Package Demo

Alexios Galanos

2026-05-22

Estimation

We use the Deutsche Mark British Pound dataset for this demonstration throughout the document.

Code 
library(tsgarch)
suppressMessages(library(data.table))
suppressMessages(library(xts))
data(dmbp)
dmbp <- xts(dmbp, as.Date(seq_len(nrow(dmbp)), origin = '1970-01-01'))
spec <- garch_modelspec(dmbp[1:1500,1], model = 'fgarch', constant = TRUE, 
                        init = 'unconditional', distribution = 'jsu')
mod <- estimate(spec)
as_flextable(summary(mod))
FGARCH Model Summary

Estimate

Std. Error

t value

Pr(>|t|)

μ\mu

-0.0154

0.0099

-1.5574

0.1194

ω\omega

0.0050

0.0032

1.5543

0.1201

α1\alpha_1

0.1621

0.0373

4.3419

0.0000

***

γ1\gamma_1

-0.3522

0.1108

-3.1776

0.0015

**

η1\eta_1

0.7829

0.1594

4.9108

0.0000

***

β1\beta_1

0.8727

0.0278

31.4083

0.0000

***

δ\delta

1.4659

0.3593

4.0799

0.0000

***

ζ\zeta

-0.2595

0.0835

-3.1076

0.0019

**

ν\nu

1.4762

0.1046

14.1174

0.0000

***

PP

0.9903

0.0104

95.0454

0.0000

***

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

variance targeting: FALSE

initialization value: 0.2405

LogLik: -829.1715

AIC: 1678 | BIC: 1731

Model Equation

zt=εtσt∼JSU(0,1,ζ,ν)z_t = \frac{\varepsilon_t}{\sigma_t} \sim JSU\left(0,1,\zeta, \nu\right)

σtδ=ω+α1σt−jδ(∣zt−1−η1∣−γ1(zt−1−η1))δ+β1σt−1δ \sigma^{\delta}_t = \omega + \alpha_1 \sigma^{\delta}_{t-j}\left(\left|z_{t-1} - \eta_1\right| - \gamma_1 \left(z_{t-1} - \eta_1\right)\right)^{\delta} + \beta_1 \sigma^{\delta}_{t-1}

Persistence (P) and Unconditional Variance Equations

P=∑j=1pβj+∑j=1qαjκj,κj=E[(∣zt−j−ηj∣−γj(zt−j−ηj))δ]P = \sum_{j=1}^p \beta_j + \sum_{j=1}^q \alpha_j \kappa_j,\quad \kappa_j = E\left[\left(\left|z_{t-j} - \eta_j\right| - \gamma_j \left(z_{t-j} - \eta_j\right)\right)^{\delta}\right]

E[εt2]=(ω1−P)2/δE\left[\varepsilon^2_t\right] = \left(\frac{\omega}{1 - P}\right)^{2/\delta}

Notice that the as_flextable method provides a publication-ready option for printing out the model summary.

We next take a look at a summary plot of the estimated model:

Code 
oldpar <- par(mfrow = c(1,1))
par(mar = c(2,2,2,2))
plot(mod)

Code 
par(oldpar)

Notice the news impact curve which is both shifted and rotated, a particularly appealing feature of the Family GARCH model.

Simulation and Prediction

The code below shows how to predict from an estimated model and how to simulate using a specification with fixed parameters.

Code 
delta <- coef(mod)["delta"]
new_spec <- spec
new_spec$parmatrix <- copy(mod$parmatrix)
sim <- simulate(new_spec, nsim = 100, h = 1000, seed = 100, burn = 250)
mean_sim <- mean(rowMeans(sim$sigma^delta))^(2/delta)
pred <- predict(mod, h = 1000, nsim = 0)
oldpar <- par(mfrow = c(1,1))
par(mar = c(2,2,2,2))
plot(as.numeric(pred$sigma^2), type = "l", xlab = "horizon", 
     ylab = expression(sigma^2), ylim = c(0.25, 0.41), main = "Family GARCH - JSU Prediction")
abline(h = as.numeric(unconditional(mod)), col = 2)
abline(h = mean_sim, col = 3)
legend("bottomright", c("h-step prediction","unconditional variance","simulated unconditional variance"), col = c(1,2,3), lty = 1, bty = "n")

Code 
par(oldpar)

Conclusion

There are other methods available, including methods for profiling the parameter distribution (tsprofile) and backtesting (tsbacktest), as well as methods for extracting information from the estimated object such as vcov, pit (probability integral transform), and confint.

The package includes focused unit tests for core behavior. Additional testing helpers and diagnostics are available in the tstests package.