Although volatility clustering has a long history as a salient empirical regularity
characterizing high-frequency speculative prices, it was not until recently that applied …

The aim of this survey paper is to provide an account of some of the important developments
in the autoregressive conditional heteroskedasticity (ARCH) model since its inception in a …

A class of nonlinear ARCH models is suggested. The proposed class encompasses several
functional forms for ARCH which have been put forth in the literature. A Lagrange multiplier …

We evaluate the performance of an extensive family of ARCH models in modeling the daily
Value-at-Risk (VaR) of perfectly diversified portfolios in five stock indices, using a number of …

A density forecast of the realization of a random variable at some future time is an estimate
of the probability distribution of the possible future values of that variable. This article …

In many applications, it has been found that the autoregressive conditional
heteroskedasticity (ARCH) model under the conditional normal or Student'st distributions are …

This paper provides a review of some recent theoretical results for time series models with
GARCH errors, and is directed towards practitioners. Starting with the simple ARCH model …

Autoregressive Conditional Heteroskedastic (ARCH) processes are used in finance to
model asset price volatility over time. This book introduces both the theory and applications …

Volatility forecasting is a major area in the pricing of derivative securities, such as stock and
index options. In this paper, we compare three methods of forecasting volatility. These are …

This paper examines the stock returns volatility in the Tokyo Stock Exchange in the period
1986 through 1989. Structures of returns volatility are estimated and forecasted. Models of …