Robust estimates for GARCH models
In this paper we present two robust estimates for GARCH models. The first is defined by the minimization of a conveniently modified likelihood and the second is similarly defined, but includes an additional mechanism for restricting the propagation of the effect of one outlier on the next estimated...
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| Formato: | Capítulo de libro |
| Lenguaje: | Inglés |
| Publicado: |
2008
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
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| 024 | 7 | |2 scopus |a 2-s2.0-45049085962 | |
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| 030 | |a JSPID | ||
| 100 | 1 | |a Muler, N. | |
| 245 | 1 | 0 | |a Robust estimates for GARCH models |
| 260 | |c 2008 | ||
| 270 | 1 | 0 | |m Muler, N.; Departamento de Matemáticas y Estadística, Universidad Torcuato Di Tella, Miñones 2177, 1428 Buenos Aires, Argentina; email: nmuler@utdt.edu |
| 506 | |2 openaire |e Política editorial | ||
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| 504 | |a Franses, P.H., van Dijk, D., 2000. Outlier detection in GARCH models. Research Report EI-9926/RV, Econometric Institute, Erasmus University, Rotterdam; Geweke, J., Comment-modeling persistency of conditional variances (1986) Econometric Rev., 5, pp. 57-61 | ||
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| 504 | |a Huber, P.J., Robust estimation of a location parameter (1964) Ann. Math. Statist., 35, pp. 73-101 | ||
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| 504 | |a Li, J., Kao, C., 2002. Bounded influence estimation and outlier detection for GARCH models with and application to foreign exchange rates. Working Paper Presented at the 57th European Meeting of the Econometric Society; Martin, R.D., Samarov, A., Vandaele, W., Robust methods for ARIMA models (1983) Applied Time Series. Analysis of Economic Data, pp. 153-177. , Zellner E. (Ed), Bureau of the Census, Washington, DC | ||
| 504 | |a Mendes, B.V.M., Assessing the bias of maximum likelihood estimates of contaminated GARCH Models (2000) J. Statist. Comput. Simulation, 67, pp. 359-376 | ||
| 504 | |a Mendes, B.V.M., Duarte, A.M., Robust estimation for ARCH models (1999) Rev. Econometria, 19, pp. 138-180 | ||
| 504 | |a Muler, N., Yohai, V.J., Robust estimates for ARCH Processes (2002) J. Time Series Anal., 23, pp. 341-375 | ||
| 504 | |a Muler, N., Yohai, V.J., 2007. Robust estimates for GARCH models. Technical Report, Instituto de Calculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires. Available at: 〈http://mate.dm.uba.ar/∼vyohai/muler-yohai-GARCH〉; Nelson, D., Conditional heteroskedasticity in asset returns: a new approach (1991) Econometrica, 59, pp. 347-370 | ||
| 504 | |a Park, B., An outlier robust GARCH model and forecasting volatility of exchange rate returns (2002) J. Forecasting, 21, pp. 381-393 | ||
| 504 | |a Peng, L., Yao, Q., Miscellanea. Least absolute deviations estimation for ARCH and GARCH models (2003) Biometrika, 90, pp. 967-975 | ||
| 504 | |a Rieder, H., Ruckdeschel, P., Kohl, M., 2002. Robust estimation for time series models based on infinitesimal neighborhoods. Working Paper Presented at ICORS 2002, Vancouver; Ronchetti, E., Trojani, F., Robust inference with GMM estimators (2001) J. Econometrics, 101, pp. 37-69 | ||
| 504 | |a Sakata, S., White, H., High breakdown point conditional dispersion estimation with application to S&P daily returns volatility (1998) Econometrica, 66, pp. 529-567 | ||
| 504 | |a Straumann, D., Mikosch, T., Quasi-maximum-likelihood estimation in heteroscedastic time series: a stochastic recurrence equation approach (2006) Ann. Statist., 34, pp. 2449-2495 | ||
| 504 | |a van Dijk, D., Lucas, A., Franses, P.H., Testing for ARCH in the presence of additive outliers (1999) J. Appl. Econometrics, 14, pp. 539-562 | ||
| 504 | |a Yohai, V.J., High breakdown-point and high efficiency robust estimates for regression (1987) Ann. Statist., 15, pp. 642-656 | ||
| 520 | 3 | |a In this paper we present two robust estimates for GARCH models. The first is defined by the minimization of a conveniently modified likelihood and the second is similarly defined, but includes an additional mechanism for restricting the propagation of the effect of one outlier on the next estimated conditional variances. We study the asymptotic properties of our estimates proving consistency and asymptotic normality. A Monte Carlo study shows that the proposed estimates compare favorably with respect to other robust estimates. Moreover, we consider some real examples with financial data that illustrate the behavior of these estimates. © 2007 Elsevier B.V. All rights reserved. |l eng | |
| 593 | |a Departamento de Matemáticas y Estadística, Universidad Torcuato Di Tella, Miñones 2177, 1428 Buenos Aires, Argentina | ||
| 593 | |a Universidad de Buenos Aires, CONICET, Argentina | ||
| 690 | 1 | 0 | |a GARCH MODELS |
| 690 | 1 | 0 | |a M-ESTIMATES |
| 690 | 1 | 0 | |a OUTLIERS |
| 690 | 1 | 0 | |a ROBUST ESTIMATION |
| 700 | 1 | |a Yohai, V.J. | |
| 773 | 0 | |d 2008 |g v. 138 |h pp. 2918-2940 |k n. 10 |p J. Stat. Plann. Inference |x 03783758 |w (AR-BaUEN)CENRE-125 |t Journal of Statistical Planning and Inference | |
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| 856 | 4 | 0 | |u https://doi.org/10.1016/j.jspi.2007.11.003 |y DOI |
| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_03783758_v138_n10_p2918_Muler |y Handle |
| 856 | 4 | 0 | |u https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03783758_v138_n10_p2918_Muler |y Registro en la Biblioteca Digital |
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