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Forecasting Models in Financial Time Series
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Journal Article
Forecasting Models in Financial Time Series
2020
Overview
A number of models have been developed that are especially suited to estimate the conditional volatility of financial series, of which the most widely used are the conditional Heteroscedatic models. The first of these was the Autoregressive Conditional Heteroscedasticity (ARCH) model proposed by Engle (1982) and generalized to GARCH (Generalized ARCH) by Bollerslev (1986) and Taylor (1986). The main objective of this paper is to review published work on ARCH and GARCH models and some of their extensions, in addition to reference to some of the probability distributions that are usually assumed with these models.
Publisher
جامعة الزعيم الأزهري
