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Outliers in multivariate Garch models

Listed author(s):
  • Veiga, Helena
  • Martín-Barragán, Belén
  • Grané, Aurea

Outliers of moderate magnitude cause large changes in financial time series of prices and returns and affect both the estimation of parameters and volatilities when fitting a GARCH-type model. The multivariate setting is still to be studied, but similar biases and impacts on correlation dynamics are believed to exist. The accurate estimation of the correlation structure is crucial in many applications, such as portfolio allocation and risk management. This paper ocuses on these issues by studding the impact of additive outliers (isolated, patches and volatility outliers) on the estimation of correlations when fitting well known multivariate GARCH models and by proposing a general detection algorithm based on wavelets that can be applied to a large class of multivariate volatility models. This procedure can be also interpreted as a model miss-specification test since it is based on residual diagnostics. The effectiveness of the new proposal is evaluated by an intensive Monte Carlo study before it is applied to daily stock market indices. The simulation studies show that correlations are highly affected by the presence of outliers and that the new method is both effective and reliable, since it detects very few false outliers.

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File URL: http://e-archivo.uc3m.es/bitstream/handle/10016/18495/ws140503.pdf?sequence=3
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Paper provided by Universidad Carlos III de Madrid. Departamento de Estadística in its series DES - Working Papers. Statistics and Econometrics. WS with number ws140503.

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Date of creation: Feb 2014
Handle: RePEc:cte:wsrepe:ws140503
Contact details of provider: Web page: http://portal.uc3m.es/portal/page/portal/dpto_estadistica

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  1. Boudt, Kris & Daníelsson, Jón & Laurent, Sébastien, 2013. "Robust forecasting of dynamic conditional correlation GARCH models," International Journal of Forecasting, Elsevier, vol. 29(2), pages 244-257.
  2. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
  3. Kiefer, Nicholas M. & Salmon, Mark, 1983. "Testing normality in econometric models," Economics Letters, Elsevier, vol. 11(1-2), pages 123-127.
  4. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
  5. van Dijk, Dick & Franses, Philip Hans & Lucas, Andre, 1999. "Testing for ARCH in the Presence of Additive Outliers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 539-562, Sept.-Oct.
  6. Peter Verhoeven, 2000. "Modeling Outliers and Extreme Observations for ARMA-GARCH Processes," Econometric Society World Congress 2000 Contributed Papers 1922, Econometric Society.
  7. M. Angeles Carnero & Daniel Peña & Esther Ruiz, 2007. "Effects of outliers on the identification and estimation of GARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(4), pages 471-497, 07.
  8. Cuesta-Albertos, J.A. & del Barrio, E. & Fraiman, R. & Matran, C., 2007. "The random projection method in goodness of fit for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4814-4831, June.
  9. Baillie, Richard T & Bollerslev, Tim, 2002. "The Message in Daily Exchange Rates: A Conditional-Variance Tale," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 60-68, January.
  10. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
  11. Carnero, M. Angeles & Peña, Daniel & Ruiz, Esther, 2012. "Estimating GARCH volatility in the presence of outliers," Economics Letters, Elsevier, vol. 114(1), pages 86-90.
  12. Ledolter, Johannes, 1989. "The effect of additive outliers on the forecasts from ARIMA models," International Journal of Forecasting, Elsevier, vol. 5(2), pages 231-240.
  13. Galeano, Pedro & Pena, Daniel & Tsay, Ruey S., 2006. "Outlier Detection in Multivariate Time Series by Projection Pursuit," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 654-669, June.
  14. Grossi, Luigi & Laurini, Fabrizio, 2009. "A robust forward weighted Lagrange multiplier test for conditional heteroscedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2251-2263, April.
  15. Grané, Aurea & Veiga, Helena, 2010. "Wavelet-based detection of outliers in financial time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2580-2593, November.
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