Style Analysis with Particle Filtering and Generalized Simulated Annealing (Forthcoming in International Journal of Financial Engeneering)



(First verion : 2016年1月30日)


This paper proposes a new approach to style analysis of mutual funds in a general state space framework with particle filtering and generalized simulated annealing (GSA). Specifically, we regard the ex-posure of each style index as a latent state variable in a state space model and employ a Monte Carlo filter as a particle filtering method, where GSA is effectively applied to estimating unknown parameters. An empirical analysis using data of three Japanese equity mu-tual funds with six standard style indexes confirms the validity of our method. Moreover, we create fund-specific style indexes to further improves estimation in the analysis.