Bayesian Semiparametric Stochastic Volatility Modeling

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and kurtosis of the distribution while the dynamics of volatility continue to be modeled with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. A Markov chain Monte Carlo sampling approach to estimation is presented with theoretical and computational issues for simulation from the posterior predictive distributions. An empirical example compares the new model to standard parametric stochastic volatility modelsClassification-JEL:

Bayesian semiparametric stochastic volatility modeling

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, we use nonparametric Bayesian methods to flexibly model the skewness and kurtosis of the distribution while continuing to model the dynamics of volatility with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. We present a Markov chain Monte Carlo sampling approach to estimation with theoretical and computational issues for simulation from the posterior predictive distributions. The new model is assessed based on simulation evidence, an empirical example, and comparison to parametric models.Econometric models ; Stochastic analysis

Bayesian semiparametric stochastic volatility modeling

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and kurtosis of the distribution while the dynamics of volatility continue to be modeled with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. A Markov chain Monte Carlo sampling approach to estimation is presented with theoretical and computational issues for simulation from the posterior predictive distributions. The new model is assessed based on simulation evidence, an empirical example, and comparison to parametric models.Dirichlet process mixture, MCMC, block sampler

Discovering New Selves: Service-Learning and the Intellectual Development of College Students

The purpose of this study was to explore college students\u27 intellectual development through their service-learning experience. This study also took into consideration the characteristics of student groups and the way in which they transformed intellectually through their service-learning experience. To examine these questions, twelve upper-division college students who had completed a service-learning course were interviewed, in order to capture the dynamics of their service-learning experiences, their perceptions of their intellectual development, and their values and priorities as college students in detail. From the interviews, five major themes related to college students\u27 intellectual development emerged. Three of the themes focused on the interpersonal capacities and complexities of intellectual development, and two were related to the complexity and challenges of unstructured problems related to service-learning and college students\u27 intellectual growth. In addition, by analyzing the themes and the characteristics of student groups together, I coined new terms to capture the intellectual transformation of modern-day college students who participate in service-learning. The findings of this study will add to the understanding of college students\u27 intellectual development through service-learning, as well as how students transformed through the experience, and provide opportunities for future research to investigate specific groups of college students in this and other collegiate settings

Bayesian Nonparametric Estimation of Ex Post Variance

Variance estimation is central to many questions in finance and economics. Until now ex post variance estimation has been based on infill asymptotic assumptions that exploit high-frequency data. This article offers a new exact finite sample approach to estimating ex post variance using Bayesian nonparametric methods. In contrast to the classical counterpart, the proposed method exploits pooling over high-frequency observations with similar variances. Bayesian nonparametric variance estimators under no noise, heteroskedastic and serially correlated microstructure noise are introduced and discussed. Monte Carlo simulation results show that the proposed approach can increase the accuracy of variance estimation. Applications to equity data and comparison with realized variance and realized kernel estimators are included

Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis

The relationship between risk and return is one of the most studied topics in finance. The majority of the literature is based on a linear, parametric relationship between expected returns and conditional volatility. However, there is no theoretical justification for the relationship to be linear. This paper models the contemporaneous relationship between market excess returns and log-realized variances nonparametrically with an infinite mixture representation of their joint distribution. With this nonparametric representation, the conditional distribution of excess returns given log-realized variance will also have a infinite mixture representation but with probabilities and arguments depending on the value of realized variance. Our nonparametric approach allows for deviation from Gaussianity by allowing for higher order non-zero moments. It also allows for a smooth nonlinear relationship between the conditional mean of excess returns and log-realized variance. Parsimony of our nonparametric approach is guaranteed by the almost surely discrete Dirichlet process prior used for the mixture weights and arguments. We find strong robust evidence of volatility feedback in monthly data. Once volatility feedback is accounted for, there is an unambiguous positive relationship between expected excess returns and expected log-realized variance. This relationship is nonlinear. Volatility feedback impacts the whole distribution and not just the conditional mean

Estimating a Semiparametric Asymmetric Stochastic Volatility Model with a Dirichlet Process Mixture

Abstract. This paper extends the stochastic volatility with leverage model, where returns are correlated with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparamet-rically. The novelty of the paper is in modeling the unknown distribution with an infinite ordered mixture of bivariate normals with mean zero, but whose mixture probabilities and covariance matrices are unknown and modeled with the Dirichlet Process prior. A Bayesian Markov chain Monte Carlo sampler is designed to fully characterize the parametric and distributional uncertainty. Cumulative marginal likelihoods and log predictive Bayes factors for the semiparametric and parametric asymmetric stochastic volatility models are compared. We find substantial empirical evidence in favor of the semiparametric leverage version of the stochastic volatility model

Plasmonically Enhanced Reflectance of Heat Radiation from Low-Bandgap Semiconductor Microinclusions

Increased reflectance from the inclusion of highly scattering particles at low volume fractions in an insulating dielectric offers a promising way to reduce radiative thermal losses at high temperatures. Here, we investigate plasmonic resonance driven enhanced scattering from microinclusions of low-bandgap semiconductors (InP, Si, Ge, PbS, InAs and Te) in an insulating composite to tailor its infrared reflectance for minimizing thermal losses from radiative transfer. To this end, we compute the spectral properties of the microcomposites using Monte Carlo modeling and compare them with results from Fresnel equations. The role of particle size-dependent Mie scattering and absorption efficiencies, and, scattering anisotropy are studied to identify the optimal microinclusion size and material parameters for maximizing the reflectance of the thermal radiation. For composites with Si and Ge microinclusions we obtain reflectance efficiencies of 57 - 65% for the incident blackbody radiation from sources at temperatures in the range 400 - 1600 {\deg}C. Furthermore, we observe a broadbanding of the reflectance spectra from the plasmonic resonances due to charge carriers generated from defect states within the semiconductor bandgap. Our results thus open up the possibility of developing efficient high-temperature thermal insulators through use of the low-bandgap semiconductor microinclusions in insulating dielectrics.Comment: Main article (8 Figures and 2 Tables) + Supporting Information (8 Figures

It Is the time to think about a treat-to-target strategy for knee osteoarthritis

Osteoarthritis (OA) is a rheumatic disease that affects the well-being of the patient, compromises physical and mental function, and affects other quality of life aspects. In the literature, several evidence-based guidelines and recommendations for the management of knee osteoarthritis (KOA) are available. These recommendations list the different therapeutic options rather than addressing a hierarchy between the treatments and defining the real target. Therefore, a question arises: are patients and physicians satisfied with the current management of KOA? Actually, the answer may be negative, thus suggesting a change in our therapeutic strategies. In this article, we address this challenge by suggesting that it is time to develop a “treat to target strategy” for KO