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 ...

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

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 ...

GARCH参数的估计是通过最大化对数似然函数的值来实现的。 在此基础上，我们可以使用时间变化的均值和方差；因此，最大化对称正态GARCH似然函数 ...

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 ...

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 ...

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

ABSTRACT In Duan, Gauthier and Simonato (1999), an analytical approximation to price European options in the generalized autoregressive conditional heteroskedastic (GARCH) framework was developed. The ...