Structural Changes Detection in GARCH(1,1) Models Using Genetic Algorithms

Student: Glazkov Danila

Educational Programme: Stochastic Modelling in Economics and Finance (Master)

Year of Graduation: 2024

In this study we compare change point detection methods based on the MDL, BIC and HQC criteria. We treat the problem of structural changes detection as the model selection problem of non-nested models: the smaller the criterion the better. We minimize the criteria using a genetic algorithm, which is well suited for difficult optimization problems. It helps us to reduce a huge search space efficiently. The algorithm specially adapted to this problem was implemented by the author in the MATLAB programming language. The study showed that the use of the MDL criterion makes it possible to more effectively detect the correct number of change points.

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