Estimating and forecasting international yield curves: a no-arbitrage VAR with macroeconomic and latent variables

The focus of this paper is to replicate and extend a model for the prediction of interest rate affine term structure models. We propose an extension of one of the most recent models in the field to some G10 countries. Our purpose is to find how well this model fares at forecasting in different markets and if its implementation in investment strategies is viable. This model uses measures of real activity and inflation as macroeconomic variables together with unobservable variables. This Work Project had the objective of delivering its results to BlackRock to build an innovative and successful investment strategy

BlackRock work project estimating and forecasting international yield curves: a no-arbitrage VAR with macroeconomic and latent variables

The focus of this paper is to replicate and extend the estimation of a model for the prediction of interest rate affine term structures to G10 countries. The existing literature for the prediction of yield curves is vast, we will be focusing on the popular class of Gaussian affine term structure models. Our model uses measures of real activity and inflation as macroeconomic variables together with unobservable variables, through non-arbitrage VARs. Our purpose is to find how well this model fares at forecasting in different markets and if the model is good at predicting the correct shifts in the yield curve. This Work Project had the objective of delivering its results to BlackRock so they can trade based on forecasts from the model

Integration of Sentinel-1 and Sentinel-2 data for Earth surface classification using Machine Learning algorithms implemented on Google Earth Engine

In this study, Synthetic Aperture Radar (SAR) and optical data are both considered for Earth surface classification. Specifically, the integration of Sentinel-1 (S-1) and Sentinel-2 (S-2) data is carried out through supervised Machine Learning (ML) algorithms implemented on the Google Earth Engine (GEE) platform for the classification of a particular region of interest. Achieved results demonstrate how in this case radar and optical remote detection provide complementary information, benefiting surface cover classification and generally leading to increased mapping accuracy. In addition, this paper works in the direction of proving the emerging role of GEE as an effective cloud-based tool for handling large amounts of satellite data.Comment: 4 pages, 7 figures, IEEE InGARSS conferenc

Multitemporal analysis in Google Earth Engine for detecting urban changes using optical data and machine learning algorithms

The aim of this work is to perform a multitemporal analysis using the Google Earth Engine (GEE) platform for the detection of changes in urban areas using optical data and specific machine learning (ML) algorithms. As a case study, Cairo City has been identified, in Egypt country, as one of the five most populous megacities of the last decade in the world. Classification and change detection analysis of the region of interest (ROI) have been carried out from July 2013 to July 2021. Results demonstrate the validity of the proposed method in identifying changed and unchanged urban areas over the selected period. Furthermore, this work aims to evidence the growing significance of GEE as an efficient cloud-based solution for managing large quantities of satellite data.Comment: 4 pages, 6 figures, 2023 InGARSS Conferenc

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

a no-arbitrage VAR with macroeconomic and latent variables- indididual project

The focus of this paper is to replicate and extend a model for the prediction of interest rate affine term structure models. We propose an extension of one of the most recent models in the field to some G10 countries. Our purpose is to find how well this model fares at forecasting in different markets and if its implementation in investment strategies is viable. This model uses measures of real activity and inflation as macroeconomic variables together with unobservable variables. This Work Project had the objective of delivering its results to BlackRock to build an innovative and successful investment strategy