Access the full text.
Sign up today, get DeepDyve free for 14 days.
T. Andersen, T. Bollerslev (1998)
Deutsche Mark–Dollar Volatility: Intraday Activity Patterns, Macroeconomic Announcements, and Longer Run DependenciesJournal of Finance, 53
D. Nicholls, B. Quinn (1982)
Random Coefficient Autoregressive Models: An Introduction
A. Thavaneswaran, S. Peiris (1998)
Hypothesis testing for some time-series models : A power comparisonStatistics & Probability Letters, 38
I.V. Baswa
Generalized score tests for composite hypotheses
R. Engle (1982)
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflationEconometrica, 50
A. Thavaneswaran, S. Appadoo, M. Samanta (2005)
Random coefficient GARCH modelsMath. Comput. Model., 41
R. Tsay (2005)
Analysis of Financial Time SeriesTechnometrics, 48
V. Godambe (1985)
The foundations of finite sample estimation in stochastic processesBiometrika, 72
R. Engle, Gloria González-Rivera (1991)
Semiparametric ARCH ModelsJournal of Business & Economic Statistics, 9
R.F. Engle
Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation
T. Bollerslev (1986)
Generalized autoregressive conditional heteroskedasticityJournal of Econometrics, 31
C. Heyde (1998)
Quasi-likelihood and its application : a general approach to optimal parameter estimationJournal of the American Statistical Association, 93
Purpose – Financial returns are often modeled as stationary time series with innovations having heteroscedastic conditional variances. This paper seeks to derive the kurtosis of stationary processes with GARCH errors. The problem of hypothesis testing for stationary ARMA( p , q ) processes with GARCH errors is studied. Forecasting of ARMA( p , q ) processes with GARCH errors is also discussed in some detail. Design/methodology/approach – Estimating‐function methodology was the principal method used for the research. The results were also illustrated using examples and simulation studies. Volatility modeling is the subject of the paper. Findings – The kurtosis of stationary processes with GARCH errors is derived in terms of the model parameters ( ψ ), Ψ‐weights, and the kurtosis of the innovation process. Hypothesis testing for stationary ARMA( p , q ) processes with GARCH errors based on the estimating‐function approach is shown to be superior to the least‐squares approach. The fourth moment of the l ‐steps‐ahead forecast error is related to the model parameters and the kurtosis of the innovation process. Originality/value – This paper will be of value to econometricians and to anyone with an interest in the statistical properties of volatility modeling.
The Journal of Risk Finance – Emerald Publishing
Published: Oct 1, 2006
Keywords: Volatility; Forecasting; Estimation
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.