRUE
#> 4 1 -0.0621711113 TRUE
#> 5 1 0.0253128550 FALSE
#> 6 1 0.0253128550 FALSE
#> 7 1 0.0306299881 FALSE
#> 8 1 0.0306299881 FALSE
#> 9 1 0.0103185990 FALSE
#> 10 1 0.0103185990 FALSE
#> 11 1 -0.0086813462 TRUE
#> 12 1 -0.0086813462 FALSE
#> 13 1 -0.0375519981 TRUE
#> 14 1 -0.0375519981 TRUE
#> 15 1 -0.0044717387 FALSE
#> 16 1 -0.0044717387 FALSE
#> 17 1 -0.0072620125 FALSE
#> 18 1 -0.0072620125 FALSE
#> 19 1 0.0303912928 FALSE
#> 20 1 0.0303912928 FALSE
#> 21 1 -0.0079964110 FALSE
#> 22 1 -0.0079964110 FALSE
#> 23 1 -0.0102580352 FALSE
#> 24 1 -0.0102580352 FALSE
#> 25 1 -0.0038800605 FALSE
#> 26 1 -0.0038800605 FALSE
#> 27 1 0.0108246338 FALSE
#> 28 1 0.0108246338 FALSE
#> 29 1 0.0173413876 FALSE
#> 30 1 0.0173413876 FALSE
#> 31 1 0.0167791727 FALSE
#> 32 1 0.0167791727 FALSE
#> 33 1 0.0173273738 FALSE
#> 34 1 0.0173273738 FALSE
#> 35 1 -0.0020944877 FALSE
#> 36 1 -0.0020944877 FALSE
#> 37 1 -0.0254386287 TRUE
#> 38 1 -0.0254386287 FALSE
#> 39 1 0.0015681758 FALSE
#> 40 1 0.0015681758 FALSE
#> 41 1 0.0079634319 FALSE
#> 42 1 0.0079634319 FALSE
#> 43 1 -0.0303947182 TRUE
#> 44 1 -0.0303947182 FALSE
#> 45 1 0.0303391289 FALSE
#> 46 1 0.0303391289 FALSE
#> 47 1 0.0007422696 FALSE
#> 48 1 0.0007422696 FALSE
#> 49 1 0.0096228399 FALSE
#> 50 1 0.0096228399 FALSE
#> 51 1 -0.0160386269 FALSE
#> 52 1 -0.0160386269 TRUE
#> 53 1 0.0045803703 FALSE
#> 54 1 0.0045803703 FALSE
#> 55 1 -0.0204478551 FALSE
#> 56 1 -0.0204478551 FALSE
#> 57 1 -0.0040765962 FALSE
#> 58 1 -0.0040765962 FALSE
#> 59 1 -0.0134517514 FALSE
#> 60 1 -0.0134517514 TRUE
#> 61 1 -0.0261555208 TRUE
#> 62 1 -0.0261555208 TRUE
#> 63 1 -0.0197764673 FALSE
#> 64 1 -0.0197764673 TRUE
#> 65 1 -0.0250208924 FALSE
#> 66 1 -0.0250208924 FALSE
#> 67 1 0.0157902093 FALSE
#> 68 1 0.0157902093 FALSE
#> 69 1 0.0200804051 FALSE
#> 70 1 0.0200804051 FALSE
#> 71 1 -0.0003797436 FALSE
#> 72 1 -0.0003797436 TRUE
#> 73 1 0.0106274406 FALSE
#> 74 1 0.0106274406 FALSE
#> 75 1 -0.0042627866 FALSE
#> 76 1 -0.0042627866 FALSE
#> 77 1 0.0021652375 FALSE
#> 78 1 0.0021652375 FALSE
#> 79 1 0.0021920993 FALSE
#> 80 1 0.0021920993 FALSE
#> 81 1 0.0281930384 FALSE
#> 82 1 0.0281930384 FALSE
#> 83 1 0.0082817798 FALSE
#> 84 1 0.0082817798 FALSE
#> 85 1 0.0048948638 FALSE
#> 86 1 0.0048948638 FALSE
#> 87 1 -0.0034468575 FALSE
#> 88 1 -0.0034468575 FALSE
#> 89 1 0.0120308204 FALSE
#> 90 1 0.0120308204 FALSE
#> 91 1 0.0024226366 FALSE
#> 92 1 0.0024226366 TRUE
#> 93 1 0.0083322551 FALSE
#> 94 1 0.0083322551 FALSE
#> 95 1 0.0216257493 FALSE
#> 96 1 0.0216257493 FALSE
#> 97 1 0.0064520152 FALSE
#> 98 1 0.0064520152 FALSE
#> 99 1 -0.0165274326 FALSE
#> 100 1 -0.0165274326 FALSE
#> 101 2 -0.0344208564 TRUE
#> 102 2 -0.0344208564 TRUE
#> 103 2 -0.0020277570 FALSE
#> 104 2 -0.0020277570 FALSE
#> 105 2 -0.0175553306 TRUE
#> 106 2 -0.0175553306 TRUE
#> 107 2 0.0158232528 FALSE
#> 108 2 0.0158232528 FALSE
#> 109 2 -0.0074703999 TRUE
#> 110 2 -0.0074703999 FALSE
#> 111 2 0.0058796351 FALSE
#> 112 2 0.0058796351 FALSE
#> 113 2 0.1593336266 FALSE
#> 114 2 0.1593336266 FALSE
#> 115 2 0.0312108993 FALSE
#> 116 2 0.0312108993 FALSE
#> 117 2 0.0087928238 FALSE
#> 118 2 0.0087928238 FALSE
#> 119 2 -0.0142490363 FALSE
#> 120 2 -0.0142490363 FALSE
#> 121 2 -0.0013247312 FALSE
#> 122 2 -0.0013247312 FALSE
#> 123 2 0.0415373526 FALSE
#> 124 2 0.0415373526 FALSE
#> 125 2 0.0182112437 FALSE
#> 126 2 0.0182112437 FALSE
#> 127 2 0.0054458532 FALSE
#> 128 2 0.0054458532 FALSE
#> 129 2 0.0167550619 FALSE
#> 130 2 0.0167550619 FALSE
#> 131 2 -0.0053603964 FALSE
#> 132 2 -0.0053603964 FALSE
#> 133 2 -0.0102568397 FALSE
#> 134 2 -0.0102568397 FALSE
#> 135 2 -0.0006267974 FALSE
#> 136 2 -0.0006267974 FALSE
#> 137 2 -0.0084634421 FALSE
#> 138 2 -0.0084634421 FALSE
#> 139 2 -0.0222462629 FALSE
#> 140 2 -0.0222462629 TRUE
#> 141 2 0.0212404474 FALSE
#> 142 2 0.0212404474 FALSE
#> 143 2 -0.0121047106 FALSE
#> 144 2 -0.0121047106 FALSE
#> 145 2 -0.0041641762 FALSE
#> 146 2 -0.0041641762 FALSE
#> 147 2 0.0300536863 FALSE
#> 148 2 0.0300536863 FALSE
#> 149 2 0.0249803196 FALSE
#> 150 2 0.0249803196 FALSE
#> 151 2 0.0255392108 FALSE
#> 152 2 0.0255392108 FALSE
#> 153 2 0.0032120208 FALSE
#> 154 2 0.0032120208 FALSE
#> 155 2 -0.0077427385 FALSE
#> 156 2 -0.0077427385 FALSE
#> 157 2 -0.0484209603 TRUE
#> 158 2 -0.0484209603 TRUE
#> 159 2 0.0271292980 FALSE
#> 160 2 0.0271292980 FALSE
#> 161 2 -0.0169787191 FALSE
#> 162 2 -0.0169787191 TRUE
#> 163 2 -0.0320713652 FALSE
#> 164 2 -0.0320713652 FALSE
#> 165 2 0.0057546384 FALSE
#> 166 2 0.0057546384 FALSE
#> 167 2 0.0056781706 FALSE
#> 168 2 0.0056781706 FALSE
#> 169 2 0.0082682749 FALSE
#> 170 2 0.0082682749 FALSE
#> 171 2 0.0021988549 FALSE
#> 172 2 0.0021988549 FALSE
#> 173 2 0.0252342359 FALSE
#> 174 2 0.0252342359 FALSE
#> 175 2 -0.0089240806 TRUE
#> 176 2 -0.0089240806 TRUE
#> 177 2 0.0024986069 FALSE
#> 178 2 0.0024986069 FALSE
#> 179 2 0.0083738319 FALSE
#> 180 2 0.0083738319 FALSE
#> 181 2 0.0142573427 FALSE
#> 182 2 0.0142573427 FALSE
#> 183 2 0.0236398197 FALSE
#> 184 2 0.0236398197 FALSE
#> 185 2 -0.0134049629 TRUE
#> 186 2 -0.0134049629 TRUE
#> 187 2 -0.0093428320 FALSE
#> 188 2 -0.0093428320 FALSE
#> 189 2 0.0013467049 FALSE
#> 190 2 0.0013467049 FALSE
#> 191 2 -0.0054628367 FALSE
#> 192 2 -0.0054628367 FALSE
#> 193 2 -0.0185334113 TRUE
#> 194 2 -0.0185334113 FALSE
#> 195 2 0.0048388271 FALSE
#> 196 2 0.0048388271 FALSE
#> 197 2 0.0060523258 FALSE
#> 198 2 0.0060523258 FALSE
#> 199 2 -0.0249925345 TRUE
#> 200 2 -0.0249925345 FALSE
# }