Nirwan, R.S. and Bertschinger, N. (2019) Applications of Gaussian Process Latent Variable Models in Finance. In: Bi, Y., Bhatia, R. and Kapoor, S., Eds., Intelligent Systems and Applications, IntelliSys 2019, Advances in Intelligent Systems and Computing, Springer, Cham, 1209-1221.
https://doi.org/10.1007/978-3-030-29513-4_87

has been cited by the following article:

• TITLE: Asset Allocation Strategy with Non-Hierarchical Clustering Risk Parity Portfolio

  AUTHORS: Kei Nakagawa, Takanobu Kawahara, Akio Ito

  KEYWORDS: Non-Hierarchical Clustering Risk Parity, Asset Allocation, x-means++

  JOURNAL NAME: Journal of Mathematical Finance, Vol.10 No.4, October 10, 2020

  ABSTRACT: Since the great financial crisis of 2008, many studies have pointed out that even in the portfolio where the asset allocation is sufficiently diversified, it is still possible that risk allocation is well concentrated to a few assets. One approach to this problem is risk parity strategies which equalize the risk contribution of each asset. However, even if we equalize the risk contribution, risk sources are not necessarily diversified. In this paper, we propose non-hierarchical clustering-risk parity strategy which will equalize risk contribution from and within each cluster. In addition, in order to ensure robustness of clustering, we also propose x-means++ algorithm which combines k-means++ with x-means. Assuming assets with similar movement have common risk sources, our approach will construct a portfolio which equalizes risk sources. Empir-ical analysis using actual price data of various asset classes shows that our proposed method will outperform risk-parity strategies or hierarchical clustering risk parity strategies.