ARCH Models
Abstract
This chapter evaluates the most important theoretical developments in ARCH type modeling
of time-varying conditional variances. The coverage include the specification of univerate
parametric ARCH models, general inference procedures, conditions for stationarity
and ergodicity, continuous time methods, aggregation and forecasting of ARCH models,
multivariate conditional covariance formulations, and the use of model selection criteria
in an ARCH context. Additionally, the chapter contains a discussion of the empirical
regularities pertaining to the temporal variation in financial market volatility.
Motivated in part by recent results on optimal filtering, a new conditional variance
model for better characterizing stock return volatility is also presented.
Type
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https://hdl.handle.net/10161/2551Collections
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Tim Bollerslev
Juanita and Clifton Kreps Distinguished Professor of Economics, in Trinity College
of Arts and Sciences
Professor Bollerslev conducts research in the areas of time-series econometrics, financial
econometrics, and empirical asset pricing finance. He is particularly well known
for his developments of econometric models and procedures for analyzing and forecasting
financial market volatility. Much of Bollerslev’s recent research has focused on
the analysis of newly available high-frequency intraday, or tick-by-tick, financial
data and so-called realized volatility measures, macroeconomic news annou

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