In an article published by the Journal of Data Science, authors present a novel and highly flexible concept to simulate correlation matrixes of financial markets. It produces realistic outcomes regarding stylized facts of empirical correlation matrixes and requires no asset return input data.
The matrix generation is based on a multiobjective evolutionary algorithm, so the authors call the approach matrix evolutions. It is suitable for parallel implementation and can be accelerated by graphics processing units and quantum-inspired algorithms. The approach is useful for backtesting, pricing, and hedging correlation-dependent investment strategies and financial products.
Its potential is demonstrated in a machine learning case study for robust portfolio construction in a multi-asset universe: An explainable machine learning program links the synthetic matrixes to the portfolio volatility spread of hierarchical risk parity versus equal risk contribution.
The article follows an approach that could be called triple ML as they:
- use ML to generate synthetic correlations (evolutionary algorithms are sometimes deemed AI)
- test where HRP (hierarchical risk parity) outperforms (HRP uses unsupervised representation learning like hierarchical clustering)
- use XML to explain the decision making of the ML
The approach augments the training data space for an explainable ML program to identify the most critical properties in matrixes that lead to the relative performance of competing approaches to portfolio construction. The authors show that HRP is very robust and that the method can identify the driving variables behind it.
Matrix evolutions can be used for many different applications, such as generating risk scenarios for portfolios and pricing of multi-asset derivatives. The entire workflow involving matrix evolutions scales well with technologies of acceleration such as GPUs and quantum-inspired algorithms. In this way, millions of realistic samples can be run to simulate correlated markets.