Description Usage Arguments Details Value Author(s) References Examples
View source: R/RobGARCHBootParallel.R
Robust GARCH (Generalized Autoregressive Conditional Heteroskedastic) Bootstrap procedure of Trucíos et al. (2017)
1 | RobGARCHBootParallel(data, n.boot = 1000, n.ahead = 1, ncl = 2)
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data |
Vector of time series returns. |
n.boot |
Number of bootsrap replications. By default n.boot = 1000 |
n.ahead |
Numbers of steps-ahead. By default n.ahead = 1 |
ncl |
Numbers of parallel processes. By default ncl = 2 |
More details can be found in Trucíos et at. (2017), Hotta and Trucíos (2018), and Trucíos (2019).
The function returns two lists with the empirical H-steps-ahead bootstrap densities for returns and squared volatilities.
Carlos Trucíos
Hotta, Luiz Koodi, and Carlos Trucíos. Inference in (M)GARCH models in the presence of additive outliers: Specification, estimation, and prediction. Advances in Mathematics and Applications. Springer, Cham, 2018. 179-202.
Trucíos, Carlos, Luiz K. Hotta, and Esther Ruiz. Robust bootstrap forecast densities for GARCH returns and volatilities. Journal of Statistical Computation and Simulation 87.16 (2017): 3152-3174.
Trucíos, Carlos. Forecasting Bitcoin risk measures: A robust approach. International Journal of Forecasting 35.3 (2019): 836-847.
1 2 3 4 5 6 7 8 9 10 | # Robust bootstrap forecast densities for returns and volatilities
boot = RobGARCHBootParallel(returnsexample, n.boot = 1000, n.ahead = 1)
# Obtaining the forecast intervals for returns (95%)
quantile(boot[[1]], prob = c(0.025, 0.975))
# Obtaining the forecast intervals for volatilities (95%)
quantile(boot[[2]], prob = c(0.025, 0.975))
# Risk measures can also be obtained
VaR1 = quantile(boot[[1]], prob = 0.01)
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