Title: Stochastic volatility modelling in portfolio selection via sequential Monte Carlo simulation

Authors: Igor Ferreira Do Nascimento; Pedro Henrique Melo Albuquerque; Yaohao Peng

Addresses: Department of Statistics, Federal Institute of Piauí, 94 Álvaro Mendes Street, Centro (Sul), Teresina – Piauí, Brazil ' Department of Administration, University of Brasília, Campus Darcy Ribeiro, School of Economics, Business and Accounting, Building A-2, Office A1-54/7, Brasília – Distrito Federal, Brazil ' Secretariat of Economic Policy, Brazilian Ministry of Economy, Esplanade of Ministries, Block P, Office 324, Brasília – Distrito Federal, Brazil

Abstract: This paper uses particle filter to estimate daily volatility in the Brazilian financial stocks market and obtain an optimal allocation of assets via Monte Carlo approach. Our volatility model outperforms the Kalman filter besides overcoming non-additivity and non-Gaussian disturbance pattern. The historical statistics use an optimist Black-Litterman priori view to systematise our analysis in a rolling window. Our proposed method has better out-of-sample metrics than Markowitz, Naive (equal assets weight) and Bovespa Index benchmark.

Keywords: stochastic volatility; particle filter; Monte Carlo methods; dynamic models; portfolio allocation; Bayesian analysis.

DOI: 10.1504/IJPAM.2021.115633

International Journal of Portfolio Analysis and Management, 2021 Vol.2 No.3, pp.249 - 267

Received: 05 Mar 2019
Accepted: 25 Sep 2019
Published online: 15 Jun 2021 *

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