Hidden Markov independent component analysis as a measure of coupling in multivariate financial time series
Modelling the dynamics of financial markets has been an area of active research in recent years. This paper presents a time series analysis model which can be used to infer patterns within financial data, in order to better understand the dynamics of financial markets. The focus of the paper is on f...
| Main Authors: | , |
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| Format: | Conference item |
| Language: | English |
| Published: |
2008
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| Summary: | Modelling the dynamics of financial markets has been an area of active research in recent years. This paper presents a time series analysis model which can be used to infer patterns within financial data, in order to better understand the dynamics of financial markets. The focus of the paper is on finding causal and time-scale relationships between financial time series. Wavelets are used to extract useful time-scale information from financial data at different frequencies and mutual information between time series is used as the canonical measure of coupling. A Hidden Markov Independent Component Analysis (HMICA) model is used to infer a series of hidden states and it is shown that these hidden states are indicative of changes in mutual information between time series at various different time scales. |
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