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Stochastic volatility modelling in portfolio selection via sequential Monte Carlo simulation

Pedro Henrique Melo Albuquerque et al.

International Journal of Portfolio Analysis and Management2021https://doi.org/10.1504/ijpam.2021.10038382article
ABDC B
Weight
0.26

What the paper says

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.

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https://doi.org/https://doi.org/10.1504/ijpam.2021.10038382

Or copy a formatted citation

@article{pedro2021,
  title        = {{Stochastic volatility modelling in portfolio selection via sequential Monte Carlo simulation}},
  author       = {Pedro Henrique Melo Albuquerque et al.},
  journal      = {International Journal of Portfolio Analysis and Management},
  year         = {2021},
  doi          = {https://doi.org/https://doi.org/10.1504/ijpam.2021.10038382},
}

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Stochastic volatility modelling in portfolio selection via sequential Monte Carlo simulation

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Evidence weight

0.26

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.20 × 0.15 = 0.03
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.