Real time clustering of time series using triangular potentials
This work addresses portfolio optimization for investors by offering a practical alternative to mean-variance approaches, though it appears incremental as it builds on existing clustering methods.
The paper tackled the problem of computing investment portfolio weightings by clustering assets with similar return characteristics to avoid inverting sample covariance matrices, and presented a method based on triangular potentials with theoretical results and synthetic data examples.
Motivated by the problem of computing investment portfolio weightings we investigate various methods of clustering as alternatives to traditional mean-variance approaches. Such methods can have significant benefits from a practical point of view since they remove the need to invert a sample covariance matrix, which can suffer from estimation error and will almost certainly be non-stationary. The general idea is to find groups of assets which share similar return characteristics over time and treat each group as a single composite asset. We then apply inverse volatility weightings to these new composite assets. In the course of our investigation we devise a method of clustering based on triangular potentials and we present associated theoretical results as well as various examples based on synthetic data.