RobGARCHBoot (version 1.2.0)

RobGARCHBootParallel: Parallel implementation of the Robust GARCH bootstrap procedure

Description

Robust GARCH (Generalized Autoregressive Conditional Heteroskedastic) Bootstrap procedure of Truc<U+00ED>os et al. (2017)

Usage

RobGARCHBootParallel(data, n.boot = 1000, n.ahead = 1, ncl = 2)

Arguments

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

Value

The function returns two lists with the empirical H-steps-ahead bootstrap densities for returns and squared volatilities.

Details

More details can be found in Truc<U+00ED>os et at. (2017), Hotta and Truc<U+00ED>os (2018), and Truc<U+00ED>os (2019).

References

Hotta, Luiz Koodi, and Carlos Truc<U+00ED>os. 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.

Truc<U+00ED>os, 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.

Truc<U+00ED>os, Carlos. Forecasting Bitcoin risk measures: A robust approach. International Journal of Forecasting 35.3 (2019): 836-847.

Examples

# NOT RUN {
# 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)
# }