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Sequential Bayesian Learning for Merton's Jump Model with Stochastic Volatility
Eric Jacquier, Nicholas Polson, Vadim Sokolov
Jump stochastic volatility models are central to financial econometrics for volatility forecasting, portfolio risk management, and derivatives pricing. Markov Chain Monte Carlo (MC…
Regularizing Bayesian Predictive Regressions
Guanhao Feng, Nicholas G. Polson
We show that regularizing Bayesian predictive regressions provides a framework for prior sensitivity analysis. We develop a procedure that jointly regularizes expectations and vari…
Deep Portfolio Theory
J. B. Heaton, N. G. Polson, J. H. Witte
We construct a deep portfolio theory. By building on Markowitz's classic risk-return trade-off, we develop a self-contained four-step routine of encode, calibrate, validate and ver…
Prediction risk for the horseshoe regression
Anindya Bhadra, Jyotishka Datta, Yunfan Li +2
We show that prediction performance for global-local shrinkage regression can overcome two major difficulties of global shrinkage regression: (i) the amount of relative shrinkage i…
Global-Local Mixtures
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
Global-local mixtures are derived from the Cauchy-Schlomilch and Liouville integral transformation identities. We characterize well-known normal-scale mixture distributions includi…
Deep Learning for Short-Term Traffic Flow Prediction
Nicholas Polson, Vadim Sokolov
We develop a deep learning model to predict traffic flows. The main contribution is development of an architecture that combines a linear model that is fitted using regula…