Theses and Dissertations
Recent Submissions
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Financial time series forecasting via RNNs and Wavelet Analysis
(2022-04-22)Recent successes in both Artificial Neural Networks (ANN) and wavelets have placed these two methods in the spotlight of quantitative traders seeking the best tool to forecast financial time series. The Wavelet Neural Network (W-NN), a prediction model which combines wavelet-based denoising and ANN, has successfully combined the two strategies in ... -
Topological Data Analysis and theoretical statistical inference for time series dependent data and error in parametric choices
(2022-07-14)Topological data analysis extracts topological features by examining the shape of the data through persistent homology to produce topological summaries, such as the persistence landscape. While the persistence landscape makes it easier to conduct statistical analysis, the Strong Law of Large Numbers and a Central Limit Theorem for the persistence ... -
Two Random Walk Problems
(2022-04-22)Among numerous probabilistic objects, random walk is one of the most fundamental but most favourable. This dissertation concerns two problems related to random walk theory. The first problem regards $d$ independent Bernoulli random walks. We investigate the first “rencontre-time” (i.e. the first time all of the $d$ Bernoulli random walks arrive in ... -
Feature Learning and Bayesian Functional Regression for High-Dimensional Complex Data
(2021-12-02)In recent years, technological innovations have facilitated the collection of complex, high-dimensional data that pose substantial modeling challenges. Most of the time, these complex objects are strongly characterized by internal structure that makes sparse representations possible. If we can learn a sparse set of features that accurately captures ... -
Spatiotemporal Extreme Value Modeling with Environmental Applications
(2021-10-06)Extreme value analysis (EVA) is essential to evaluate the extreme events brought on by natural hazards in the environment. Particularly, EVA informs risk assessment for communities, which is crucial to protecting people and property. This work focuses on an application to extreme rainfall in the Houston, TX region and Harris County, and performs ... -
Computational and Statistical Methodology for Highly Structured Data
(2020-09-15)Modern data-intensive research is typically characterized by large scale data and the impressive computational and modeling tools necessary to analyze it. Equally important, though less remarked upon, is the important structure present in large data sets. Statistical approaches that incorporate knowledge of this structure, whether spatio-temporal ... -
Dynamic Multivariate Wavelet Signal Extraction and Forecasting with Applications to Finance
(2020-04-16)Over the past few years, we have seen an increased need for analyzing the dynamically changing behaviors of economic and financial time series. These needs have led to significant demand for methods that denoise non-stationary time series across time and for specific investment horizons (scales) and localized windows (blocks) of time. This thesis ... -
Filtering and Estimation for a Class of Stochastic Volatility Models with Intractable Likelihoods
(2015-08-28)A new approach to state filtering and parameter estimation for a class of stochastic volatility models for which the likelihood function is unknown is considered. The alpha-stable stochastic volatility model provides a flexible framework for modeling asymmetry and heavy tails, which is useful when modeling financial returns. However, a problem posed ... -
On longest consecutive patterns in Markov chains
(2019-11-11)The length of longest consecutive head in Bernoulli trials L(n) has been studied extensively and has been found applications in biology, finance and non-parametric statistics. The study of longest consecutive successes in random trials dates the work of de Moivre. Limiting theorems and large deviation results are provided for L(n) with the assumption ... -
Essays on Crude Oil Markets and Electricity Access
(2019-05-13)In the first chapter I discuss how OPEC's internal costs restrict their ability collude. Where membership in 2007 was anchored by three large, low-cost producers in Iran, Venezuela and Saudi Arabia, by 2015 Venezuela and Iran were no longer large producers due to lack of investment and sanctions, respectively. This left Saudi Arabia and Iraq as the ... -
An Old Dog Learns New Tricks: Novel Applications of Kernel Density Estimators on Two Financial Datasets
(2017-12-01)In our first application, we contribute two nonparametric simulation methods for analyzing Leveraged Exchange Traded Fund (LETF) return volatility and how this dynamic is related to the underlying index. LETFs are constructed to provide the indicated leverage multiple of the daily total return on an underlying index. LETFs may perform as expected ... -
Dynamic Characterization of Multivariate Time Series
(2017-12-01)The standard non-negative matrix factorization focuses on batch learning assuming that the fixed global latent parameters completely describe the observations. Many online extensions assume rigid constraints and smooth continuity in observations. However, the more complex time series processes can have multivariate distributions switch between a ... -
Robust Discriminant Analysis and Clustering by a Partial Minimum Integrated Squared Error Criterion
(2017-08-10)In parametric supervised classification and unsupervised clustering traditional methods are often inadequate when data are generated under departures from normality assumptions. A class of density power divergences was introduced by Basu et al. (1998) to alleviate these problems. This class of estimators is indexed by a parameter α which balances ... -
Characterizing Production in the Barnett Shale Resource: Essays on Efficiency, Operator Effects and Well Decline
(2016-04-21)This dissertation is composed of three papers in the field of energy economics. The first paper estimates revenue and technical efficiency for more than 11,000 wells that were drilled in the Barnett between 2000 and 2010, and also examines how the efficiency estimates differ among operators. To achieve this objective, we use stochastic frontier ... -
Impact of News on Crude Oil Futures
(2017-04-21)Crude oil futures are worlds the most actively traded commodity futures, with more than 3 billion barrels per year in open interest. In this thesis we use related news to model the price dynamics of oil futures. We examine the empirical patterns of oil market news data processed by Thompson Reuters News Analytics, plus the intraday trading data of ... -
Approximate dynamic factor models for mixed frequency data
(2015-10-15)Time series observed at different temporal scales cannot be simultaneously analyzed by traditional multivariate time series methods. Adjustments must be made to address issues of asynchronous observations. For example, many macroeconomic time series are published quarterly and other price series are published monthly or daily. Common solutions to the ... -
Robust Methods for Forecast Aggregation
(2014-08-18)This study introduces a new forecast aggregation technique. Adding to the well- known difficulties and uncertainty involved in the forecasting process, the aggregation of hundreds or thousands of forecasters’ opinions and expert predictions on social, economical and political matters makes the process even more difficult. Simple quan- titative data ... -
Identifying and Dealing with the Approach of Bears and their Departure
(2013-05-29)Based on the identification of market dynamics, capital allocation in long positions can be dynamically controlled by means of interrupting an otherwise strictly-long investment strategy allowing for an overall improved risk profile and faster response times during periods of persistent negative market returns. Herein, a portfolio selection methodology ... -
Robust GARCH methods and analysis of partial least squares regression
(2014-04-24)New approaches to modeling volatility are evaluated and properties of partial least squares (PLS) regression are investigated. Common methods for modeling volatility, the standard deviation of price changes over a period, that account for the heavy tails of asset returns rely on maximum likelihood estimation using a heavy-tailed distribu- tion. A ... -
Robust Parametric Functional Component Estimation Using a Divergence Family
(2013-09-16)The classical parametric estimation approach, maximum likelihood, while providing maximally efficient estimators at the correct model, lacks robustness. As a modification of maximum likelihood, Huber (1964) introduced M-estimators, which are very general but often ad hoc. Basu et al. (1998) developed a family of density-based divergences, many of ...
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