Long-term equity anticipation securities and stock market volatility dynamics

Recent empirical findings suggest that the long-run dependence in U.S. stock market volatility is best described by a slowly mean-reverting fractionally integrated process. The present study complements this existing time-series-based evidence by comparing the risk-neutralized option pricing distributions from various ARCH-type formulations. Utilizing a panel data set consisting of newly created exchange traded long-term equity anticipation securities, or leaps, on the Standard and Poor's 500 stock market index with maturity times ranging up to three years, we find that the degree of mean reversion in the volatility process implicit in these prices is best described by a Fractionally Integrated EGARCH (FIEGARCH) model. © 1999 Elsevier Science S.A. All rights reserved.

Advances in Dynamic Modeling and Computation for Count Data

A class of multi-process models is developed for collections of time indexed count data. Autocorrelation in counts is achieved with dynamic models for the natural parameter of the binomial distribution. In addition to modeling binomial time series, the framework includes dynamic models for multinomial and Poisson time series. Markov chain Monte Carlo (MCMC) and Po ́lya-Gamma data augmentation (Polson et al., 2013) are critical for fitting multi-process models of counts. To facilitate computation when the counts are high, a Gaussian approximation to the P ́olya- Gamma random variable is developed.

Three applied analyses are presented to explore the utility and versatility of the framework. The first analysis develops a model for complex dynamic behavior of themes in collections of text documents. Documents are modeled as a “bag of words”, and the multinomial distribution is used to characterize uncertainty in the vocabulary terms appearing in each document. State-space models for the natural parameters of the multinomial distribution induce autocorrelation in themes and their proportional representation in the corpus over time.

The second analysis develops a dynamic mixed membership model for Poisson counts. The model is applied to a collection of time series which record neuron level firing patterns in rhesus monkeys. The monkey is exposed to two sounds simultaneously, and Gaussian processes are used to smoothly model the time-varying rate at which the neuron’s firing pattern fluctuates between features associated with each sound in isolation.

The third analysis presents a switching dynamic generalized linear model for the time-varying home run totals of professional baseball players. The model endows each player with an age specific latent natural ability class and a performance enhancing drug (PED) use indicator. As players age, they randomly transition through a sequence of ability classes in a manner consistent with traditional aging patterns. When the performance of the player significantly deviates from the expected aging pattern, he is identified as a player whose performance is consistent with PED use.

All three models provide a mechanism for sharing information across related series locally in time. The models are fit with variations on the P ́olya-Gamma Gibbs sampler, MCMC convergence diagnostics are developed, and reproducible inference is emphasized throughout the dissertation.

Safety-critical medical device development using the UPP2SF model translation tool

Software-based control of life-critical embedded systems has become increasingly complex, and to a large extent has come to determine the safety of the human being. For example, implantable cardiac pacemakers have over 80,000 lines of code which are responsible for maintaining the heart within safe operating limits. As firmware-related recalls accounted for over 41% of the 600,000 devices recalled in the last decade, there is a need for rigorous model-driven design tools to generate verified code from verified software models. To this effect, we have developed the UPP2SF model-translation tool, which facilitates automatic conversion of verified models (in UPPAAL) to models that may be simulated and tested (in Simulink/Stateflow). We describe the translation rules that ensure correct model conversion, applicable to a large class of models. We demonstrate how UPP2SF is used in themodel-driven design of a pacemaker whosemodel is (a) designed and verified in UPPAAL (using timed automata), (b) automatically translated to Stateflow for simulation-based testing, and then (c) automatically generated into modular code for hardware-level integration testing of timing-related errors. In addition, we show how UPP2SF may be used for worst-case execution time estimation early in the design stage. Using UPP2SF, we demonstrate the value of integrated end-to-end modeling, verification, code-generation and testing process for complex software-controlled embedded systems. © 2014 ACM.

The Role of mRNA Translation in the Regulation of Ribosome Trafficking to the Endoplasmic Reticulum

The goal of this research is to identify the trafficking patterns that direct ribosomes to the endoplasmic reticulum (ER). It is widely believed that the SRP pathway is the only mechanism that cells use to localize mRNA and ribosomes to the ER, but this has been found not to be a sufficient explanation for the patterns of RNA localization in cells, namely that non-signal sequence-containing mRNA are translated on the ER and that ribosomes retain their membrane association after translation termination. First, a summary of the history of the field is presented to provide context for the key, unanswered questions in the field. Then, experiments employing [32Pi] pulse-chase labeling of HeLa cells over a time course to follow nascent ribosome trafficking are presented. The purpose of the cell labeling was to track rRNA processing and assembly into nascent ribosomes, followed by their export into the cytoplasm and recruitment into active polysomes. A detergent-based cell fractionation procedure was also utilized to separate the cytosol and ER compartments in order to observe ribosomes on their path as they exit the nucleus and either localize to the ER or cytosolic cellular compartment. Through this method, it was seen that ribosomes appear in both compartments at the same time, suggesting a mechanism may be occurring in addition to SRP-dependent ribosome trafficking. This research provides an understanding toward a mechanism that is not currently known, but will one day more fully explain the patterns of ribosomal localization.

The DLK-1 kinase promotes mRNA stability and local translation in C. elegans synapses and axon regeneration.

Growth cone guidance and synaptic plasticity involve dynamic local changes in proteins at axons and dendrites. The Dual-Leucine zipper Kinase MAPKKK (DLK) has been previously implicated in synaptogenesis and axon outgrowth in C. elegans and other animals. Here we show that in C. elegans DLK-1 regulates not only proper synapse formation and axon morphology but also axon regeneration by influencing mRNA stability. DLK-1 kinase signals via a MAPKAP kinase, MAK-2, to stabilize the mRNA encoding CEBP-1, a bZip protein related to CCAAT/enhancer-binding proteins, via its 3'UTR. Inappropriate upregulation of cebp-1 in adult neurons disrupts synapses and axon morphology. CEBP-1 and the DLK-1 pathway are essential for axon regeneration after laser axotomy in adult neurons, and axotomy induces translation of CEBP-1 in axons. Our findings identify the DLK-1 pathway as a regulator of mRNA stability in synapse formation and maintenance and also in adult axon regeneration.