Sentence examples for model volatility from inspiring English sources

Accordingly, beginning with Engle's (1982) autoregressive conditional heteroscedasticity (ARCH) model, several models have already been developed to model volatility.

Therefore, it would be interesting to model volatility clustering with such models while imposing restrictions on the persistence parameters.

The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, designed to model volatility clustering, exhibits heavy-tailedness regardless of the distribution of its innovation term.

To specify the variance equation to model volatility presence in differenced logarithmic exchange rates (i.e., exchange rate returns), we used five different GARCH family models, each of which has a different purpose.

Engle (1982) pioneered modeling volatility using conditionally heteroscedastic regression with the autoregressive conditional heteroscedasticity (ARCH) model.

GARCH models can provide a systematic framework for modelling volatility of time series [14, 15].

Consequently the traditional volatility models which do not take into consideration the regime switching characteristics of the factors such as oil shocks became obsolete in modeling volatility in petrol prices.

From a frequentist perspective the study of time series with structural changes has been of far reaching in econometric theory for univariate time series, frequentist dynamic models, volatility and even financial return models.

Thus, it is established that the differenced logarithmic exchange rate of the taka against the US dollar is conditionally heteroscedastic, and this remains crucially important for modeling volatility clustering.

We model the volatility in the error term in our MS-AR model as a Garch (1,1).

In particular, this study tried to model the volatility dynamics of the taka US dollar exchange rate return using GARCH, APARCH, EGARCH, TGARCH, and IGARCH models.