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Classification of Volatility in Presence of Changes in Model Parameters

  • E. Otranto

    ()

The classification of volatility of financial time series has recently received a lot of contributions - in particular using model based clustering algorithms. Recent works have evidenced how volatility structure can vary along time, with gradual or abrupt changes in the coefficients of the model. We wonder if these changes can affect the classification of series in terms of similar volatility structure. We propose to classify the level of the unconditional volatility obtained from Multiplicative Er- ror Models with the possibility of changes in the parameters of the model in terms of regime switching or time varying smoothed coefficients. They provide different unconditional volatility structures with a proper interpretation, useful to represent different situations of interest. The different methodologies are coherent with each other and provide a common synthetic pattern. The procedure is experimented on fifteen stock indices volatilities.

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Paper provided by Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia in its series Working Paper CRENoS with number 201113.

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Date of creation: 2011
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Handle: RePEc:cns:cnscwp:201113
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  1. Otranto, Edoardo, 2008. "Clustering heteroskedastic time series by model-based procedures," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4685-4698, June.
  2. Dueker, Michael J, 1997. "Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 26-34, January.
  3. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  4. Pattarin, Francesco & Paterlini, Sandra & Minerva, Tommaso, 2004. "Clustering financial time series: an application to mutual funds style analysis," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 353-372, September.
  5. Robert F. Engle & Giampiero M. Gallo, 2003. "A Multiple Indicators Model for Volatility Using Intra-Daily Data," NBER Working Papers 10117, National Bureau of Economic Research, Inc.
  6. Caiado, Jorge & Crato, Nuno & Pena, Daniel, 2006. "A periodogram-based metric for time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2668-2684, June.
  7. Otranto, Edoardo, 2010. "Identifying financial time series with similar dynamic conditional correlation," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 1-15, January.
  8. BAUWENS, Luc & LAURENT, Sébastien & ROMBOUTS, Jeroen VK, . "Multivariate GARCH models: a survey," CORE Discussion Papers RP -1847, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  9. Maharaj, E.A., 1994. "A Significance Test for Classifying ARMA Models," Monash Econometrics and Business Statistics Working Papers 18/94, Monash University, Department of Econometrics and Business Statistics.
  10. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
  11. Edoardo Otranto & Giampiero M. Gallo, 2001. "A Nonparametric Bayesian Approach to Detect the Number of Regimes in Markov Switching Models," Econometrics Working Papers Archive wp2001_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
  12. Zacharias Psaradakis & Nicola Spagnolo, 2003. "On The Determination Of The Number Of Regimes In Markov-Switching Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(2), pages 237-252, 03.
  13. Giampiero Gallo & Edoardo Otranto, 2007. "Volatility Spillovers, Interdependence and Comovements: A Markov Switching Approach," Econometrics Working Papers Archive wp2007_11, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
  14. repec:oxf:wpaper:264 is not listed on IDEAS
  15. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
  16. Thomas Mikosch & Catalin Starica, 2004. "Non-stationarities in financial time series, the long range dependence and the IGARCH effects," Econometrics 0412005, EconWPA.
  17. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
  18. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
  19. Theodossiou, Panayiotis & Lee, Unro, 1993. "Mean and Volatility Spillovers across Major National Stock Markets: Further Empirical Evidence," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 16(4), pages 337-50, Winter.
  20. Corduas, Marcella & Piccolo, Domenico, 2008. "Time series clustering and classification by the autoregressive metric," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1860-1872, January.
  21. Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394.
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