Parameter optimization for GARCH Model using hybrid PSO-GA

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dc.contributor.authorLalitha, R.
dc.contributor.authorMohan, B.R.
dc.date.accessioned2026-02-06T06:35:26Z
dc.date.issued2022
dc.description.abstractBuilding Garch Models using classical numerical methods has been a difficult task, and many approaches to solve this problem has been proposed in the recent past which includes meta heuristic and evolutionary algorithms as well. In addition, the Fluctuations found in stock market follow volatility clustering. This clustering feature has made forecasting difficult. In this paper we plan to estimate the parameters of GARCH Model using PSO-GA approach, and also consider the effects caused by the positive shocks and negative shocks using GJR-Garch. We also propose a comparison between hybrid PSO(Particle Swarm Optimization) and GA(Genetic Algorithm) for parameter optimization in GARCH Model, which will be compared with the existing systems. © 2022 IEEE.
dc.identifier.citation2022 IEEE 3rd Global Conference for Advancement in Technology, GCAT 2022, 2022, Vol., , p. -
dc.identifier.urihttps://doi.org/10.1109/GCAT55367.2022.9971873
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/29830
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectGarch Models
dc.subjectGenetic Algorithms
dc.subjectGJR-GARCH
dc.subjectHeteroskedacity
dc.subjectParameter Optimization
dc.subjectParticle Swarm Optimisation
dc.subjectVolatility Clustering
dc.subjectVolatility Forecasting
dc.titleParameter optimization for GARCH Model using hybrid PSO-GA

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