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The Effect of (Mis-Specified) GARCH Filters on the Finite Sample Distribution of the BDS Test

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  • Brooks, Chris
  • Heravi, Saeed M

Abstract

This paper considers the effect of using a GARCH filter on the properties of the BDS test statistic as well as a number of other issues relating to the application of the test. It is found that, for certain values of the user-adjustable parameters, the finite sample distribution of the test is far-removed from asymptotic normality. In particular, when data generated from some completely different model class are filtered through a GARCH model, the frequency of rejection of iid falls, often substantially. The implication of this result is that it might be inappropriate to use non-rejection of iid of the standardised residuals of a GARCH model as evidence that the GARCH model 'fits' the data. Citation Copyright 1999 by Kluwer Academic Publishers.

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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 13 (1999)
Issue (Month): 2 (April)
Pages: 147-62

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Handle: RePEc:kap:compec:v:13:y:1999:i:2:p:147-62

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Cited by:
  1. Brooks, Chris & Henry, Olan T., 2000. "Can portmanteau nonlinearity tests serve as general mis-specification tests?: Evidence from symmetric and asymmetric GARCH models," Economics Letters, Elsevier, vol. 67(3), pages 245-251, June.
  2. Brooks, Chris & Henry, Olan T., 2000. "Linear and non-linear transmission of equity return volatility: evidence from the US, Japan and Australia," Economic Modelling, Elsevier, vol. 17(4), pages 497-513, December.
  3. Theodore Panagiotidis, 2002. "Testing the assumption of Linearity," Economics Bulletin, AccessEcon, vol. 3(29), pages 1-9.
  4. Paul Alagidede & Theodore Panagiotidis, 2009. "Modelling stock returns in Africa’s emerging equity markets," Discussion Paper Series 2009_01, Department of Economics, University of Macedonia, revised Jan 2009.
  5. Evzen Kocenda & Lubos Briatka, 2004. "Advancing the iid Test Based on Integration across the Correlation Integral: Ranges, Competition, and Power," CERGE-EI Working Papers wp235, The Center for Economic Research and Graduate Education - Economic Institute, Prague.
  6. Yi-Ting Chen & Chung-Ming Kuan, 2002. "Time irreversibility and EGARCH effects in US stock index returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 565-578.
  7. Theodore Panagiotidis, 2005. "Market capitalization and efficiency. Does it matter? Evidence from the Athens Stock Exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 15(10), pages 707-713.
  8. K.P. Lim & M.J. Hinich & K.S. Liew, 2003. "GARCH Diagnosis with Portmanteau Bicorrelation Test: An Application on the Malaysia's Stock Market," Finance 0307013, EconWPA.
  9. Dungey, Mardi & Milunovich, George & Thorp, Susan, 2010. "Unobservable shocks as carriers of contagion," Journal of Banking & Finance, Elsevier, vol. 34(5), pages 1008-1021, May.
  10. Geoff Willcocks, 2009. "UK Housing Market: Time Series Processes with Independent and Identically Distributed Residuals," The Journal of Real Estate Finance and Economics, Springer, vol. 39(4), pages 403-414, November.
  11. Jorge Pérez-Rodríguez & Julián Andrada-Félix, 2013. "Estimating critical values for testing the i.i.d. in standardized residuals from GARCH models in finite samples," Computational Statistics, Springer, vol. 28(2), pages 701-734, April.

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