<p> Modern general equilibria under uncertainty are modeled based on the recognition that all risks cannot be eliminated, perfect hedging is not possible, and some risk exposures must be tolerated. Therefore, we need to define the set of acceptable risks as a primitive of the financial economy. This set will be a cone, hence the word conic. Such a conic perspective challenges classical economics by introducing finance into the economic models and enables us to rewrite major chapters of classical micro- and macro-economics textbooks. </p>

<p> This research focused on hawala regulations in multiple jurisdictions, strategies of international bodies to mitigate illicit transfers, and implementation of a standardized approach to monitor money remittances. Transfer mechanisms used to remit funds internationally appeal to individuals, organized crime groups, terrorist financiers, and money launderers. Literature reviewed consisted of government studies, financial body reports, media articles, and peer-reviewed journals. Evaluation of different methodologies and the Financial Action Task Force&rsquo;s supervisory controls was completed. It was determined that economic pressure may impact financial networks and encourage compliance if regional government bodies have the necessary authority to enforce regulations. Research revealed recommendations for education programs to aid jurisdictions in setting up financial intelligence units, developing statutes tailored to their economies, and enforcement of supervisory controls. This report further suggested accountability amongst jurisdictions to reduce the ability of criminals and terrorist financiers to move their financial activities to areas with lax enforcement and corrupt governments that do not enforce regulatory recommendations. It also encouraged tracking financial activity and implementing licensing requirements to mitigate de-risking of high-risk customers with the provision of education to customers and third-parties through formal financial institutions. Reduction of unlicensed money remittances and mitigation of illicit funding benefiting organized crime and terrorism is the ultimate goal.</p>

<p> Political processes, particularly democratic policies of the United States (U.S) presidents, have played significant role in investment strategies of Multinational Corporations (MNCs) since the 1970s. This study used quantitative multiple regression analysis to estimate the correlational relationships between the U.S. net foreign direct investment (FDI) and the presidential policies, real long-term interest rates, and the weighted dollar values from the period 1975 to 2011. The sample variables, including the net foreign direct investment, the real interest rates (30-year Treasury bond rates minus inflation rates), and the weighted value of the U.S. dollar with major industrial trading countries using 1973 as the constant year were collected from the U.S. Department of Commerce, Bureau of Economic Analysis public databases and archival tables with numerical data. The presidential party politics (dummy variable) was suitable for determining the correlational impact on FDI in the United States in the observed period. The first major result in the study was that the political party of the president had significant correlational relationship with the U.S. net FDI in the observed period. U.S. political stability attracts foreign financial flows. Sound political policies drive foreign investments (Jakobsen and De Soysa, 2006). Moreover, there was a significant correlation relationship between real interest rates and U.S. net FDI at the five percent level. This result lends support to the Fisher effect which argues that investors are real interest rate conscious. However, there was no correlational relationship between the weighted value of the U.S. dollar and U.S. net FDI in the observed period. Finally, the combined influence of the presidential policies and the Federal Reserve policies (long-term interest rates) was statistically significant on the level of U.S. net foreign investments.</p>

The importance of skewness and kurtosis in the return generating process is assessed by examining the out-of-sample forecasting power of three different Exponential GARCH models that assume the conditional errors are generated by a normal distribution, a generalized error distribution, and a nonparametric distribution. These models are selected because they incorporate the time-series properties of security returns and each of these distributions allows for various degrees of conditional skewness and kurtosis. / First, daily security returns of firms listed on the New York and American Stock Exchanges over the period 1971 to 1991, excluding the year of 1987, are used to estimate the three models. This study finds that the importance of skewness and kurtosis varies over time and across firm size. The length of the holding period also affects the accuracy and reliability of expected returns generated by the three Exponential GARCH models. / Second, daily security returns, computed from both traded prices and bid-ask averages, of National Market System firms in the OTC market from 1988 to 1991, are used to estimate the three models. This study finds that there is a tradeoff between obtaining lower forecast errors and the volatility of the forecast errors when skewness and kurtosis are incorporated in the return generating process. Overall, forecast errors are lower and less volatile when bid-ask averages are used to compute security returns. However, the bid-ask "bounce" does not have a significant affect on the importance of skewness and kurtosis in the return generating process. / Source: Dissertation Abstracts International, Volume: 54-10, Section: A, page: 3835. / Major Professor: David R. Peterson. / Thesis (Ph.D.)--The Florida State University, 1993.

The windfall of oil revenues that flooded the Venezuelan economy, during the decade of the seventies, provided government officials with the financial means to stimulate the process of development and enhance the well-being of future generations. In spite of that wealth, Venezuela is now-a-days showing an alarming negative trend in its rate of growth, accompanied by higher levels of unemployment, poverty and income inequality. / The purpose of this dissertation is to establish a link among oil revenues, distributional coalitions and the process of development in Venezuela during the 1968-1984 period. / The study first defines a set of criteria to analyse the performance of the Venezuelan economy before, during and after the oil boom of the seventies. / After clearly establishing that development has not taken place since the oil boom, the study elaborates an "estimating equation", using Ordinary Least Squares, to explain the process of development. / The study finds that the development process has been negatively affected by the "rent-seeking" activities of the so called "distributional coalitions". The study also finds that oil revenues have not been statistically significant in promoting development. in Venezuela. Instead, oil revenues have been mostly used for non-productive activities or have leaked out of the national economy in the form of capital flight. / Source: Dissertation Abstracts International, Volume: 51-11, Section: A, page: 3833. / Major Professor: James Cobbe. / Thesis (Ph.D.)--The Florida State University, 1990.

Recently, there has been a broadening concern on forecasting techniques that are applied on large data sets, since economists in business and management want to deal with the great magnitude of information. In this analysis, the issue of forecasting a large data set by using different model averaging approaches is addressed. In particular, Bayesian and frequentist model averaging methods are considered, including Bayesian model averaging (BMA), information theoretic model averaging (ITMA) and predictive likelihood model averaging (PLMA). The predictive performance of each scheme is compared with the most promising existing alternatives, namely benchmark AR model and the equal weighted model averaging (AV) scheme. An empirical application on Inflation forecasting for five countries using large data sets within the model averaging framework is applied. The average ARX model with weights constructed differently according to each model averaging scheme is compared with both the benchmark AR and the AV model. For the comparison of the accuracy of forecasts several performance indicators have been provided such as the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), the U-Theil’s Inequality Coefficient (U), Mean Square Forecast Error (MSFE) and the Relative Mean Square Forecast Error (RMSFE). Next, within the Granger causality framework through the Diebold & Mariano (DM) test and the Clark & McCracken (CM) test, whether the data-rich models represented by the three different model averaging schemes have made a statistically significant improvement relative to the benchmark forecasts has been tested. Critical values at 5% and at 10% have been calculated based on bootstrap approximation of the finite sample distribution of the DM and CM test statistics. The main outcome is that although the information theoretic model averaging scheme is a more powerful approach, the other two model averaging techniques can be regarded as useful alternatives.

We analyze the impacts of globalisation on firm performance and firms‟ relative wage payments in an emerging economy context. Using a rich set of manufacturing firm census data for Chile, we study four specific empirical issues, (1) the impacts of foreign direct investment in services on firm total factor productivity (TFP) growth, (2) the heterogeneous effects of import competition on firm TFP, (3) the effect of import competition on firm product upgrading and, (4) the question whether trade had an impact on decreasing relative manufacturing wages. The analysis exploits the panel nature of the dataset, an exogenous measure of import competition – transport costs – and, most importantly, the availability of detailed information on firms‟ products and their prices to investigate these questions rigorously. Specifically, we use the latter information to compute improved TFP measures, analyze product upgrading with a direct quantitative measure - unit prices of firms‟ products – and assess the impact of price changes on wages not only by industry but equally at the firm level. We find an overall positive impact of import competition on firm performance – both on TFP improvements and product innovation. Moreover, results show a positive and significant effect of foreign direct investment in services on TFP growth. However, our results also suggest that globalisation alone may not be sufficient a tool for development. While import competition has a strongly positive impact on product upgrading, we do not find that competition from developed countries stimulates such innovation. The gap between both types of economies may be too large. Also, the overall positive impact of import competition on firm TFP is lower for smaller firms; they will not benefit to the same extent as larger firms. Finally, we find that price effects of trade do not explain the observed reduction in manufacturing wage inequality.

<p> A rational long-horizon stock investment decision is a complex process due to uncertainty in supply and demand, competitive advantage, macroeconomic parameters and various perspectives of investors. Today, the &lsquo;non-tangible assets&rsquo; (NTA) that include goodwill and intangible assets are a significant part of corporate assets, but their role in stock performance has not well studied. The purpose of this research is to empirically analyze the implications of NTA and of gross domestic product (GDP) of the United States on the stock price. According to the efficient market hypothesis, stock price reflects all relevant information. The research question focused on the extent to which NTA and the GDP reflected in the stock price. To determine the extent to which NTA and GDP reflected on the stock price, regression analysis and other statistical tests were used. The sample for the empirical study was 56 corporations listed on the New York Stock Exchange (NYSE) and National Association of Securities Dealers Automated Quotation (NASDAQ). The required data from October 2007 to September 2018 were collected from the United States Securities and Exchange Commission (SEC) and the United States Bureau of Economics (BEA). The key findings of the study are: the NTA and stock price of 45 corporations have a statistically significant correlation as opposed to 11 corporations. The combined NTA of these 11 corporations for the third quarter of 2018 was $531.64 billion. Furthermore, the GDP and stock price of 53 corporations have a statistically significant correlation, but no evidence for three corporations was found. The significance for positive social change is knowledge from this research about the implications of NTA and GDP on stock performance that the investors, policymakers, and other stakeholders could use for preserving the limited resources and creating wealth.</p><p>

This dissertation consists of four papers that investigate long-standing issues in international finance, macroeconomics and international macroeconomics. The common theme of the papers is the fact that each uses novel statistical methods to forecast or evaluate outcomes in international macroeconomics and macroeconomics. / The first two papers focus on the predictability of nominal exchange rates and the profitability of foreign exchange speculation. The first chapter of my dissertation investigates the predictability of nominal exchange rates. Prediction of the exchange rate has long interested economic researchers. A model able to accurately forecast exchange rate movements would have important implications for economic theory and market participants alike. However, it is a stylized fact that empirical models of the exchange rate cannot produce forecasts that are more accurate than those of a random walk---the well-known Meese-Rogoff puzzle. In this chapter, I apply the gradient boosting method due to Hastie, Tibshirani & Friedman (2000) to the problem of forecasting exchange rate movements for nine major currencies relative to the U.S. dollar. The method performs model selection and builds flexible, potentially non-linear models of the exchange rate. I find that the economic fundamentals I consider do contain predictive power at both short and long horizons. The key to successfully forecasting exchange rates is building the 'correct' forecasting model, because I find that model with the greatest explanatory power varies across currencies and across time for any individual currency. At short horizons the method I use is unable to select ex ante a model of the exchange rate that convincingly outperforms a random walk. However, when forecasting 12 months ahead, boosted models produce extremely accurate forecasts of the exchange rate, easily besting the random walk null. / Chapter two, written with Oscar Jorda and Alan M. Taylor, explores the profitability of currency speculation. We document the actions of a hypothetical trader who trades based on forecasts of nominal exchange rates coming from standard forecasting models. Although the efficient markets hypothesis implies that such a trade would yield zero profits on average, these actions would have delivered economically meaningful profits to the trader. That currency carry trades are on average profitable is well-known. The literature accounts for these profits by noting that realized returns exhibit conditional negative skewness, or by presuming that the profits compensate for some other risk. Yet when the trader uses our preferred model of the exchange rate, the ensuing profits exhibit little to no conditional skewness. Intriguingly, we also find that the profits are not compensation for risk, as the excess returns from the carry trade do not covary in a meaningful way with a broad set of conventional risk factors. / The final two papers of my thesis analyze the business cycle. The third chapter of my dissertation, written with Oscar Jorda, investigates the classification of economic activity in the U.S. into expansions and recessions. In the U.S., the Business Cycle Dating Committee (BCDC) of the NBER determines the peaks and troughs of the business cycle. But because there is no universally accepted definition of "recession," the true state of the economy is fundamentally unobservable and the problem of classifying economic data into the two phases of the business cycle is not a trivial undertaking. This chapter evaluates the classification skill of the BCDC relative to several state-of-the-art statistical methodologies designed to describe the unobservable state of the economy. The methods we use are novel to the economics literature and have the advantage of being completely non-parametric. In the final portion of the paper, we introduce a forecasting model to predict future states of the business cycle. / The final chapter of my dissertation, written with Fushing Hsieh, Shu-Chun Chen and Oscar Jorda, concerns business cycles outside of the U.S. The existence of an apolitical, unbiased committee dedicated to determining business cycle peaks and troughs is a luxury that American economists take for granted---only two other economies in the world have such a committee. In most countries, recessions are determined either by the popular press, who can use only rough rules-of-thumb, or by the government, so that such decisions are fraught with political influence. We classify international data into recessions and expansions for 22 OECD economies using a novel classification algorithm. The algorithm is completely non-parametric, a feature that is necessary to complete the analysis because the state-space of the problem makes standard classification methods unfeasible. The algorithm operates by recognizing intervals where economic activity is particularly intense, in the sense that the recurrence time between observations of extreme events (e.g., negative GDP growth) is shorter than would otherwise be expected. We apply the algorithm to monthly country-level data on industrial production, employment and GDP. The resulting binomial series are aggregated in order to produce novel chronologies of country-specific, regional, and global recessions.

I show that relative levels of aggregate consumption and personal oil consumption provide an excellent proxy for oil prices, and that high oil prices predict low future aggregate consumption growth. Motivated by these facts, I add an oil consumption good to the long-run risk model of Bansal and Yaron [2004] to study the asset pricing implications of observed changes in the dynamic interaction of consumption and oil prices. Empirically I observe that, compared to the first half of my 1987--2010 sample, oil consumption growth in the last 10 years is unresponsive to levels of oil prices, creating an decrease in the mean-reversion of oil prices, and an increase in the persistence of oil price shocks. The model implies that the change in the dynamics of oil consumption generates increased systematic risk from oil price shocks due to their increased persistence. However, persistent oil prices also act as a counterweight for shocks to expected consumption growth, with high expected growth creating high expectations of future oil prices which in turn slow down growth. The combined effect is to reduce overall consumption risk and lower the equity premium. The model also predicts that these changes affect the riskiness of of oil futures contracts, and combine to create a hump shaped term structure of oil futures, consistent with recent data.