ACM Computing Surveys (CSUR)
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Mixtures of ARMA Models for Model-Based Time Series Clustering
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Time series clustering and classification by the autoregressive metric
Computational Statistics & Data Analysis
Volatility spillovers, interdependence and comovements: A Markov Switching approach
Computational Statistics & Data Analysis
A Bayesian approach to estimate the marginal loss distributions in operational risk management
Computational Statistics & Data Analysis
A periodogram-based metric for time series classification
Computational Statistics & Data Analysis
Clustering of time series data-a survey
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Identifying financial time series with similar dynamic conditional correlation
Computational Statistics & Data Analysis
Clustering of discretely observed diffusion processes
Computational Statistics & Data Analysis
Non-linear time series clustering based on non-parametric forecast densities
Computational Statistics & Data Analysis
Time series labeling algorithms based on the K-nearest neighbors' frequencies
Expert Systems with Applications: An International Journal
A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples
Computational Statistics & Data Analysis
A Graphical Tool for Describing the Temporal Evolution of Clusters in Financial Stock Markets
Computational Economics
A spatial contagion measure for financial time series
Expert Systems with Applications: An International Journal
| Hi-index | 0.03 |
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.