Issue |
E3S Web of Conf.
Volume 508, 2024
International Conference on Green Energy: Intelligent Transport Systems - Clean Energy Transitions (GreenEnergy 2023)
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Article Number | 04014 | |
Number of page(s) | 10 | |
Section | Mathematical Physics and Mathematics | |
DOI | https://doi.org/10.1051/e3sconf/202450804014 | |
Published online | 05 April 2024 |
Bayesian estimation of generalized long-memory stochastic volatility
University of the Philippines Manila, Department of Physical Sciences and Mathematics, College of Arts and Sciences, Padre Faura Street, Manila 1000, Philippines
* Corresponding author: acgonzaga@up.edu.ph
We propose a Bayesian approach to estimating the parameters of a Generalized Long-Memory Stochastic Volatility (GLMSV) model, a versatile framework designed to address both persistent (long-memory) and seasonal (cyclic) behaviors across various frequencies. This provides an alternative method incorporating prior information about the model parameters, and allows for relatively computationally efficient sampling from the posterior distribution by a reparametrization of the model parameters. The practical applicability of this methodology is demonstrated through the analysis of intraday volatility in Microsoft stock prices.
Key words: Bayesian estimation / stochastic volatility / generalized long-memory / intraday volatility / Microsoft stock prices
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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