Activity Number:
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701
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Marketing
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Abstract #318766
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Title:
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Weighted Dirichlet Process Mixture GARCH Model for Predicting Stock Price
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Author(s):
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Inyoung Kim and Peng Sun*
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Companies:
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Virginia Tech and Virginia Tech
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Keywords:
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Back Testing ;
Nonparametric Bayesian Model ;
Weighted Dirichlet process
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Abstract:
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In this paper we propose a flexible nonparametric Bayesian approach to analyze Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model. Our model flexibility is obtained from relaxing the assumption that prior distribution of error term parameters is the same for every observation. Introducing weighted Dirichlet process mixture (WDPM), we can provide multiple candidate priors and let observations to favor different candidates. We model such difference by carefully chosen explanatory covariates and explore different ways of constructing weights. Since GARCH models the log return, it enables us to predict stock price. We develop an empirical way of model evaluation by taking the complexity of real stock trading into consideration and refer it to the Back Testing Return approach. This Back Testing Return approach simulates the real process of stock price prediction and help us to provide decision making. We have conducted large number of model comparisons among parametric and nonparametric Bayesian approaches of stochastic volatility model and nonparametric Bayesian approaches of stochastic volatility model and GARCH using marginal likelihood and using our approach.
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Authors who are presenting talks have a * after their name.