GARCH-based robust clustering of time series

Autor: Livia De Giovanni, Pierpaolo D'Urso, Riccardo Massari
Rok vydání: 2016
Předmět:
Fuzzy clustering
Heteroskedastic time series
Logic
Autoregressive conditional heteroskedasticity
02 engineering and technology
01 natural sciences
GARCH model
010104 statistics & probability
Unconditional and time-varying volatility
Fuzzy partitioning around medoids
Outliers
Robust metric
Noise cluster
Trimming
Volatilities daily stocks returns
International stock-market volatility daily returns
Artificial Intelligence
CURE data clustering algorithm
0202 electrical engineering
electronic engineering
information engineering
Econometrics
0101 mathematics
Cluster analysis
Mathematics
Robustification
Medoid
ComputingMethodologies_PATTERNRECOGNITION
Financial models with long-tailed distributions and volatility clustering
Parametric model
020201 artificial intelligence & image processing
Algorithm
Zdroj: Fuzzy Sets and Systems. 305:1-28
ISSN: 0165-0114
DOI: 10.1016/j.fss.2016.01.010
Popis: In this paper we propose different robust fuzzy clustering models for classifying heteroskedastic (volatility) time series, following the so-called model-based approach to time series clustering and using a partitioning around medoids procedure. The proposed models are based on a GARCH parametric modeling of the time series, i.e. the unconditional volatility and the time-varying volatility GARCH representation of the time series. We first suggest a timid robustification of the fuzzy clustering. Then, we propose three robust fuzzy clustering models belonging to the so-called metric, noise and trimmed approaches, respectively. Each model neutralizes the negative effects of the outliers in the clustering process in a different manner. In particular, the first robust model, based on the metric approach, achieves its robustness with respect to outliers by taking into account a robust distance measure; the second, based on the noise approach, achieves its robustness by introducing a noise cluster represented by a noise prototype; the third, based on the trimmed approach, achieves its robustness by trimming away a certain fraction of outlying time series. The usefulness and effectiveness of the proposed clustering models is illustrated by means of a simulation study and two applications in finance and economics.
Databáze: OpenAIRE
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