The scope of this paper is twofold. We first describe the tail behavior for general AR-GARCH processes and hence extend the results of Basrak, Davis, and Mikosch (2002b) to another empirical relevant ...

ABSTRACT This paper investigates the estimation of a 10-day value-at-risk (VaR) based on a data set of 250 daily values. The commonly used square-rootof-time rule, which scales the one-day 99% VaR ...

Time varying correlations are often estimated with multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models that are linear in squares and cross products of the data. A ...

For more information on this research see: Age-specific copula-AR-GARCH mortality models. Insurance Mathematics & Economics, 2015;61 ():110-124.

Generalized Autoregressive Conditional Heteroskedasticity (GARCH) The generalized autoregressive conditional heteroskedasticity (GARCH) model is a statistical tool used to analyze time-series data ...

The AR (1)-GARCH (1,1) model with t -distribution is used as a benchmark. Regulator and firm loss functions are used to select the best volatility model. Two tests of causality in risk are used in our ...

We also propose the copula-S-RMDNGARCH, which extends the current recurrent mixture density network architecture to multivariate settings. We compare the value-at-risk forecast obtained with the ...

What Is the GARCH Process? The generalized autoregressive conditional heteroskedasticity (GARCH) process is an econometric term developed in 1982 by Robert F. Engle, an economist and 2003 winner ...