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Activity Number: 676
Type: Invited
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract #318179 View Presentation
Title: Bayesian Modeling of High-Frequency Crude Oil Prices
Author(s): Jonathan Stroud* and Michael Johannes and Norman Seeger
Companies: Georgetown University and Columbia University and VU University
Keywords: Stochastic Volatility ; Particle Filter ; Markov chain Monte Carlo ; GARCH ; Realized Volatility ; State Space Model

We propose a new class of stochastic volatility models for around-the-clock 5-minute returns on crude oil prices. Our models incorporate the following key features: multiple persistent stochastic volatility factors, jumps in prices and volatilities, seasonal components capturing time of the day patterns, correlations between return and volatility shocks, and announcement effects. We develop an integrated MCMC approach to estimate interday and intraday parameters and states using high-frequency data without resorting to various aggregation measures like realized volatility. We provide a case study using data from 2008 to 2015, and use particle filters to construct likelihood functions for model comparison and out-of-sample forecasting. We show that our approach improves realized volatility forecasts over existing benchmarks like intraday GARCH models and interday realized volatility models.

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