Long-Run Cash-Flow and Discount-Rate Risks in the Cross-Section of US Returns

This paper decomposes the overall market (CAPM) risk into parts re.ecting uncertainty related to the long-run dynamics of portfolio-speci.c and market cash .ows and discount rates. We decompose market betas into four sub-betas (as- sociated with assets.and market.s cash .ows and discount rates) and we employ a discrete time version of the I-CAPM to derive a four-beta model. The model performs well in pricing average returns on single- and double-sorted portfolios ac- cording to size, book-to-market, dividend-price ratios and past risk, by producing high estimates for the explained cross-sectional variation in average returns and economically and statistically acceptable estimates for the coe¢ cient of relative risk aversion.CAPM, cash-.ow risk,discount-rate risk, VAR-GARCH,BEKK, asset pricing

Out-of-sample equity premium prediction: A complete subset quantile regression approach

This paper extends the complete subset linear regression framework to a quantile regression setting. We employ complete subset combinations of quantile forecasts in order to construct robust and accurate equity premium predictions. Our recursive algorithm that selects, in real time, the best complete subset for each predictive regression quantile succeeds in identifying the best subset in a time- and quantile-varying manner. We show that our approach delivers statistically and economically significant out-of-sample forecasts relative to both the historical average benchmark and the complete subset mean regression approach

Out-of-sample equity premium prediction: A complete subset quantile regression approach

This paper extends the complete subset linear regression framework to a quantile regression setting. We employ complete subset combinations of quantile forecasts in order to construct robust and accurate equity premium predictions. Our recursive algorithm that selects, in real time, the best complete subset for each predictive regression quantile succeeds in identifying the best subset in a time- and quantile-varying manner. We show that our approach delivers statistically and economically significant out-of-sample forecasts relative to both the historical average benchmark and the complete subset mean regression approach

Maps of the number of HI clouds along the line of sight at high galactic latitude

Characterizing the structure of the Galactic Interstellar Medium (ISM) in three dimensions is of high importance for accurate modeling of dust emission as a foreground to the Cosmic Microwave Background (CMB). At high Galactic latitude, where the total dust content is low, accurate maps of the 3D structure of the ISM are lacking. We develop a method to quantify the complexity of the distribution of dust along the line of sight with the use of HI line emission. The method relies on a Gaussian decomposition of the HI spectra to disentangle the emission from overlapping components in velocity. We use this information to create maps of the number of clouds along the line of sight. We apply the method to: (a) the high-galactic latitude sky and (b) the region targeted by the BICEP/Keck experiment. In the North Galactic Cap pixels are occupied by 3 clouds on average, while in the South the number falls to 2.5. The statistics of the number of clouds are affected by Intermediate-Velocity Clouds (IVCs), primarily in the North. The presence of IVCs results in detectable features in the dust emission measured by \textit{Planck}. We investigate the complexity of HI spectra in the BICEP/Keck region and find evidence for the existence of multiple components along the line of sight. The data and software are made publicly available, and can be used to inform CMB foreground modeling and 3D dust mapping.Comment: 28 pages (including appendices), 19 figures, submitted to AAS journals, comments welcom

Extreme Starlight Polarization in a Region with Highly Polarized Dust Emission

Galactic dust emission is polarized at unexpectedly high levels, as revealed by Planck. The origin of the observed $\simeq 20\%$ polarization fractions can be identified by characterizing the properties of optical starlight polarization in a region with maximally polarized dust emission. We measure the R-band linear polarization of 22 stars in a region with a submillimeter polarization fraction of $\simeq 20$. A subset of 6 stars is also measured in the B, V and I bands to investigate the wavelength dependence of polarization. We find that starlight is polarized at correspondingly high levels. Through multiband polarimetry we find that the high polarization fractions are unlikely to arise from unusual dust properties, such as enhanced grain alignment. Instead, a favorable magnetic field geometry is the most likely explanation, and is supported by observational probes of the magnetic field morphology. The observed starlight polarization exceeds the classical upper limit of $\left[p_V/E\left(B-V\right)\right]_{\rm max} = 9$%mag$^{-1}$ and is at least as high as 13%mag$^{-1}$ that was inferred from a joint analysis of Planck data, starlight polarization and reddening measurements. Thus, we confirm that the intrinsic polarizing ability of dust grains at optical wavelengths has long been underestimated.Comment: Accepted by A&AL, data to appear on CDS after publication. 6 page

Maps of the Number of H I Clouds along the Line of Sight at High Galactic Latitude

Characterizing the structure of the Galactic interstellar medium (ISM) in three dimensions is of high importance for accurate modeling of dust emission as a foreground to the cosmic microwave background (CMB). At high Galactic latitude, where the total dust content is low, accurate maps of the 3D structure of the ISM are lacking. We develop a method to quantify the complexity of the distribution of dust along the line of sight with the use of H i line emission. The method relies on a Gaussian decomposition of the H i spectra to disentangle the emission from overlapping components in velocity. We use this information to create maps of the number of clouds along the line of sight. We apply the method to (a) the high Galactic latitude sky and (b) the region targeted by the BICEP/Keck experiment. In the north Galactic cap we find on average three clouds per 0.2 square degree pixel, while in the south the number falls to 2.5. The statistics of the number of clouds are affected by intermediate-velocity clouds (IVCs), primarily in the north. IVCs produce detectable features in the dust emission measured by Planck. We investigate the complexity of H i spectra in the BICEP/Keck region and find evidence for the existence of multiple components along the line of sight. The data (doi: 10.7910/DVN/8DA5LH) and software are made publicly available and can be used to inform CMB foreground modeling and 3D dust mapping

Eliminating artefacts in polarimetric images using deep learning

Polarization measurements done using Imaging Polarimeters such as the Robotic Polarimeter are very sensitive to the presence of artefacts in images. Artefacts can range from internal reflections in a telescope to satellite trails that could contaminate an area of interest in the image. With the advent of wide-field polarimetry surveys, it is imperative to develop methods that automatically flag artefacts in images. In this paper, we implement a Convolutional Neural Network to identify the most dominant artefacts in the images. We find that our model can successfully classify sources with 98 per cent true positive and 97 per cent true negative rates. Such models, combined with transfer learning, will give us a running start in artefact elimination for near-future surveys like WALOP

Search for AGN counterparts of unidentified Fermi-LAT sources with optical polarimetry: Demonstration of the technique

The third Fermi-LAT catalog (3FGL) presented the data of the first four years of observations from the Fermi Gamma-ray Space Telescope mission. There are 3034 sources, 1010 of which still remain unidentified. Identifying and classifying gamma-ray emitters is of high significance with regard to studying high-energy astrophysics. We demonstrate that optical polarimetry can be an advantageous and practical tool in the hunt for counterparts of the unidentified gamma-ray sources (UGSs). Using data from the RoboPol project, we validated that a significant fraction of active galactic nuclei (AGN) associated with 3FGL sources can be identified due to their high optical polarization exceeding that of the field stars. We performed an optical polarimetric survey within $3\sigma$ uncertainties of four unidentified 3FGL sources. We discovered a previously unknown extragalactic object within the positional uncertainty of 3FGL J0221.2+2518. We obtained its spectrum and measured a redshift of $z=0.0609\pm0.0004$. Using these measurements and archival data we demonstrate that this source is a candidate counterpart for 3FGL J0221.2+2518 and most probably is a composite object: a star-forming galaxy accompanied by AGN. We conclude that polarimetry can be a powerful asset in the search for AGN candidate counterparts for unidentified Fermi sources. Future extensive polarimetric surveys at high galactic latitudes (e.g., PASIPHAE) will allow the association of a significant fraction of currently unidentified gamma-ray sources.Comment: accepted to A&

Quantile Forecast Combinations in Realised Volatility Prediction

This paper tests whether it is possible to improve point, quantile and density forecasts of realised volatility by conditioning on a set of predictive variables. We employ quantile autoregressive models augmented with macroeconomic and financial variables. Complete subset combinations of both linear and quantile forecasts enable us to efficiently summarise the information content in the candidate predictors. Our findings suggest that no single variable is able to provide more information for the evolution of the volatility distribution beyond that contained in its own past. The best performing variable is the return on the stock market followed by the inflation rate. Our complete subset approach achieves superior point, quantile and density predictive performance relative to the univariate models and the autoregressive benchmark