A Bayesian Approach to Heterocedastic Models of Time Series and its Application in the Volatility of Financial Assets
DOI:
https://doi.org/10.15381/pesquimat.v24i2.21152Keywords:
DCC - GARCH heteroscedastic models, MCMC methodologyAbstract
In this work, the modeling of the volatility of financial assets is studied using a Bayesian approach. DCC - GARCH models are used, for the errors of these models asymmetric and leptokurtic probability distributions are considered, which are parameterized according to the asymmetry and the weight of the tails, therefore these parameters are also estimated. The estimation of the model parameters was performed using the MCMC methodology Metropolis - Hastings random walk algorithm using the software R package bayesDccGarch, daily data from 04/01/2015 - 01/31/2020 of the stock indices of: Frankfurt are considered (DAX), Tokyo (NIKKEI225), Paris (CAC40), and Lima (BVL). The Bayesian approach to estimating the model parameters facilitates interpretation and provides the ability to insert a priori information for the parameters.
Downloads
Published
Issue
Section
License
Copyright (c) 2021 Edwin Antero Flores Montoya, Antonio Bravo Quiroz
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
THE AUTHORS RETAIN THEIR RIGHTS:
a) The authors retain their trademark and patent rights, and also on any process or procedure described in the article.
b) The authors retain the right to share, copy, distribute, execute and publicly communicate the article published in Pesquimat magazine (for example, place it in an institutional repository or publish it in a book), with recognition of its initial publication in the Pesquimat magazine.
c) The authors retain the right to make a later publication of their work, to use the article or any part of it (for example: a compilation of their works, notes for conferences, thesis, or for a book), provided that they indicate the source of publication (authors of the work, magazine, volume, number and date).
