Access the full text.
Sign up today, get DeepDyve free for 14 days.
P. Hall, Q. Yao (2003)
Inference in ARCH and GARCH models with heavy-tailed errorsEconometrica, 71
Ana Bianco, M. Ben, V. Yohai (2005)
Robust estimation for linear regression with asymmetric errorsCanadian Journal of Statistics, 33
L. Peng, Q. Yao (2003)
Least absolute deviations estimation for ARCH and GARCH modelsBiometrika, 90
(1985)
Advanced Econometrics
K. Mukherjee (2008)
M-ESTIMATION IN GARCH MODELSEconometric Theory, 24
Zhou Zhou, Tuesday May, Eckhart Hall (2005)
“A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High-Frequency Data”
T. Andersen, T. Bollerslev, F. Diebold, Paul Labys (2001)
Modeling and Forecasting Realized VolatilityCapital Markets: Asset Pricing & Valuation eJournal
I. Berkes, Lajos 'ath, P. Kokoszka (2003)
GARCH processes: structure and estimationBernoulli, 9
D. Straumann, T. Mikosch (2006)
Quasi-maximum-likelihood estimation in conditionally heteroscedastic time series: A stochastic recurrence equations approachAnnals of Statistics, 34
N. Muler, V. Yohai (2008)
Robust estimates for GARCH modelsJournal of Statistical Planning and Inference, 138
Paul Kupiec (1995)
Techniques for Verifying the Accuracy of Risk Measurement Models, 3
C. Francq, J. Zakoian (2004)
Maximum likelihood estimation of pure GARCH and ARMA-GARCH processesBernoulli, 10
Peter CHRISTOFFERSENti (2016)
EVALUATING INTERVAL FORECASTS
M. Visser (2008)
Garch Parameter Estimation Using High-Frequency Data
T. Bollerslev (1986)
Generalized autoregressive conditional heteroskedasticityJournal of Econometrics, 31
F. Iqbal, K. Mukherjee (2010)
M-estimators of some GARCH-type models; computation and applicationStatistics and Computing, 20
L. Glosten, R. Jagannathan, D. Runkle (1993)
On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on StocksJournal of Finance, 48
P. Bougerol, N. Picard (1992)
Stationarity of Garch processes and of some nonnegative time seriesJournal of Econometrics, 52
M. McAleer, M. Medeiros (2008)
Realized Volatility: A ReviewEconometric Reviews, 27
In this paper, we study the GJR scaling model which embeds the intraday return processes into the daily GJR model and propose a class of robust M-estimates for it. The estimation procedures would be more efficient when high-frequency data is taken into the model. However, high-frequency data brings noises and outliers which may lead to big bias of the estimators. Therefore, robust estimates should be taken into consideration. Asymptotic results are derived from the robust M-estimates without the finite fourth moment of the innovations. A simulation study is carried out to assess the performance of the model and its estimates. Robust M-estimate of GJR model is also applied in predicting VaR for real financial time series.
Acta Mathematicae Applicatae Sinica – Springer Journals
Published: Apr 26, 2015
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.