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The Global ETD Search service is a free service for researchers
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Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
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Showing 1 to 3 of 3 (0.025 seconds)Spelling suggestions: "subject:"[een] MONTE CARLO LIKELIHOOD"" "subject:"[enn] MONTE CARLO LIKELIHOOD""
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[en] STOCHASTIC VOLATILITY VIA MONTE CARLO LIKELIHOOD: A COMPARATIVE STUDY / [pt] VOLATILIDADE ESTOCÁSTICA VIA VEROSSIMILHANÇA DE MONTE CARLO: UM ESTUDO COMPARATIVORAPHAEL PIMENTEL DE OLIVEIRA CRUZ 26 May 2004 (has links)
[pt] Esta dissertação discute o modelo de Volatilidade
Estocástica (SV) estimado via metodologia Durbin & Koopman,
chamada Verossimilhança de Monte Carlo( MCL). Comparou-se a
cobertura condicional do valor em risco (VaR), deste
modelo, com as do modelo GARCH(1,1) e SV estimado via Quasi
Máxima Verossimilhança (QML). Os modelos foram estendindos a
distúrbios Gaussiano e t-Student na equação da média. O
desempenho dos modelos foi avaliado fora da amostra para
retornos diários dos índices Ibovespa, S&P500, Nasdaq e Dow
Jones. Para o critério de avaliação foi utilizado o teste
de Christoffersen. Foram econtradas evidências empíricas
de que o modelo SV estimado via MCL é tão eficiente quanto
o modelo GARCH(1,1), em termos da cobertura condicional do
VaR. / [en] This dissertation discusses the estimation of the
Stochastic Volatility (SV)model using a Durbin and Koopman
methodology called Monte Carlo Like-lihood (MCL). The
conditional coverage of value at risk (VaR) of SV via
MCL model was compared to the GARCH (1,1) model and to the
SV model via Quasi Maximum Likelihood (QML) estimation. The
models were extended to Gaussian and Student-t isturbances
in the mean equation. The performances of the models were
evaluated out-of-sample for daily returns on the Ibovespa,
S&P500, Nasdaq and Dow Jones indexes. Christoffersen test
were applied for the evaluation criteria. In terms of the
VaR conditional coverage, empirical evidences indicate that
the SV model via MCL estimation is as efficient as the
GARCH (1,1) model.
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Importance sampling on the coalescent with recombinationJenkins, Paul A. January 2008 (has links)
Performing inference on contemporary samples of homologous DNA sequence data is an important task. By assuming a stochastic model for ancestry, one can make full use of observed data by sampling from the distribution of genealogies conditional upon the sample configuration. A natural such model is Kingman's coalescent, with numerous extensions to account for additional biological phenomena. However, in this model the distribution of interest cannot be written down analytically, and so one solution is to utilize importance sampling. In this context, importance sampling (IS) simulates genealogies from an artificial proposal distribution, and corrects for this by weighting each resulting genealogy. In this thesis I investigate in detail approaches for developing efficient proposal distributions on coalescent histories, with a particular focus on a two-locus model mutating under the infinite-sites assumption and in which the loci are separated by a region of recombination. This model was originally studied by Griffiths (1981), and is a useful simplification for considering the correlated ancestries of two linked loci. I show that my proposal distribution generally outperforms an existing IS method which could be recruited to this model. Given today's sequencing technologies it is not difficult to find volumes of data for which even the most efficient proposal distributions might struggle. I therefore appropriate resampling mechanisms from the theory of sequential Monte Carlo in order to effect substantial improvements in IS applications. In particular, I propose a new resampling scheme and confirm that it ensures a significant gain in the accuracy of likelihood estimates. It outperforms an existing scheme which can actually diminish the quality of an IS simulation unless it is applied to coalescent models with care. Finally, I apply the methods developed here to an example dataset, and discuss a new measure for the way in which two gene trees are correlated.
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Applications of Advanced Time Series Models to Analyze the Time-varying Relationship between Macroeconomics, Fundamentals and Pan-European Industry Portfolios / Anwendungen moderner Zeitreihenverfahren zur Analyse zeitvariabler Zusammenhänge zwischen gesamtwirtschaftlichen Entwicklungen, Fundamentaldaten und europäischen BranchenportfoliosMergner, Sascha 04 March 2008 (has links)
No description available.
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