The Forward- and the Equity-Premium Puzzles: Two Symptoms of the Same Illness?

We build a pricing kernel using only US domestic assets data and checkwhether it accounts for foreign markets stylized facts that escape consumptionbased models. By interpreting our stochastic discount factor as the projection ofa pricing kernel from a fully specified model in the space of returns, our results indicatethat a model that accounts for the behavior of domestic assets goes a longway toward accounting for the behavior of foreign assets. We address predictabilityissues associated with the forward premium puzzle by: i) using instrumentsthat are known to forecast excess returns in the moments restrictions associatedwith Euler equations, and; ii) by pricing Lustig and Verdelhan (2007)'s foreigncurrency portfolios. Our results indicate that the relevant state variables that explainforeign-currency market asset prices are also the driving forces behind U.S.domestic assets behavior.

The forward- and the equity-premium puzzles: two symptoms of the same illness?

Using information on US domestic financial data only, we build a stochastic discountfactor—SDF— and check whether it accounts for foreign markets stylized factsthat escape consumption based models. By interpreting our SDF as the projection ofa pricing kernel from a fully specified model in the space of returns, our results indicatethat a model that accounts for the behavior of domestic assets goes a long waytoward accounting for the behavior of foreign assets prices. We address predictabilityissues associated with the forward premium puzzle by: i) using instruments that areknown to forecast excess returns in the moments restrictions associated with Eulerequations, and; ii) by pricing Lustig and Verdelhan (2007)’s foreign currency portfolios.Our results indicate that the relevant state variables that explain foreign-currencymarket asset prices are also the driving forces behind U.S. domestic assets behavior.

Simple and Efficient Computational Method to Analyze Cylindrical Plasmonic Nanoantennas

We present in this work a simple and efficient technique to analyze cylindrical plasmonic nanoantennas. In this method, we take into account only longitudinal current inside cylindrical structures and use 1D integral equation for the electric field with a given surface impedance of metal. The solution of this integral equation is obtained by the Method of Moments with sinusoidal basis functions. Some examples of calculations of nanoantennas with different geometries and sources are presented and compared with the commercial software Comsol 3D simulations. The results show that the proposed technique provides a good precision in the near-infrared and lower optical frequencies 100–400 THz

Overcoming over–indebtedness with AI - A case study on the application of AutoML to research

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThis research examines how artificial intelligence may contribute to better understanding and overcoming over-indebtedness in contexts of high poverty risk. This study uses a field database of 1,654 over-indebted households to identify distinguishable clusters and to predict its risk factors. First, unsupervised machine learning generated three overindebtedness clusters: low-income (31.27%), low credit control (37.40%), and crisis-affected households (31.33%). These served as basis for a better understanding on the complex issue that is over-indebtedness. Second, a predictive model was developed to serve as a tool for policymakers and advisory services by streamlining the classification of overindebtedness profiles. On building such model, an AutoML approach was leveraged achieving performant results (92.1% accuracy score). Furthermore, within the AutoML framework, two techniques were employed, leading to a deeper discussion on the benefits and inner workings of such strategy. Ultimately, this research looks to contribute on three fronts: theoretical, by unfolding previously unexplored characteristics on the concept of over-indebtedness; methodological, by proposing AutoML as a powerful research tool accessible to investigators on many backgrounds; and social, by building real-world applications that aim at mitigating over-indebtedness and, consequently, poverty risk