Center for Computational Finance and Economic Systems (CoFES)

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Browsing Center for Computational Finance and Economic Systems (CoFES) by Author "Center for Computational Finance and Economic Systems"

Now showing 1 - 7 of 7
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    Beating the House: Identifying Inefficiencies in Sports Betting Markets
    (arXiv, 2019) Ramesh, Sathya; Mostofa, Ragib; Bornstein, Marco; Dobelman, John; Center for Computational Finance and Economic Systems
    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, and created a non-parametric win probability model to find positive expected value situations. As the United States Supreme Court has recently repealed the federal ban on sports betting, research on sports betting markets is increasingly relevant for the growing sports betting industry.
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    Discussion on an approach for identifying and predicting economic recessions in real-time using time-frequency functional models
    (Wiley, 2012) Ensor, Katherine B.; Center for Computational Finance and Economic Systems
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    Dynamic jump intensities and news arrival in oil futures markets
    (Springer Nature, 2020) Ensor, Katherine B.; Han, Yu; Ostdiek, Barbara; Turnbull, Stuart M.; Center for Computational Finance and Economic Systems
    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 an asymmetric effect on oil futures returns and volatility. Using the first 12 futures contracts, our empirical results confirm that the effects of negative and positive news are described by different processes, a significant proportion of volatility is explained by news arrival and the impact of negative news is larger than that of positive news.
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    Effect on Prediction When Modeling Covariates in Bayesian Nonparametric Models
    (Springer Nature, 2013) Cruz-Marcelo, Alejandro; Rosner, Gary L.; Müller, Peter; Stewart, Clinton F.; Center for Computational Finance and Economic Systems
    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 literature for extending Bayesian nonparametric models to include dependence on covariates. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.
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    Enterprise and Political Risk Management in Complex Systems
    (International Research Center for Energy and Economic Development, 2007) Ensor, Katherine B.; Kyj, Lada; Marfin, Gary C.; Center for Computational Finance and Economic Systems
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    High-Dimensional Multivariate Time Series With Additional Structure
    (Taylor & Francis, 2017) Schweinberger, Michael; Babkin, Sergii; Ensor, Katherine B.; Center for Computational Finance and Economic Systems
    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 common form of additional structure. We propose novel high-dimensional methods that take advantage of such structure without making model assumptions about how distance affects dependence. We provide nonasymptotic bounds on the statistical error of parameter estimators in high-dimensional settings and show that the proposed approach reduces the statistical error. An application to air pollution in the USA demonstrates that the estimation approach reduces both computing time and prediction error and gives rise to results that are meaningful from a scientific point of view, in contrast to high-dimensional methods that ignore spatial structure. In practice, these high-dimensional methods can be used to decompose high-dimensional multivariate time series into lower-dimensional multivariate time series that can be studied by other methods in more depth.
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    Modeling Covariates with Nonparametric Bayesian Methods
    (SSRN, 2010) Cruz-Marcelo, Alejandro; Rosner, Gary L.; Mueller, Peter; Stewart, Clinton; Center for Computational Finance and Economic Systems
    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 in order to produce better results. This article proposes answers to those questions focusing on predictive inference and continuous covariates. Specifically, we show that 1) nonparametric models using different strategies for modeling continuous covariates can show noteworthy differences when they are being used for prediction, even though they produce otherwise similar posterior inference results, and 2) when the predictive density is a mixture, it is convenient to make the weights depend on the covariates in order to produce sensible estimators. Such claims are supported by comparing the Linear DDP (an extension of the Sethuraman representation) and the Conditional DP (which augments the nonparametric distribution to include the covariates). Unlike the Conditional DP, the weights in the predictive mixture density of the Linear DDP are not covariate-dependent. This results in poor estimators of the predictive density. Specifically, in a simulation example, the Linear DDP wrongly introduces an additional mode into the predictive density, while in an application to a pharmacokinetic study, it produces unrealistic concentration-time curves.