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Este trabalho apresenta uma abordagem inovadora para a modelagem do risco cambial. Ao invés de utilizar regressões de MQO \"equação por equação\", explora-se a correlação contemporânea e estrutural entre os choques de preferência num sistema de equações de precificação de ativos. Estima-se um modelo via SUR em uma amostra de excessos de retorno de países entre 1999Q1 e 2014Q2, utilizando-se novos fatores de risco associados ao PIB das diferentes economias. O modelo empírico é derivado de preferências que são consistentes com um problema de economia aberta, em contraste com a abordagem habitual que utiliza o crescimento do consumo de bens duráveis e não-duráveis como fatores de risco. A estratégia econométrica escolhida leva a uma melhora significativa da precisão das quantidades de risco (betas) estimadas. A relação positiva entre taxas de juros e quantidades de risco, contudo, não é corroborada para todos os betas. / This thesis presents an innovative approach for modeling currency risk. Instead of using equation by equation OLS, we explore the structural contemporaneous correlation between preference shocks across a system of asset pricing equations. SUR regressions as well as new risk factors lead to a marked improvement in efficiency for the estimation of the quantities of risk (betas) in a sample of country excess returns from 1999Q1 to 2014Q2. However, the monotonic relation between interest rates and quantities of risk is not statistically significant for all betas. Our model is derived from preferences that are consistent to an open economy problem, in contrast to the typical approach of using durable and non-durable consumption growth as risk factors.

M.Com. (Financial Economics) / Recently, there has been a growth in the bond market. This growth has brought with it an ever-increasing volume and range of interest rate depended derivative products known as interest rate derivatives. Amongst the variables used in pricing these derivative products is the short-term interest rate. A numbers of short-term interest rate models that are used to fit the short-term interest rate exist. Therefore, understanding the features characterised by various short-term interest rate models, and determining the best fitting models is crucial as this variable is fundamental in pricing interest rate derivatives, which further determine the decision making of economic agents. This dissertation examines various short-term interest rate models in continuous time in order to determine which model best fits the South African short-term interest rates. Both the linear and nonlinear short-term interest rate models were estimated. The methodology adopted in estimating the models was parametric approach using Quasi Maximum Likelihood Estimation (QMLE). The findings indicate that nonlinear models seem to fit the South African short-term interest rate data better than the linear models

M.Com. (Financial Economics) / Exchange rate forecasting has been an important and complex field of study originating mainly from the introduction of floating exchange rates in the 1970s. Since then, various models have been developed to explain exchange rate behaviour, all contributing in their own way to the understanding of what economic and financial information reveal about the future price of exchange rates. To measure the performance of a variety of exchange rate models, researchers in exchange rate forecasting almost always use the random walk model as benchmark to evaluate the forecasting performance of exchange rate models. An exchange rate model is regarded as superior if it can outperform a random process. The random walk model, a special case of the unit root process, helps us to identify the kinds of disturbances that drive the exchange rate to follow an independent successive process. If the exchange rate follows a random walk process, it has no mean reversion tendency and a directional shock in the exchange rate will cause it to deviate from its long-run equilibrium. Conversely, if the exchange rate does not follow a random walk, it has mean reverting tendencies, and will follow a stationary process which allows us to accurately forecast the exchange rate based on historic observations (Lam, Wong and Wong, 2005:1). However, it seems unrealistic that exchange rates will follow either a random walk process or a stationary process. If we assume that the exchange rate follows a random walk, we also assume that the order flow information from exchange rate trades follows a random walk, and by implication that macroeconomic exchange rate information follows a random walk [see Lyons (2001) for the link between order flow and macroeconomic fundamentals]. It seems unrealistic that exchange rates will follow an identifiable mean reverting (stationary) process, as daily exchange rates are exposed to risk, news and speculation which functions independent from long-run exchange rate fundamentals. Ironically, Meese and Rogoff (who laid the foundation for the use of random walk models as benchmark in exchange rate forecasting) emphasize that exchange rates do not follow an exact random walk (Meese and Rogoff, 1983:14). However, if it is known that exchange rates do not follow a random process explicitly, alternative exchange rate benchmark models should be considered. Yet, judging by the universal...

Much depends on the future course of interest rates. The decisions of families to make major purchases, the willingness of businesses to expand and invest, the rise and fall of the economy and stock market, the ability of lesser developed countries to repay their debts, the tenure of presidents and prime ministers--all of these may turn on whether interest rates increase or decrease in the months ahead. Several decision functions developed in the dissertation permit the direction of change of interest rates on long-term U. S. government bonds to be forecast correctly about 60% of the time. When the different models are combined, effectiveness is increased, and when the forecasts are dollar-weighted, performance in excess of 70% is possible. The results are evaluated in comparison with a Bayesian forecasting model and a 10,000-event Monte Carlo simulation of a random decision rule. The forecasting ability of the models is statistically significant at the 99% level of confidence. The dissertation reports on one of the first application of powerful techniques recently developed in cybernetics and engineering to forecasting the direction of change in interest rates. Two forecasting algorithms, called linear decision functions or linear classifiers, are derived using the principles of pattern recognition. Because they are recursively updated, both algorithms operate dynamically and adapt their performance to changes in the economic environment. One classifier, a modification of the widely used least-mean-squared-error algorithm, is adapted to permit monthly revision and to allow larger movements of interest rates to have greater weight in future decisions. The second algorithm permits refinement of the parameter estimates generated by the first. These formal, mathematical constructs are then supplied with financial variables--leading indicators of inflation and investment activity--to permit unconditional, ex-ante forecasts of the direction of change of interest rates on long-term government bonds over a one-month time horizon throughout the period 1969-82. The results should be of interest to investment managers, speculators, corporate treasurers, policymakers, economists and forecasters.

The overall college enrollment rates for young adults have increased over the last several years. While this is promising, a notable amount of students do not attain a degree. This scenario can create major consequences for the United States as global competitiveness requires a workforce that possesses a postsecondary degree. Dual enrollment is a program that has been seen to answer the need for more postsecondary graduates. Despite the robust literature that suggests the positive effects for students who participate in dual enrollment, limited research exists on the effects of dual enrollment on the institution. Therefore, this study attempted to fill the gap in the literature by examining the effects of dual enrollment on an institution. The independent variable was participation in dual enrollment and the dependent variables were persistence rates and degree completion. The population consisted of 5,251 first-time, full-time students in the Mississippi Community College System. Of this number, 741 had taken at least 1 dual enrollment course between the fall of 2010 and the spring of 2015, and 4,510 had no previous dual enrollment experience at all. A Chi-square test was used for both research questions. Results of the study indicate that there is a significant difference in persistence rates when comparing dual enrolled students to non-dual enrolled students. First-time, full-time students who had previous dual enrollment experience were more likely to maintain consistent enrollment (69%) at the community college than students who had no previous dual enrollment experience (45%). There is also a significant relationship between students attaining a degree in a timely manner when comparing dual enrolled students to non-dual enrolled students. First-time, full-time students who had previous dual enrollment experience were more likely to earn a degree in 3 years (61%) than students who did not participate in dual enrollment (35%). The effect size for both research questions was small. While the outcomes of this study are positive, it is imperative to continue to examine the effects of dual enrollment on an institution. Policy differences at each of the Mississippi community colleges could render different outcomes for the students and ultimately affect the institution.

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Student retention rates and graduation rates currently play a major role in measuring the success of institutions of higher education. To contribute to the likelihood of this success many institutions offer programs designed to increase the academic performance of their students especially those classified as incoming freshmen. Others are more focused and target those who are from underrepresented populations. Nonetheless not many programs have been designed to aid those students in the subsequent years that follow freshman year. The purpose of this research project was to determine if there are significant differences in the success of those students who participate in a multi-year program as opposed to those who participate in a program specifically designed for incoming freshmen. Additionally these 2 groups were compared with students who did not participate in either program. The participants in this study were classified within 3 groups: Quest for Success, Student Support Services, and nonprogram participants. Archival data were used to examine grade point averages, retention rates, and graduation rates. A random sample of 125 students from each of the 3 groups (375 total) was examined for the purposes of comparing mean grade point averages. For the purposes of comparing retention rates and graduation rates, however, the population was examined due to the manner in which data were provided. Additionally the use of the population provided more precise retention rates and graduation rates in this study. Findings of the study are congruent with the literature in terms of the role that outreach programs play in the success of underrepresented students. These results revealed that students in the multi-year program, Student Support Services, had significantly higher grade point averages, retention rates, and graduation rates when compared to Quest for Success (a 1-year incoming freshman program). Student Support Services also had significantly higher grade point averages and retention rates than nonprogram participants from underrepresented student populations. Furthermore there were no significant differences found in comparisons between Quest for Success and nonprogram participants.

This dissertation is comprised of four chapters. Chapter 1 provides an introduction to<br />Economic application of time series analysis and discusses the topics covered in each of the following chapters along with some main results therein. <br />    In Chapter 2, I construct a measure of information asymmetry in the financial markets in U.S., by estimating an index of agency cost pertaining to U.S. manufacturing firms. The cyclical behavior of the unobservable agency cost is derived by a novel application of the Kalman filter within a Bayesian framework, using firm level data from 1984-2006. The preliminary results provide support to the financial accelerator mechanism in the business cycle literature. <br />    In Chapter 3, I show that people\'s expectation of uncertainty in financial markets is a significant factor impacting short-term real exchange rate movements. Specifically, a sudden increase in expectation of stock market volatility in a low interest rate country tends to appreciate their currencies against high interest rate currencies. I construct a measure of conditional expected uncertainty from volatility of returns of the dominant portfolio (indices) of 7 industrialized countries. I identify uncertainty shocks and its impact on dollar real exchange rate, and explain my results in the context of currency carry trade.<br />    Chapter 4 of my dissertation documents the presence of significant non-linearity in the deficit-interest rate relationship in the U.S. economy. Using an asymptotic threshold test as per Hansen (2000), I find strong evidence for threshold effects in the impact of expected deficit on future long-term interest rates. I find that a percentage point increase in expected deficit in a regime where the expected deficit/GDP ratio is above 1.8 percent (the estimated threshold value) increases future nominal long term interest rates by 29-30 basis point, and a "news shock" to expectation of future deficit increases future real long term interest rates by 12-18 basis points. When expected deficit/GDP ratio is below 1.8 percent, an increase in expected deficit has no impact on future long-term interest rates. <br /> / Ph. D.