Center for Computational Finance and Economic Systems (CoFES)
Recent Submissions
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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 ... -
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 ... -
Denoising Non-stationary Signals by Dynamic Multivariate Complex Wavelet Thresholding
(2020)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. Wavelets have ... -
Multivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatility
(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 ... -
An Examination of Some Open Problems in Time Series Analysis
(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
(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. -
Estimating Marginal Survival in the Presence of Dependent and Independent Censoring: With Applications to Dividend Initiation Policy
(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 ... -
Covariance Estimation in Dynamic Portfolio Optimization: A Realized Single Factor Model*
(2009)Realized covariance estimation for large dimension problems is little explored and poses challenges in terms of computational burden and estimation error. In a global minimum volatility setting, we investigate the performance of covariance conditioning techniques applied to the realized covariance matrices of the 30 DJIA stocks. We find that not only ... -
Estimating the Term Structure With a Semiparametric Bayesian Hierarchical Model: An Application to Corporate Bonds
(2011)The term structure of interest rates is used to price defaultable bonds and credit derivatives, as well as to infer the quality of bonds for risk management purposes. We introduce a model that jointly estimates term structures by means of a Bayesian hierarchical model with a prior probability model based on Dirichlet process mixtures. The modeling ... -
Computational finance: correlation, volatility, and markets
(2014)Financial data by nature are inter-related and should be analyzed using multivariate methods. Many models exist for the joint analysis of multiple financial instruments. Early models often assumed some type of constant behavior between the instruments over the time period of analysis. But today, time-varying covariance models are a key component of ...
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