The Empirical Risk-Return Relation: a factor analysis approach

Financial economists have long been interested in the empirical relation between the conditional mean and conditional volatility of excess stock market returns, often referred to as the risk-return relation. Unfortunately, the body of empirical evidence on the risk-return relation is mixed and inconclusive. A key criticism of the existing empirical literature relates to the relatively small amount of conditioning information used to model the conditional mean and conditional volatility of excess stock market returns. To the extent that financial market participants have information not reflected in the chosen conditioning variables, measures of conditional mean and conditional volatility--and ultimately the risk-return relation itself--will be misspecified and possibly highly misleading. We consider one remedy to these problems using the methodology of dynamic factor analysis for large datasets, whereby a large amount of economic information can be summarized by a few estimated factors. We find that several estimated factors contain important information about one-quarter ahead excess returns and volatility that is not contained in commonly used predictor variables. Moreover, the factor-augmented specifications we examine predict an unusual 16-20 percent of the one-quarter ahead variation in excess stock market returns, and exhibit remarkably stable and strongly statistically significant out-of-sample forecasting power. Finally, in contrast to several pre-existing studies that rely on a small number of conditioning variables, we find a positive conditional correlation between risk and return that is strongly statistically significant, whereas the unconditional correlation is weakly negative and statistically snginficantpredictability, conditioning information, large dimension factor models

Hybridity and Habitation: A Rhetorical Analysis of Interior Design in Live-Action Cyberpunk Films Through the Lens of Posthumanism and Thing Theory

Characterized by hyper-urban environments, extreme class division, and an abundance ofcorporate oversight, the cyberpunk subgenre of science-fiction is a prime candidate for scholarly research on speculative interior design and associated technologies. Using the theoretical frameworks of posthumanism, or the concept that humans will transcend their current biological form in the near future, and thing theory, or the worldview that objects are able to enact their autonomy on living subjects, interior design in three live-action films in the cyberpunk subgenre were analyzed in order to determine how depictions of future spaces reflect present ideations of our potential real future. Metaphor analysis was employed as this thesis’ methodology, as the ideologies implemented in design and technology are often understood through tactile or visual interactions with artifacts. After applying metaphor analysis to one domestic space, one workspace, and one decorative element in each film, it has become evident that in our inevitable posthuman future, human scale and the corporeal form of our species must be taken into account when creating spaces and technologies for us to inhabit and use. Though it may be inaccurate to surmise that design can solve all of the institutional problems that plague societies, there is reason to believe that the inanimate spaces and technologies that occupy our lives do play a role in affecting our psyche and ability to foster healthy relationships

A Factor Analysis of Bond Risk Premia

This paper uses the factor augmented regression framework to analyze the relation between bond excess returns and the macro economy. Using a panel of 131 monthly macroeconomic time series for the sample 1964:1-2007:12, we estimate 8 static factors by the method of asymptotic principal components. We also use Gibb sampling to estimate dynamic factors from the 131 series reorganized into 8 blocks. Regardless of how the factors are estimated, macroeconomic factors are found to have statistically significant predictive power for excess bond returns. We show how a bias correction to the parameter estimates of factor augmented regressions can be obtained. This bias is numerically trivial in our application. The predictive power of real activity for excess bond returns is robust even after accounting for finite sample inference problems. Forecasts of excess bond returns (or bond risk premia) are countercyclical. This implies that investors are compensated for risks associated with recessions.

Percussion Convocation

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Human Metapneumovirus Detection in Patients with Severe Acute Respiratory Syndrome

We used a combination approach of conventional virus isolation and molecular techniques to detect human metapneumovirus (HMPV) in patients with severe acute respiratory syndrome (SARS). Of the 48 study patients, 25 (52.1%) were infected with HMPV; 6 of these 25 patients were also infected with coronavirus, and another 5 patients (10.4%) were infected with coronavirus alone. Using this combination approach, we found that human laryngeal carcinoma (HEp-2) cells were superior to rhesus monkey kidney (LLC-MK2) cells commonly used in previous studies for isolation of HMPV. These widely available HEp-2 cells should be included in conjunction with a molecular method for cell culture followup to detect HMPV, particularly in patients with SARS

Attention bias to emotional faces varies by IQ and anxiety in Williams syndrome

Individuals with Williams syndrome (WS) often experience significant anxiety. A promising approach to anxiety intervention has emerged from cognitive studies of attention bias to threat. To investigate the utility of this intervention in WS, this study examined attention bias to happy and angry faces in individuals with WS (N=46). Results showed a significant difference in attention bias patterns as a function of IQ and anxiety. Individuals with higher IQ or higher anxiety showed a significant bias toward angry, but not happy faces, whereas individuals with lower IQ or lower anxiety showed the opposite pattern. These results suggest that attention bias interventions to modify a threat bias may be most effectively targeted to anxious individuals with WS with relatively high IQ

Greenhouse gas emissions from a Western Australian finfish supply chain

Greenhouse gas (GHG) emissions in the form of carbon dioxide equivalent (CO2 - eq) from two Western Australian finfish supply chains, from harvest to retail outlet, were measured using streamlined life cycle assessment methodology. The identification of interventions to potentially reduce the GHG emissions was determined from the results obtained. Electricity consumption contributed to the highest GHG emissions within the supply chains measured, followed by refrigeration gas leakage and disposal of unused fish portions. Potential cleaner production strategies (CPS) to reduce these impacts included installing solar panels, recycling the waste, good housekeeping in refrigeration equipment maintenance, and input substitution of refrigeration gas. The results show a combination of these strategies have the potential to reduce up to 35% of the total GHG emissions from fillet harvest, processing and retail

The NANOGrav 11-year Data Set: High-precision Timing of 45 Millisecond Pulsars

We present high-precision timing data over time spans of up to 11 years for 45 millisecond pulsars observed as part of the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) project, aimed at detecting and characterizing low-frequency gravitational waves. The pulsars were observed with the Arecibo Observatory and/or the Green Bank Telescope at frequencies ranging from 327 MHz to 2.3 GHz. Most pulsars were observed with approximately monthly cadence, and six high-timing-precision pulsars were observed weekly. All were observed at widely separated frequencies at each observing epoch in order to fit for time-variable dispersion delays. We describe our methods for data processing, time-of-arrival (TOA) calculation, and the implementation of a new, automated method for removing outlier TOAs. We fit a timing model for each pulsar that includes spin, astrometric, and (for binary pulsars) orbital parameters; time-variable dispersion delays; and parameters that quantify pulse-profile evolution with frequency. The timing solutions provide three new parallax measurements, two new Shapiro delay measurements, and two new measurements of significant orbital-period variations. We fit models that characterize sources of noise for each pulsar. We find that 11 pulsars show significant red noise, with generally smaller spectral indices than typically measured for non-recycled pulsars, possibly suggesting a different origin. A companion paper uses these data to constrain the strength of the gravitational-wave background