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Multi rate Multi regime and Multivariate Models for Continuous Traits

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Abstract

Markov regime switching (MRS) models successfully describe the cyclical behavior of time series by introducing hidden states and can better explain some stylised facts of asset returns. However, due to the complexity of the model, especially for multi-variate and multi-state cases, traditional maximum likelihood estimation (MLE) methods for MRS model suffers from strict assumptions and prone to converge to local optima. In this paper, we design a spectral clustering algorithm to predict hidden states of multi-variate MRS model by constructing feature vector and derive the parameter estimation. Monte-Carlo simulation results show that our algorithm is more robust than MLE. Meanwhile, we also give an application example of the algorithm by implementing a MRS asset allocation strategy in Chinese stock market.

Notes

  1. Transition matrix is Markovian matrix, whose row sum equals 1. Covariance matrix is positive definite.

  2. The first eigenvalue of a Laplacian matrix is 0.

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Acknowledgements

Zheng and Zhang gratefully acknowledges the financial support of the National Natural Science Foundation of China (No. 71671164). Xu gratefully acknowledges the financial support of the National Natural Science Foundation of China (No. 71771197). Zheng acknowledges the views expressed are solely those of the author and do not reflect the views of his employer, Moody's Analytics, or its parent company, Moody's Corporation or its affiliates.

Author information

Authors and Affiliations

  1. Moody's Analytics, New York, USA

    Kai Zheng

  2. School of Economics, North Minzu University, Yinchuan, China

    Weidong Xu

  3. School of Management, Zhejiang University, Hangzhou, China

    Kai Zheng, Weidong Xu & Xili Zhang

Corresponding author

Correspondence to Weidong Xu.

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Cite this article

Zheng, K., Xu, W. & Zhang, X. Multivariate Regime Switching Model Estimation and Asset Allocation. Comput Econ (2021). https://doi.org/10.1007/s10614-021-10203-9

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  • DOI : https://doi.org/10.1007/s10614-021-10203-9

Keywords

  • Multi-variate Markov regime switching
  • Feature construction
  • Spectral clustering
  • Machine learning
  • Asset allocation

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