Clustering heteroskedastic time series by model-based procedures
Financial time series are often characterized by similar volatility structures. The detection of clusters of series displaying similar behavior could be important in understanding the differences in the estimated processes, without having to study and compare the estimated parameters across all the series. This is particularly relevant when dealing with many series, as in financial applications. The volatility of a time series can be characterized in terms of the underlying GARCH process. Using Wald tests and the Autoregressive metrics to measure the distance between GARCH processes, it is shown that it is possible to develop a clustering algorithm, which can provide three classifications (with increasing degree of deepness) based on the heteroskedastic patterns of the time series. The number of clusters is detected automatically and it is not fixed a priori or a posteriori. The procedure is evaluated by simulations and applied to the sector indices of the Italian market.
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- Giampiero M. Gallo & Edoardo Otranto, 2007.
"Volatility transmission across markets: a Multichain Markov Switching model,"
Applied Financial Economics,
Taylor & Francis Journals, vol. 17(8), pages 659-670.
- Giampiero Gallo & Edoardo Otranto, 2006. "Volatility Transmission Across Markets: A Multi-Chain Markov Switching Model," Econometrics Working Papers Archive wp2006_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
- 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".
- Gallo, Giampiero M. & Otranto, Edoardo, 2008. "Volatility spillovers, interdependence and comovements: A Markov Switching approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3011-3026, February.
- 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.
- Edoardo Otrano & Umberto Triacca, 2007. "Testing for Equal Predictability of Stationary ARMA Processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(9), pages 1091-1108.
- Kristin Forbes & Roberto Rigobon, 1999.
"No Contagion, Only Interdependence: Measuring Stock Market Co-movements,"
NBER Working Papers
7267, National Bureau of Economic Research, Inc.
- Kristin J. Forbes & Roberto Rigobon, 2002. "No Contagion, Only Interdependence: Measuring Stock Market Comovements," Journal of Finance, American Finance Association, vol. 57(5), pages 2223-2261, October.
- Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
- Monica Billio & Massimiliano Caporin & Michele Gobbo, 2006. "Flexible Dynamic Conditional Correlation multivariate GARCH models for asset allocation," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 2(2), pages 123-130, March.
- Steece, Bert & Wood, Steven, 1985. "A Test for the Equivalence of k ARMA Models," Empirical Economics, Springer, vol. 1(1), pages 1-11.
- 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.
- Engle, Robert F & Sheppard, Kevin K, 2001.
"Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH,"
University of California at San Diego, Economics Working Paper Series
qt5s2218dp, Department of Economics, UC San Diego.
- Robert F. Engle & Kevin Sheppard, 2001. "Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH," NBER Working Papers 8554, National Bureau of Economic Research, Inc.
- Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
- 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.
- 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.
- 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.
- Edoardo Otranto, 2004. "Classifying the Markets Volatility with ARMA Distance Measures," Econometrics 0402009, EconWPA, revised 05 Mar 2004.
- Brooks, Chris & Burke, Simon P. & Persand, Gita, 2001. "Benchmarks and the accuracy of GARCH model estimation," International Journal of Forecasting, Elsevier, vol. 17(1), pages 45-56.
- Dalla Valle, L. & Giudici, P., 2008. "A Bayesian approach to estimate the marginal loss distributions in operational risk management," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3107-3127, February.
- 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.
- Tim Chenoweth & Robert Hubata & Robert D. St. Louis, 2004. "The power of tests for equivalent ARMA models: The implications for practitioners," Empirical Economics, Springer, vol. 29(2), pages 281-292, 05.
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