Center for Computational Finance and Economic Systems (CoFES)
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Prediction of WilderHill Clean Energy Index Directional Movement
(2023-05-08)The popularity of clean energy has risen recently due to concerns about climate change and the exhaustion of traditional energy sources. The stock price of clean energy companies reflects the public’s attention to the industry’s growth potential, and clean energy stocks are among the riskiest stocks to invest in. Thus, it is important to apply ... -
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 ... -
A Bayesian Multivariate Functional Dynamic Linear Model
(2017)We present a Bayesian approach for modeling multivariate, dependent functional data. To account for the three dominant structural features in the data—functional, time dependent, and multivariate components—we extend hierarchical dynamic linear models for multivariate time series to the functional data setting. We also develop Bayesian spline theory ... -
A Bayesian approach for capturing daily heterogeneity in intra-daily durations time series
(2013)Intra-daily financial durations time series typically exhibit evidence of long range dependence. This has motivated the introduction of models able to reproduce this stylized fact, like the Fractionally Integrated Autoregressive Conditional Duration Model. In this work we introduce a novel specification able to capture long range dependence. We propose ... -
Beating the House: Identifying Inefficiencies in Sports Betting Markets
(2019)Inefficient markets allow investors to consistently outperform the market. To demonstrate that inefficiencies exist in sports betting markets, we created a betting algorithm that generates above market returns for the NFL, NBA, NCAAF, NCAAB, and WNBA betting markets. To formulate our betting strategy, we collected and examined a novel dataset of bets, ... -
Modeling Covariates with Nonparametric Bayesian Methods
(2010)A research problem that has received increased attention in recent years is extending Bayesian nonparametric methods to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. First, analyzing how the performance of such extensions differs, and second, understanding which features are worthwhile ... -
Effect on Prediction When Modeling Covariates in Bayesian Nonparametric Models
(2013)In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparamric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the ... -
Dynamic jump intensities and news arrival in oil futures markets
(2020)We introduce a new class of discrete-time models that explicitly recognize the impact of news arrival. The distribution of returns is governed by three factors: dynamics volatility and two Poisson compound processes, one for negative news and one for positive news. We show in a model-free environment that the arrival of negative and positive news has ... -
High-Dimensional Multivariate Time Series With Additional Structure
(2017)High-dimensional multivariate time series are challenging due to the dependent and high-dimensional nature of the data, but in many applications there is additional structure that can be exploited to reduce computing time along with statistical error. We consider high-dimensional vector autoregressive processes with spatial structure, a simple and ... -
Filtering and Estimation for a Class of Stochastic Volatility Models with Intractable Likelihoods
(2019)We introduce a new approach to latent state filtering and parameter estimation for a class of stochastic volatility models (SVMs) for which the likelihood function is unknown. The α-stable stochastic volatility model provides a flexible framework for capturing asymmetry and heavy tails, which is useful when modeling financial returns. However, the ... -
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 ... -
Predicting Student Loan Debt: A Hierarchical Time Series Analysis
(2021)In recent years, and especially in response to the Covid-19 pandemic, much attention has been brought to the issue of rapidly increasing student debt. Yet in the field of time series analysis, there is a dearth of studies examining trends in student loan debt. This is likely due to the impression of simple, yet steep, linear increase in student loan ... -
Modeling SPX Volatility to Improve Options Pricing
(2021)In this project, we develop a model to predict future stock market volatility and facilitate more accurate options pricing. The Black Scholes model gives an expected premium for an options contract; however, it uses an unknown fixed parameter referred to as volatility. We advance this by using a modified Glosten-Jagannathan-Runkle Generalized ... -
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 ... -
Time-varying wavelet-based applications for evaluating the Water-Energy Nexus
(2020)This paper quantifies the rising global dynamic, interconnected relationship between energy and water commodities. Over the last decade, increased international concern has emerged about the water-energy nexus. However, recent research still lacks a quantified understanding of the role of water within a financial-economic view of the nexus. The ...
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