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dc.contributor.advisor Thompson, James R.
dc.creatorLawera, Martin Lukas
dc.date.accessioned 2009-06-04T08:19:17Z
dc.date.available 2009-06-04T08:19:17Z
dc.date.issued 2000
dc.identifier.urihttp://hdl.handle.net/1911/19526
dc.description.abstract We present a series of models capturing the non-stationarities and dependencies in the variance of yields on natural gas futures. Both univariate and multivariate models are explored, based on the ARIMA and Hidden-Markov methodologies. The models capture the effects uncovered through various data mining techniques including seasonality, age and transaction-time effects. Such effect have been previously described in the literature, but never comprehensively captured for the purpose of modeling. In addition, we have investigated the impact of temporal aggregation, by modeling both the daily and the monthly data. The issue of aggregation has not been explored in the current literature that focused on the daily data with uniformly underwhelming results. We have shown that modifications to current models to allow aggregation leads to improvements in performance. This is demonstrated by comparing the proposed models to the models currently used in the financial markets.
dc.format.extent 176 p.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectStatistics
Economics
Finance
dc.title Futures prices: Data mining and modeling approaches
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Statistics
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Doctoral
thesis.degree.name Doctor of Philosophy
dc.identifier.citation Lawera, Martin Lukas. "Futures prices: Data mining and modeling approaches." (2000) Diss., Rice University. http://hdl.handle.net/1911/19526.


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