Bayesian modeling and forecasting of 24-hour high-frequency volatility: A case study of the financial crisis

This paper estimates models of high frequency index futures returns using `around the clock' 5-minute returns that 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 financial crisis data from 2007 to 2009, and use particle filters to construct likelihood functions for model comparison and out-of-sample forecasting from 2009 to 2012. We show that our approach improves realized volatility forecasts by up to 50% over existing benchmarks.Comment: 48 pages, 7 figure

A Thousand Tiny Pieces: The Federal Circuit\u27s Fractured \u27Myriad\u27 Ruling, Lessons to Be Learned, and the Way Forward

The Supreme Court granted, vacated, and remanded the infamous Myriad gene isolation patentability case to the Court of Appeals for the Federal Circuit (CAFC) in light of the recent Prometheus decision, which held 9-0 that a certain diagnostic method was invalid subject matter because it was an abstract idea merely modified by other obvious steps. This Essay argues that Myriad should be affirmed again by the Federal Circuit, particularly in light of Prometheus, in order to inject certainty, clarity, and consistency into the § 101 patentable subject matter jurisprudenc

Understanding Human Actions in Video

Understanding human behavior is crucial for any autonomous system which interacts with humans. For example, assistive robots need to know when a person is signaling for help, and autonomous vehicles need to know when a person is waiting to cross the street. However, identifying human actions in video is a challenging and unsolved problem. In this work, we address several of the key challenges in human action recognition. To enable better representations of video sequences, we develop novel deep learning architectures which improve representations both at the level of instantaneous motion as well as at the level of long-term context. In addition, to reduce reliance on fixed action vocabularies, we develop a compositional representation of actions which allows novel action descriptions to be represented as a sequence of sub-actions. Finally, we address the issue of data collection for human action understanding by creating a large-scale video dataset, consisting of 70 million videos collected from internet video sharing sites and their matched descriptions. We demonstrate that these contributions improve the generalization performance of human action recognition systems on several benchmark datasets.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162887/1/stroud_1.pd

A Thousand Tiny Pieces: The Federal Circuit\u27s Fractured \u27Myriad\u27 Ruling, Lessons to Be Learned, and the Way Forward

The Supreme Court granted, vacated, and remanded the infamous Myriad gene isolation patentability case to the Court of Appeals for the Federal Circuit (CAFC) in light of the recent Prometheus decision, which held 9-0 that a certain diagnostic method was invalid subject matter because it was an abstract idea merely modified by other obvious steps. This Essay argues that Myriad should be affirmed again by the Federal Circuit, particularly in light of Prometheus, in order to inject certainty, clarity, and consistency into the § 101 patentable subject matter jurisprudenc

Ensemble Kalman methods for high-dimensional hierarchical dynamic space-time models

We propose a new class of filtering and smoothing methods for inference in high-dimensional, nonlinear, non-Gaussian, spatio-temporal state-space models. The main idea is to combine the ensemble Kalman filter and smoother, developed in the geophysics literature, with state-space algorithms from the statistics literature. Our algorithms address a variety of estimation scenarios, including on-line and off-line state and parameter estimation. We take a Bayesian perspective, for which the goal is to generate samples from the joint posterior distribution of states and parameters. The key benefit of our approach is the use of ensemble Kalman methods for dimension reduction, which allows inference for high-dimensional state vectors. We compare our methods to existing ones, including ensemble Kalman filters, particle filters, and particle MCMC. Using a real data example of cloud motion and data simulated under a number of nonlinear and non-Gaussian scenarios, we show that our approaches outperform these existing methods