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Results tagged with garch
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user 76419
Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used for time series in which the conditional variance is time-varying and autocorrelated. The conditional variance is a linear combination of lagged conditional variances and lagged squared errors. The conditional variance equation in GARCH models is deterministic, in contrast to Stochastic Volatility (SV) models.
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How to compute standardized residuals in GARCH-MIDAS model?
I'm trying to compute the standardized residuals in GARCH-MIDAS model but I think that the calculation is not similar to GARCH standard models in R. …
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How can I apply the GARCH-MIDAS model to the FTSE MIB using the CPU as an explanatory variable?
This rise the computation, but could make your model better than the univariate GJR-GARCH model without MIDAS variable when you compute VaR and ES, I think. …