dc.contributor.author | Lamo, Eivind Mathias | |
dc.date.accessioned | 2024-02-03T00:40:11Z | |
dc.date.available | 2024-02-03T00:40:11Z | |
dc.date.issued | 2023-11-27 | |
dc.date.submitted | 2023-11-27T13:01:46Z | |
dc.identifier | STAT399K 0 MAO ORD 2023 HØST | |
dc.identifier.uri | https://hdl.handle.net/11250/3115382 | |
dc.description.abstract | With the continuous increase in computational power, sequential Monte Carlo
methods have emerged as an efficient technique for estimating unknown data in
a world consisting of nonlinearity and non-Gaussianity. In this thesis, we are
building a theoretical foundation by the help of Bayesian statistics, that can be
applied to numerous real-world problems. We are interested in solving the prob-
lem of estimating an unknown signal process given certain observations, where
both processes are modelled as Markovian, nonlinear, non-Gaussian state-space
models. In particular, we will try to estimate the unobserved volatility dynamics
for the S&P 500 index using observed returns and a slight modification of Hes-
ton’s stochastic volatility model. This will be done by the sequential importance
resampling filter, which we will also combine with Markov chain Monte Carlo
for parameter estimation. Our overall goal is to propose another alternative
to Heston’s model, by investigating how well the model responds to measuring
volatility when including data from the financial crisis of 2007-2008.
Keywords: Bayesian inference; sequential Monte Carlo; volatility filtering; fi-
nancial econometrics; particle Markov chain Monte Carlo | |
dc.language.iso | eng | |
dc.publisher | The University of Bergen | |
dc.rights | Copyright the Author. All rights reserved | |
dc.subject | Financial econometrics | |
dc.subject | Particle Markov chain Monte Carlo | |
dc.subject | Sequential Monte Carlo | |
dc.subject | Bayesian inference | |
dc.subject | Volatility filtering | |
dc.title | Sequential Monte Carlo Methods in Practice | |
dc.type | Master thesis | |
dc.date.updated | 2023-11-27T13:01:46Z | |
dc.rights.holder | Copyright the Author. All rights reserved | |
dc.description.degree | Masteroppgave i statistikk | |
dc.description.localcode | STAT399K | |
dc.description.localcode | MAMN-STAT | |
dc.subject.nus | 753299 | |
fs.subjectcode | STAT399K | |
fs.unitcode | 12-11-0 | |