Recent Submissions

  • A Bayesian Multivariate Functional Dynamic Linear Model 

    Kowal, Daniel R.; Matteson, David S.; Ruppert, David (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 

    Brownlees, Christian T.; Vannucci, Marina (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 ...
  • Enterprise and Political Risk Management in Complex Systems 

    Ensor, Katherine B.; Kyj, Lada; Marfin, Gary C.; Center for Computational Finance and Economic Systems (2007)
  • Beating the House: Identifying Inefficiencies in Sports Betting Markets 

    Ramesh, Sathya; Mostofa, Ragib; Bornstein, Marco; Dobelman, John; Center for Computational Finance and Economic Systems (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, ...
  • Effect on Prediction When Modeling Covariates in Bayesian Nonparametric Models 

    Cruz-Marcelo, Alejandro; Rosner, Gary L.; Müller, Peter; Stewart, Clinton F.; Center for Computational Finance and Economic Systems (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 ...
  • Modeling Covariates with Nonparametric Bayesian Methods 

    Cruz-Marcelo, Alejandro; Rosner, Gary L.; Mueller, Peter; Stewart, Clinton; Center for Computational Finance and Economic Systems (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 ...
  • Discussion on an approach for identifying and predicting economic recessions in real-time using time-frequency functional models 

    Ensor, Katherine B.; Center for Computational Finance and Economic Systems (2012)
  • High-Dimensional Multivariate Time Series With Additional Structure 

    Schweinberger, Michael; Babkin, Sergii; Ensor, Katherine B.; Center for Computational Finance and Economic Systems (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 ...
  • Dynamic jump intensities and news arrival in oil futures markets 

    Ensor, Katherine B.; Han, Yu; Ostdiek, Barbara; Turnbull, Stuart M.; Center for Computational Finance and Economic Systems (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 ...
  • Filtering and Estimation for a Class of Stochastic Volatility Models with Intractable Likelihoods 

    Vankov, Emilian R.; Guindani, Michele; Ensor, Katherine B. (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 

    Zohner, Ye Emma M (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 

    Fagnant, Carlynn (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 

    Ensor, Katherine; Elsesser, George (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 

    Ensor, Katherine; Dobelman, John A.; Aiman, Jared; Iglesias, Vicente; Sarkar, Sumit (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 

    Weylandt, Michael (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 ...
  • Estimating Marginal Survival in the Presence of Dependent and Independent Censoring: With Applications to Dividend Initiation Policy 

    Fix, Gretchen Abigail (2005)
    In many survival analysis settings, the assumption of non-informative (i.e. independent) censoring is not valid. Zheng and Klein (1995, 1996) develop a copula-based method for estimating the marginal survival functions of bivariate dependent competing risks data. We expand upon this earlier work and adapt their method to data in which there are three ...
  • An Examination of Some Open Problems in Time Series Analysis 

    Davis, Ginger Michelle (2005)
    We investigate two open problems in the area of time series analysis. The first is developing a methodology for multivariate time series analysis when our time series has components that are both continuous and categorical. Our specific contribution is a logistic smooth transition regression (LSTR) model whose transition variable is related to a ...
  • WRDS Index Data Extraction Methodology 

    Dobelman, J.A.; Kang, H.B.; Park, S.W. (2014)
    This paper provides and validates an automatic procedure to generate accurate CRSP PERMNOs from Compustat GVKEYs for historical index constituents. We then validate the resulting PERMNO lists and examine some of the many pitfalls in other attempts to accomplish this, and provide cautionary guidance for WRDS index data researchers.
  • Time-varying wavelet-based applications for evaluating the Water-Energy Nexus 

    Raath, Kim C.; Ensor, Katherine B. (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 ...
  • Multivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatility 

    Weylandt, Michael; Han, Yu; Ensor, Katherine B. (2019)
    Financial markets for Liquified Natural Gas (LNG) are an important and rapidly-growing segment of commodities markets. Like other commodities markets, there is an inherent spatial structure to LNG markets, with different price dynamics for different points of delivery hubs. Certain hubs support highly liquid markets, allowing efficient and robust ...

View more