In this Portfolio Optimization Project, we used Markowitz¡¯s modern portfolio theory for portfolio optimization. We selected fifteen stocks traded on the New York Stock Exchange and gathered these stocks¡¯ historical data from Yahoo Finance [1]. Then we used Markowitz¡¯s theory to analyze this data in order to obtain the optimal weights of our initial portfolio. To maintain our investment in a current tangency portfolio, we recalculated the optimal weights and rebalanced the positions every week. In the CAPM project, we used the security characteristic line to calculate the stocks¡¯ daily returns. We also computed the risk of each asset, portfolio beta, and portfolio epsilons. In the Factor Modeling project, we computed estimates of each asset¡¯s expected returns and return variances of fifteen stocks for each of our factor models. Also we computed estimates of the covariances among our asset returns. In order to find which model performs best, we compared each portfolio¡¯s actual return with its corresponding estimated portfolio return.

In this Portfolio Optimization Project, we used Markowitz¡¯s modern portfolio theory for portfolio optimization. We selected fifteen stocks traded on the New York Stock Exchange and gathered these stocks¡¯ historical data from Yahoo Finance [1]. Then we used Markowitz¡¯s theory to analyze this data in order to obtain the optimal weights of our initial portfolio. To maintain our investment in a current tangency portfolio, we recalculated the optimal weights and rebalanced the positions every week. In the CAPM project, we used the security characteristic line to calculate the stocks¡¯ daily returns. We also computed the risk of each asset, portfolio beta, and portfolio epsilons. In the Factor Modeling project, we computed estimates of each asset¡¯s expected returns and return variances of fifteen stocks for each of our factor models. Also we computed estimates of the covariances among our asset returns. In order to find which model performs best, we compared each portfolio¡¯s actual return with its corresponding estimated portfolio return.

Faculty of Engineering School of Civil and Enviromental Engineering 9308302w fredb@ananzi.co.za / The Renoster River is a minor left-bank tributary of the Vaal River located some 120 km south-west of Johannesburg in the Free State Province of South Africa. The river is dammed by the Koppies Dam, which divides the river into upper and lower basins. No water is released directly from the Koppies Dam into the Renoster River channel except as a protective measure during natural flooding events. Irrigation releases are made through a separate canal system. The lower Renoster River was modeled as a series of channels, crossing four quaternary catchments. Hydraulic routing was used to estimate potential flow losses for discrete flow events. Out-of-bank flow was found to occur when flow exceeded 300 m3/s. A Pitman monthly rainfall-runoff hydrologic model was used to model longer term historical streamflow data and associated losses. Flow losses for individual flood events along the lower Renoster River were found to vary between 3% and 17%. For a longer term constant flow regime, losses can be expected to range between 10% and 40%. Based on both the hydraulic and hydrologic models, the primary cause of flow loss was found to be evaporation, with insignificant transpiration and transmission losses. For eighty years flow in the lower Renoster River has been artificially controlled. Within that time period the river has adjusted itself to the imposed anthropogenic flow regime. Water losses associated with planned releases into the lower Renoster River can be minimized by using a high discharge rate, to a maximum of 300 m3/s, combined with larger volumetric flows. Evaporation losses can be significant, and release strategies will need to take this into consideration.

The horseshoe prior has been shown to successfully handle high-dimensional sparse estimation problems. It both adapts to sparsity efficiently and provides nearly unbiased estimates for large signals. In addition, efficient sampling algorithms have been developed and successively applied to a vast array of high-dimensional sparse estimation problems. In this dissertation, we investigate the prediction performance of the horseshoe prior in sparse regression, and extend the horseshoe prior to two multivariate settings.<br><br>We begin with a study of the finite sample prediction performance of shrinkage regression methods, where the risk can be unbiasedly estimated using Stein's approach. We show that the horseshoe prior achieves an improved prediction risk over global shrinkage rules, by using a component-specific local shrinkage term that is learned from the data under a heavy-tailed prior, in combination with a global term providing shrinkage towards zero. We demonstrate improved prediction performance in a simulation study and in a pharmacogenomics data set, confirming our theoretical findings.<br><br>We then shift to extending the horseshoe prior to handle two high-dimensional multivariate problems. First, we develop a new estimator of the inverse covariance matrix for high-dimensional multivariate normal data. The proposed graphical horseshoe estimator has attractive properties compared to other popular estimators. The most prominent benefit is that when the true inverse covariance matrix is sparse, the graphical horseshoe estimator provides estimates with small information divergence from the sampling model. The posterior mean under the graphical horseshoe prior can also be almost unbiased under certain conditions. In addition to these theoretical results, we provide a full Gibbs sampler for implementation. The graphical horseshoe estimator compares favorably to existing techniques in simulations and in a human gene network data analysis.<br><br>In our second setting, we apply the horseshoe prior to the joint estimation of regression coefficients and the inverse covariance matrix in normal models. The computational challenge in this problem is due to the dimensionality of the parameter space that routinely exceeds the sample size. We show that the advantages of the horseshoe prior in estimating a mean vector, or an inverse covariance matrix, separately are also present when addressing both simultaneously. We propose a full Bayesian treatment, with a sampling algorithm that is linear in the number of predictors. Extensive performance comparisons are provided with both frequentist and Bayesian alternatives, and both estimation and prediction performances are verified on a genomic data set.

Thesis advisor: James E. Anderson / Thesis advisor: Rosen Valchev / The effect of uncertainty on firms' behavior and on the macroeconomy is generally negative in the literature. Extensive research has also demonstrated that financial frictions limit the extent of firms' activities and growth prospects. In the first two chapters of this dissertation, I study both empirically and theoretically how a specific type of uncertainty, exchange rate uncertainty, interacts with financial frictions to affect the behavior of exporting firms. In line with the existing literature, I find in the first chapter that exports of manufacturing sectors in which firms are more financially constrained decrease by more in times of high uncertainty. Having more tangible capital, which can potentially be used as collateral, makes the effect of uncertainty less negative, especially im sectors where firms are large. Relying more on external financing, on the other hand, makes the effect more negative and affects sectors with small firms more. Current theoretical models have little to say about the effect of uncertainty on heterogeneous firms. To address this issue, I introduce in the second chapter a model of financially-constrained heterogeneous exporting firms in which credit conditions depend on the degree of exchange rate uncertainty. Firms in different sectors face different types of financial constraints, and are therefore differently affected by uncertainty. I use the calibrated model to evaluate potential policies that could be implemented to alleviate the negative effect of exchange rate uncertainty on exports. The uncovered interest parity puzzle is the empirical finding that countries with higher risk-free interest rates tend to see their currencies appreciate in the short run. Typical two-country macroeconomic models instead predict that high interest-rate currencies depreciate, with arbitrage opportunities eliminating profitable carry trade strategies. The international finance literature responded to this puzzle by providing several alternative theoretical models able to explain the puzzle. In the third chapter of this dissertation, I study how the predictions of two of these alternative models - the habit model of Verdelhan (2010) and the distorted belief model of Gourinchas and Tornell (2004) - are affected when re-cast in a standard dynamic stochastic general equilibrium framework. I investigate how the mechanisms rely on specific parameter values in order to find under which conditions, if any, they can explain the UIP puzzle. In addition, I obtain business cycle moments from model simulations and compare them to the moments obtained from a standard two-country DSGE model and from the data. My results show that for the first model, the habit model, the UIP results disappear under realistic calibrations. For the second model, the distorted beliefs model, UIP properties remain under some calibrations. In addition, business cycle predictions remain close to empirical evidence. / Thesis (PhD) — Boston College, 2019. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

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The Hydro Muscle is a soft linear actuator which utilizes hydraulic pressure and elastic properties of its core for actuation. The Hydro Muscle has been recruited to actuate bio-inspired robot systems using a classic set point tracking feedback control system. A more efficient method is to develop a model-based control system which uses a dynamic model of the Hydro Muscle. The dynamic behavior of the Hydro Muscle which describes the relation between the forces exerted to the resultant motion can be studied with the help of a dynamic viscoelastic model. A dynamic viscoelastic model defines the force exerted by the Hydro Muscle as a function of the hydraulic pressure, the tensile expansion of the Hydro Muscle and the rate of its tensile expansion. Multivariable linear regression is employed to generate a model to relate fluid pressure, tensile expansion, and the rate of tensile expansion to the force exerted by the Hydro Muscle. The developed model can be utilized to implement a model-based control algorithm for the force control of individual joints. This model-based control design could be extended to systems involving multiple Hydro Muscles to allow for a modular control system. The design and test of multi-fiber Hydro Muscle actuated biologically inspired ankle is considered to study control strategies for multi-fiber system. A set-point tracking control algorithm with a proportional differential controller is used to minimize the tracking error. Modular force variation with sequential recruitment of Hydro Muscle is studied.

"Automatic assembly machines have many cam-driven linkages that provide motion to tooling. Newer machines are typically designed to operate at higher speeds and may need to handle products with small and delicate features that must be assembled precisely every time. In order to design a good tooling mechanism linkage, the dynamic behavior of the components must be considered; this includes both the gross kinematic motion and self-induced vibration motion. Current simulations of cam-follower system dynamics correlate poorly to the actual dynamic behavior because they ignore two events common in these machines: impact and over-travel. A new dynamic model was developed with these events. From this model, an insight into proper design of systems with deliberate impact was developed through computer modeling. To attain more precise representations of these automatic assembly machines, a simplified industrial cam-follower system model was constructed in SolidWorks CAD software. A two-mass, single-degree-of-freedom dynamic model was created in Simulink, a dynamic modeling tool, and validated by comparing to the model results from the cam design program, DYNACAM. After the model was validated, a controlled impact and over-travel mechanism was designed, manufactured, and assembled to a simplified industrial cam-follower system, the Cam Dynamic Test Machine (CDTM). Then, a new three-mass, two-degree-of-freedom dynamic model was created. Once the model was simulated, it was found that the magnitude and the frequency of the vibration, in acceleration comparison, of the dynamic model matched with the experimental results fairly well. The two maximum underestimation errors, which occurred where the two bodies collided, were found to be 119 m/s2 or 45% and 41 m/s2 or 30%. With the exception of these two impacts, the simulated results predicted the output with reasonable accuracy. At the same time, the maximum simulated impact force overestimated the maximum experimental impact force by 2 lbf or 1.3%. By using this three-mass, two-DOF impact model, machine design engineers will be able to simulate and predict the behavior of the assembly machines prior to manufacturing. If the results found through the model are determined to be unsatisfactory, modifications to the design can be made and the simulation rerun until an acceptable design is obtained."

Cette thèse a proposé quelques développements pour améliorer la modélisation appliquée de la mobilité liée à l'activité d'achat de la population d'une agglomération. Pour ce faire, nous nous sommes appuyés sur un schéma de principe du processus de caractérisation de cette mobilité. Dans ce schéma nous avons considéré que cette mobilité est définie au sein d'un ménage en répondant à quatre questions posées simultanément (qui ?, où ?, comment ? et quoi ?). La première partie de cette thèse porte sur les aspects tant qualitatifs que quantitatifs qui doivent apparaître dans le schéma ; la deuxième, sur les outils de modélisation théorique et opérationnelle ; la troisième, sur la construction du schéma proprement dit ; et la quatrième, sur l'utilisation appliquée d'un outil déjà existant. Les contributions principales de notre thèse consistent en l'utilisation d'une approche systémique pour décrire cette mobilité et l'utilisation de techniques peu utilisées actuellement en France / This thesis has aimed to enhance some aspects of the modelling of shopping trips in an urban area. We have used a block diagram to represent the mechanisms used to define this mobility and we have focused on some sides of this problem in a disaggregated approach. In our diagram we have supposed that shopping trips are defined in a household by answering four simultaneous questions (who?, where?, how? and what?). We have started by studying the qualitative and quantitative elements which must appear in our block diagram and we have continued by the study of the existing theoretical and applied modelling tools. We could then build our block diagram, and we have finished by using an existing modelling tool. The main contributions of our work are the use of a systemic approach for the description of this mobility and the use of techniques not common in France

Cílem práce na téma Finanční analýza akciové společnosti bylo zjištění absolutních a poměrových ukazatelů (např. likvidita, zadluženost, rentabilita, EVA), určení hodnot pro bankrotní a bonitní model a odvození závěrů ze zjištěných hodnot.