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Dijital Arşivi

Bayesian changepoint and time-varying parameter learning in regime switching volatility models

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dc.contributor Ph.D. Program in Computer Engineering.
dc.contributor.advisor Gürgen, Fikret.
dc.contributor.author Yümlü, Mustafa Serdar.
dc.date.accessioned 2023-03-16T10:13:44Z
dc.date.available 2023-03-16T10:13:44Z
dc.date.issued 2015.
dc.identifier.other CMPE 2015 Y86 PhD
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/12601
dc.description.abstract This dissertation proposes a combined state and piecewise time-varying parameter learning technique in regime switching volatility models using multiple changepoint detection. This approach is a Sequential Monte Carlo method for estimating GARCH & EGARCH based volatility models with an unknown number of changepoints. Modern auxiliary particle ltering techniques are used to calculate the posterior densities and online forecasts. This approach also automatically deals with the common ancestral path dependence problem faced in these type volatility models. The model is tested on Borsa Istanbul (BIST) formerly known as Istanbul Stock Exchange (ISE) market data using daily log returns. A full structural changepoint speci cation is de- ned in which all parameters of the conditional variance of the volatility models are dynamic. Finally, it is shown with simulation experiments that the proposed approach partitions the series into several regimes and learns the parameters of each regime's volatility model in parallel with the multiple changepoint detection process and shows better forecasting power compared to previous techniques.
dc.format.extent 30 cm.
dc.publisher Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2015.
dc.subject.lcsh GARCH model.
dc.subject.lcsh Time-series analysis.
dc.subject.lcsh Stochastic models.
dc.subject.lcsh volatility models
dc.title Bayesian changepoint and time-varying parameter learning in regime switching volatility models
dc.format.pages xv, 101 leaves ;


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