julienraffaud / GMM

Gaussian mixture model for regime detection in financial time series

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GMM

The objective is to detect regimes in arbitrarily large multivariate time series by "greedily" partitioning the time series into segments, with each segment's data consisting of independent samples from a multivariate Gaussian distribution.

Data:

Here I use daily log-returns of the USD/JPY exchange rate, the 10Y constant maturity JGB yield, and the Nikkei 225 Index. The timespan is 1971 to 2017.

Academic Reference:

Hallac D., Nystrup P., Boyd S. 2018. Greedy Gaussian Segmentation of Multivariate Time Series. (Arxiv: https://arxiv.org/pdf/1610.07435.pdf).

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Gaussian mixture model for regime detection in financial time series

License:MIT License


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Language:Python 100.0%