A Score Test for Discreteness in GARCH Models
AbstractThe standard continuous-state GARCH model is misspecified if applied to returns calculated from discrete price series. We propose a modiÞcation of the above model for handling such cases, by modeling the dependent variable as an unobservable stochastic variable with certain observed outcomes. We further construct a score test that can be used to check if the proposed model differ significantly from the one we would have if all variables were observed, i.e. an underlying latent GARCH model. Using price data from some Australian stocks with high tick size to price ratios, we find the important result that in no case does the proposed model differ significantly from an unobservable continuous-state GARCH model.
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Bibliographic InfoPaper provided by Quantitative Finance Research Centre, University of Technology, Sydney in its series Research Paper Series with number 76.
Date of creation: 01 Mar 2002
Date of revision:
GARCH; latent variables; generalized residuals; score test;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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- Gourieroux, Christian & Monfort, Alain & Renault, Eric & Trognon, Alain, 1987. "Generalised residuals," Journal of Econometrics, Elsevier, vol. 34(1-2), pages 5-32.
- Gourieroux, C. & Monfort, A. & Trognon, A., 1985. "A General Approach to Serial Correlation," Econometric Theory, Cambridge University Press, vol. 1(03), pages 315-340, December.
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics,
Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Neil Shephard & Tina Hviid Rydberg, 2002.
"Dynamics of trade-by-trade price movements: decomposition and models,"
Economics Series Working Papers
2002-FE-04, University of Oxford, Department of Economics.
- Tina Hviid Rydberg & Neil Shephard, 2003. "Dynamics of Trade-by-Trade Price Movements: Decomposition and Models," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 1(1), pages 2-25.
- Tina Hviid Rydberg & Neil Shephard, 2002. "Dynamics of trade-by-trade price movements: decomposition and models," Economics Papers 2002-W1, Economics Group, Nuffield College, University of Oxford.
- Tina Hviid Rydberg & Neil Shephard, 2002. "Dynamics of trade-by-trade price movements: decomposition and models," OFRC Working Papers Series 2002fe04, Oxford Financial Research Centre.
- McKenzie, Michael D. & Brooks, Robert D. & Faff, Robert W. & Ho, Yew Kee, 2000. "Exploring the economic rationale of extremes in GARCH generated betas The case of U.S. banks," The Quarterly Review of Economics and Finance, Elsevier, vol. 40(1), pages 85-106.
- Gourieroux Christian & Monfort Alain & Trognon A, 1984. "General approach of serial correlation (a)," CEPREMAP Working Papers (Couverture Orange) 8424, CEPREMAP.
- Bollerslev, Tim & Engle, Robert F. & Nelson, Daniel B., 1986. "Arch models," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 49, pages 2959-3038 Elsevier.
- Szpiro, George G., 1998. "Tick size, the compass rose and market nanostructure," Journal of Banking & Finance, Elsevier, vol. 22(12), pages 1559-1569, December.
- M. F. Omran & E. McKenzie, 2000. "Heteroscedasticity in stock returns data revisited: volume versus GARCH effects," Applied Financial Economics, Taylor & Francis Journals, vol. 10(5), pages 553-560.
- Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
- Jurgen A. Doornik and Marius Ooms, 2001.
"Multimodality and the GARCH Likelihood,"
Computing in Economics and Finance 2001
76, Society for Computational Economics.
- Jurgen A. Doornik & Marius Ooms, 2000. "Multimodality and the GARCH Likelihood," Econometric Society World Congress 2000 Contributed Papers 0798, Econometric Society.
- Joel Hasbrouck, 1999. "The Dynamics of Discrete Bid and Ask Quotes," Journal of Finance, American Finance Association, vol. 54(6), pages 2109-2142, December.
- Lange, Stephen, 1999. "Modeling asset market volatility in a small market:: Accounting for non-synchronous trading effects," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 9(1), pages 1-18, January.
- Brooks, Robert D. & Faff, Robert W. & Fry, Tim R. L., 2001. "GARCH modelling of individual stock data: the impact of censoring, firm size and trading volume," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 11(2), pages 215-222, June.
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