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

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

Past dynamics of HIV transmission among men who have sex with men in Montréal, Canada: a mathematical modeling study

BACKGROUND: Gay, bisexual, and other men who have sex with men (gbMSM) experience disproportionate risks of HIV acquisition and transmission. In 2017, Montréal became the first Canadian Fast-Track City, setting the 2030 goal of zero new HIV infections. To inform local elimination efforts, we estimate the evolving role of prevention and sexual behaviours on HIV transmission dynamics among gbMSM in Montréal between 1975 and 2019. METHODS: Data from local bio-behavioural surveys were analyzed to develop, parameterize, and calibrate an agent-based model of sexual HIV transmission. Partnership dynamics, HIV's natural history, and treatment and prevention strategies were considered. The model simulations were analyzed to estimate the fraction of HIV acquisitions and transmissions attributable to specific groups, with a focus on age, sexual partnering level, and gaps in the HIV care-continuum. RESULTS: The model-estimated HIV incidence peaked in 1985 (2.3 per 100 person years (PY); 90% CrI: 1.4-2.9 per 100 PY) and decreased to 0.1 per 100 PY (90% CrI: 0.04-0.3 per 100 PY) in 2019. Between 2000-2017, the majority of HIV acquisitions and transmissions occurred among men aged 25-44 years, and men aged 35-44 thereafter. The unmet prevention needs of men with > 10 annual anal sex partners contributed 90-93% of transmissions and 67-73% of acquisitions annually. The primary stage of HIV played an increasing role over time, contributing to 11-22% of annual transmissions over 2000-2019. In 2019, approximately 70% of transmission events occurred from men who had discontinued, or never initiated antiretroviral therapy. CONCLUSIONS: The evolving HIV landscape has contributed to the declining HIV incidence among gbMSM in Montréal. The shifting dynamics identified in this study highlight the need for continued population-level surveillance to identify gaps in the HIV care continuum and core groups on which to prioritize elimination efforts

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

The association between heterosexual anal intercourse and HIV acquisition in three prospective cohorts of women

The extent to which receptive anal intercourse (RAI) increases the HIV acquisition risk of women compared to receptive vaginal intercourse (RVI) is poorly understood. We evaluated RAI practice over time and its association with HIV incidence during three prospective HIV cohorts of women: RV217, MTN-003 (VOICE), and HVTN 907. At baseline, 16% (RV 217), 18% (VOICE) of women reported RAI in the past 3 months and 27% (HVTN 907) in the past 6 months, with RAI declining during follow-up by around 3-fold. HIV incidence in the three cohorts was positively associated with reporting RAI at baseline, albeit not always significantly. The adjusted hazard rate ratios for potential confounders (aHR) were 1.1 (95% Confidence interval: 0.8-1.5) for VOICE and 3.3 (1.6-6.8) for RV 217, whereas the ratio of cumulative HIV incidence by RAI practice was 1.9 (0.6-6.0) for HVTN 907. For VOICE, the estimated magnitude of association increased slightly when using a time-varying RAI exposure definition (aHR = 1.2; 0.9-1.6), and for women reporting RAI at every follow-up survey (aHR = 2.0 (1.3-3.1)), though not for women reporting higher RAI frequency (> 30% acts being RAI vs. no RAI in the past 3 months; aHR = 0.7 (0.4-1.1)). Findings indicated precise estimation of the RAI/HIV association, following multiple RVI/RAI exposures, is sensitive to RAI exposure definition, which remain imperfectly measured. Information on RAI practices, RAI/RVI frequency, and condom use should be more systematically and precisely recorded and reported in studies looking at sexual behaviors and HIV seroconversions; standardized measures would aid comparability across geographies and over time

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