Dynamic Correlation Multivariate Stochastic Volatility Black-Litterman With Latent Factors

  •  Mihnea S. Andrei    
  •  Sujit K. Ghosh    
  •  Jian Zou    


In finance, it is often of interest to study market volatility for portfolios that may consist of a large number of assets using multivariate stochastic volatility models. However, such models, though useful, do not usually incorporate investor views that might be available. In this paper we introduce a novel hierarchical Bayesian methodology of modeling volatility for a large portfolio of assets that incorporates investor’s personal views of the market via the Black-Litterman (BL) model. We extend the scope and use of BL models by using it within a multivariate stochastic volatility model based on latent factors for dimensionality reduction but allows for time varying correlations. Detailed derivations of MCMC algorithm are provided with an illustration with S&P500 asset returns. Moreover, sensitivity analysis for the confidence levels that the investor has in their personal views is also explored. Numerical results show that the proposed method provides flexible interpretation based on the investor’s uncertainty in personal beliefs, and converges to the empirical sample estimate when their confidence level of the market becomes weak.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1927-7032
  • ISSN(Online): 1927-7040
  • Started: 2012
  • Frequency: bimonthly

Journal Metrics

  • h-index (December 2021): 20
  • i10-index (December 2021): 51
  • h5-index (December 2021): N/A
  • h5-median(December 2021): N/A

( The data was calculated based on Google Scholar Citations. Click Here to Learn More. )