Theory of foreign portfolio investment

This dissertation examines one category of international capital flows, private portfolio investments (private refers to the source of capital). There is an overall lack of a coherent and consistent definition of foreign portfolio investment. We clarify these definitional issues.^ Two main questions that pertain to private foreign portfolio investments (FPI) are explored. The first problem is the phenomenon of home preference, often referred to as home bias. Related to this are the observed cross-investment flows between countries that seem to contradict the textbook rendition of private FPI. A description of the theories purporting to resolve the home preference puzzle (and the cross-investment one) are summarized and evaluated. Most of this literature considers investors from major developed countries. I consider--as well--whether investors in less developed countries have home preference.^ The dissertation shows that home preference is indeed pervasive and profound across countries, in both developed and emerging markets. For the U.S., I examine home bias in both equity and bond holdings as well. I find that home bias is greater when we look at equity and bond holdings than equity holdings solely.^ In this dissertation a model is developed to explain home bias. This model is original and fills a gap in the literature as there have been no satisfactory models that handle at the same time both home preference and cross-border holdings in the context of information asymmetries. This model reflects what we see in the data and permits us to reach certain results by the use of comparative statics methods. The model suggests, counter-intuitively, that as the rate of return in a country relative to the world rate of return increases, home preference decreases. In the context of our relatively simple model we ascribe this result to the higher variance of the now higher return for home assets. We also find, this time as intended, that as risk aversion increases, investors diversify further so that home preference decreases.^ The second question that the dissertation deals with is the volatility of private foreign portfolio investment. Countries that are recipients of these flows have been wary of such flows because of their perceived volatility. Often the contrast is made with the perceived absence of volatility in foreign direct investment flows. I analyze the validity of these concerns using first net flow data and then gross flow data. The results show that FPI is not, in relative terms, more volatile than other flows in our sample of eight countries (half were developed countries and the rest were emerging markets).^ The implication therefore is that restricting FPI flows may be harmful in the sense that private capital may not be allocated efficiently worldwide to the detriment of capital poor economies. More to the point, any such restrictions would in fact be misguided. ^

Liquidity and asset pricing: A comparison between three-factor and three-moment capital asset pricing models

Liquidity is an important attribute of an asset that investors would like to take into consideration when making investment decisions. However, the previous empirical evidence whether liquidity is a determinant of stock return is not unanimous. This dissertation provides a very comprehensive study about the role of liquidity in asset pricing using the Fama-French (1993) three-factor and Kraus and Litzenberger (1976) three-moment CAPM as models for risk adjustment. The relationship between liquidity and well-known determinants of stock returns such as size and book-to-market are also investigated. This study examines the liquidity and asset pricing issues for both intertemporal as well as cross-sectional data. ^ The results indicate an existence of a liquidity premium, i.e., less liquid stocks would demand higher rate of return than more liquid stocks. More specifically, a drop of 1 percent in liquidity is associated with a higher rate of return of about 2 to 3 basis points per month. Further investigation reveals that neither the Fama-French three-factor model nor the three-moment CAPM captures the liquidity premium. Finally, the results show that well-known determinants of stock return such as size and book-to-market do not serve as proxy for liquidity. ^ Overall, this dissertation shows that a liquidity premium exists in the stock market and that liquidity is a distinct effect, and is not influenced by the presence of non-market factors, market factors and other stock characteristics.^

Estimation of global beta and tests of capital asset pricing models

With the rapid globalization and integration of world capital markets, more and more stocks are listed in multiple markets. With multi-listed stocks, the traditional measurement of systematic risk, the domestic beta, is not appropriate since it only contain information from one market. ^ Prakash et al. (1993) developed a technique, the global beta, to capture information from multiple markets wherein the stocks are listed. In this study, the global betas are obtained as well as domestic betas for 704 multi-listed stocks from 59 world equity markets. Welch tests show that domestic betas are not equal across markets, therefore, global beta is more appropriate in a global investment setting. ^ The traditional Capital Asset Pricing Models (CAPM) is also tested with regards to both domestic beta and global beta. The results generally support the positive relationship between stocks returns and global beta while tend to reject this relationship between stocks returns and domestic beta. Further tests of International CAPM with domestic beta and global beta strengthen the conclusion.^

The role of overseas Chinese investment in the emerging countries of Southeast and East Asia: A Confucian model of the foreign direct investment decision-making process using factors unconsidered in the West

The purpose of this study was to gain a better understanding of the foreign direct investment location decision making process through the examination of non-Western investors and their investment strategies in non-traditional markets. This was accomplished through in-depth personal interviews with 50 Overseas Chinese business owners and executives in several different industries from Hong Kong, Singapore, Taiwan, Malaysia, and Thailand about 97 separate investment projects in Southeast and East Asia, including The Philippines, Malaysia, Hong Kong, Singapore, Vietnam, India, Pakistan, South Korea, Australia, Indonesia, Cambodia, Thailand, Burma, Taiwan, and Mainland China.^ Traditional factors utilized in Western models of the foreign direct investment decision making process are reviewed, as well as literature on Asian management systems and the current state of business practices in emerging countries of Southeast and East Asia. Because of the lack of institutionalization in these markets and the strong influences of Confucian and patriarchal value systems on the Overseas Chinese, it was suspected that while some aspects of Western rational economic models of foreign direct investment are utilized, these models are insufficient in this context, and thus are not fully generalizable to the unique conditions of the Overseas Chinese business network in the region without further modification.^ Thus, other factors based on a Confucian value system need to be integrated into these models. Results from the analysis of structured interviews suggest Overseas Chinese businesses rely more heavily on their network and traditional Confucian values than rational economic factors when making their foreign direct investment location decisions in emerging countries in Asia. This effect is moderated by the firm's industry and the age of the firm's owners. ^

A generic model of execution for synthesizing domain-specific models

Software engineering researchers are challenged to provide increasingly more powerful levels of abstractions to address the rising complexity inherent in software solutions. One new development paradigm that places models as abstraction at the forefront of the development process is Model-Driven Software Development (MDSD). MDSD considers models as first class artifacts, extending the capability for engineers to use concepts from the problem domain of discourse to specify apropos solutions. A key component in MDSD is domain-specific modeling languages (DSMLs) which are languages with focused expressiveness, targeting a specific taxonomy of problems. The de facto approach used is to first transform DSML models to an intermediate artifact in a HLL e.g., Java or C++, then execute that resulting code.^ Our research group has developed a class of DSMLs, referred to as interpreted DSMLs (i-DSMLs), where models are directly interpreted by a specialized execution engine with semantics based on model changes at runtime. This execution engine uses a layered architecture and is referred to as a domain-specific virtual machine (DSVM). As the domain-specific model being executed descends the layers of the DSVM the semantic gap between the user-defined model and the services being provided by the underlying infrastructure is closed. The focus of this research is the synthesis engine, the layer in the DSVM which transforms i-DSML models into executable scripts for the next lower layer to process.^ The appeal of an i-DSML is constrained as it possesses unique semantics contained within the DSVM. Existing DSVMs for i-DSMLs exhibit tight coupling between the implicit model of execution and the semantics of the domain, making it difficult to develop DSVMs for new i-DSMLs without a significant investment in resources.^ At the onset of this research only one i-DSML had been created for the user- centric communication domain using the aforementioned approach. This i-DSML is the Communication Modeling Language (CML) and its DSVM is the Communication Virtual machine (CVM). A major problem with the CVM's synthesis engine is that the domain-specific knowledge (DSK) and the model of execution (MoE) are tightly interwoven consequently subsequent DSVMs would need to be developed from inception with no reuse of expertise.^ This dissertation investigates how to decouple the DSK from the MoE and subsequently producing a generic model of execution (GMoE) from the remaining application logic. This GMoE can be reused to instantiate synthesis engines for DSVMs in other domains. The generalized approach to developing the model synthesis component of i-DSML interpreters utilizes a reusable framework loosely coupled to DSK as swappable framework extensions.^ This approach involves first creating an i-DSML and its DSVM for a second do- main, demand-side smartgrid, or microgrid energy management, and designing the synthesis engine so that the DSK and MoE are easily decoupled. To validate the utility of the approach, the SEs are instantiated using the GMoE and DSKs of the two aforementioned domains and an empirical study to support our claim of reduced developmental effort is performed.^

Exploring business models to provide a foundation for enhanced eye care services in high street optometric practice

High street optometric practices are for-profit businesses. They mostly provide sight testing and eye examination services and sell optical products, such as spectacles and contact lenses. The sight testing services are often sold at a vastly reduced price and profits are generated primarily through high margin spectacle sales, in a loss leading strategy. Published literature highlights weaknesses in this strategy as it forms a barrier to widening the scope of services provided within optometric practices. This includes specialist non-refraction based services, such as shared care. In addition this business strategy discourages investment in advanced diagnostic equipment and higher professional qualifications. The aim of this thesis was to develop a greater understanding of the traditional loss-leading strategy. The thesis also aimed to assess the plausibility of alternative business models to support the development of specialist non-refraction services within high street optometric practice. This research was based on a single independent optometric practice that specialises in advanced retinal imaging and offers a broad range of shared care services. Specialist non-refraction based services were found to be poor generators of spectacle sales likely due to patient needs and presenting concerns. Alternative business strategies to support these services included charging more realistic professional fees via cost-based pricing and monthly payment plans. These strategies enabled specialist services to be more self-sustainable with less reliance on cross-subsidy from spectacle sales. Furthermore, improving operational efficiency can increase stand-alone profits for specialist services.Practice managers may be reluctant to increase professional fees due to market pressures and confidence. However, this thesis found that patients were accepting of increased professional fees. Practice managers can implement alternative business models to enhance eye care provision in high street optometric practices. These alternative business models also improve revenues and profits generated via clinical services and improve patient loyalty.

Capacity Investment in Renewable and Conventional Energy Sources

This dissertation studies capacity investments in energy sources, with a focus on renewable technologies, such as solar and wind energy. We develop analytical models to provide insights for policymakers and use real data from the state of Texas to corroborate our findings.

We first take a strategic perspective and focus on electricity pricing policies. Specifically, we investigate the capacity investments of a utility firm in renewable and conventional energy sources under flat and peak pricing policies. We consider generation patterns and intermittency of solar and wind energy in relation to the electricity demand throughout a day. We find that flat pricing leads to a higher investment level for solar energy and it can still lead to more investments in wind energy if considerable amount of wind energy is generated throughout the day.

In the second essay, we complement the first one by focusing on the problem of matching supply with demand in every operating period (e.g., every five minutes) from the perspective of a utility firm. We study the interaction between renewable and conventional sources with different levels of operational flexibility, i.e., the possibility

of quickly ramping energy output up or down. We show that operational flexibility determines these interactions: renewable and inflexible sources (e.g., nuclear energy) are substitutes, whereas renewable and flexible sources (e.g., natural gas) are complements.

In the final essay, rather than the capacity investments of the utility firms, we focus on the capacity investments of households in rooftop solar panels. We investigate whether or not these investments may cause a utility death spiral effect, which is a vicious circle of increased solar adoption and higher electricity prices. We observe that the current rate-of-return regulation may lead to a death spiral for utility firms. We show that one way to reverse the spiral effect is to allow the utility firms to maximize their profits by determining electricity prices.

A Generic Model of Execution for Synthesizing Domain-Specific Models

Software engineering researchers are challenged to provide increasingly more pow- erful levels of abstractions to address the rising complexity inherent in software solu- tions. One new development paradigm that places models as abstraction at the fore- front of the development process is Model-Driven Software Development (MDSD). MDSD considers models as first class artifacts, extending the capability for engineers to use concepts from the problem domain of discourse to specify apropos solutions. A key component in MDSD is domain-specific modeling languages (DSMLs) which are languages with focused expressiveness, targeting a specific taxonomy of problems. The de facto approach used is to first transform DSML models to an intermediate artifact in a HLL e.g., Java or C++, then execute that resulting code. Our research group has developed a class of DSMLs, referred to as interpreted DSMLs (i-DSMLs), where models are directly interpreted by a specialized execution engine with semantics based on model changes at runtime. This execution engine uses a layered architecture and is referred to as a domain-specific virtual machine (DSVM). As the domain-specific model being executed descends the layers of the DSVM the semantic gap between the user-defined model and the services being provided by the underlying infrastructure is closed. The focus of this research is the synthesis engine, the layer in the DSVM which transforms i-DSML models into executable scripts for the next lower layer to process. The appeal of an i-DSML is constrained as it possesses unique semantics contained within the DSVM. Existing DSVMs for i-DSMLs exhibit tight coupling between the implicit model of execution and the semantics of the domain, making it difficult to develop DSVMs for new i-DSMLs without a significant investment in resources. At the onset of this research only one i-DSML had been created for the user- centric communication domain using the aforementioned approach. This i-DSML is the Communication Modeling Language (CML) and its DSVM is the Communication Virtual machine (CVM). A major problem with the CVM’s synthesis engine is that the domain-specific knowledge (DSK) and the model of execution (MoE) are tightly interwoven consequently subsequent DSVMs would need to be developed from inception with no reuse of expertise. This dissertation investigates how to decouple the DSK from the MoE and sub- sequently producing a generic model of execution (GMoE) from the remaining appli- cation logic. This GMoE can be reused to instantiate synthesis engines for DSVMs in other domains. The generalized approach to developing the model synthesis com- ponent of i-DSML interpreters utilizes a reusable framework loosely coupled to DSK as swappable framework extensions. This approach involves first creating an i-DSML and its DSVM for a second do- main, demand-side smartgrid, or microgrid energy management, and designing the synthesis engine so that the DSK and MoE are easily decoupled. To validate the utility of the approach, the SEs are instantiated using the GMoE and DSKs of the two aforementioned domains and an empirical study to support our claim of reduced developmental effort is performed.

Improved models for estimating temporal changes in carbon sequestration in above-ground biomass of mixed-species environmental plantings

Plantings of mixed native species (termed 'environmental plantings') are increasingly being established for carbon sequestration whilst providing additional environmental benefits such as biodiversity and water quality. In Australia, they are currently one of the most common forms of reforestation. Investment in establishing and maintaining such plantings relies on having a cost-effective modelling approach to providing unbiased estimates of biomass production and carbon sequestration rates. In Australia, the Full Carbon Accounting Model (FullCAM) is used for both national greenhouse gas accounting and project-scale sequestration activities. Prior to undertaking the work presented here, the FullCAM tree growth curve was not calibrated specifically for environmental plantings and generally under-estimated their biomass. Here we collected and analysed above-ground biomass data from 605 mixed-species environmental plantings, and tested the effects of several planting characteristics on growth rates. Plantings were then categorised based on significant differences in growth rates. Growth of plantings differed between temperate and tropical regions. Tropical plantings were relatively uniform in terms of planting methods and their growth was largely related to stand age, consistent with the un-calibrated growth curve. However, in temperate regions where plantings were more variable, key factors influencing growth were planting width, stand density and species-mix (proportion of individuals that were trees). These categories provided the basis for FullCAM calibration. Although the overall model efficiency was only 39-46%, there was nonetheless no significant bias when the model was applied to the various planting categories. Thus, modelled estimates of biomass accumulation will be reliable on average, but estimates at any particular location will be uncertain, with either under- or over-prediction possible. When compared with the un-calibrated yield curves, predictions using the new calibrations show that early growth is likely to be more rapid and total above-ground biomass may be higher for many plantings at maturity. This study has considerably improved understanding of the patterns of growth in different types of environmental plantings, and in modelling biomass accumulation in young (<25. years old) plantings. However, significant challenges remain to understand longer-term stand dynamics, particularly with temporal changes in stand density and species composition. © 2014.

Decisions on investment allocation in the post-keynesian growth model

Neste artigo os modelos de crescimento e alocação de investimento a la Feldman-Mahalanobis são estendidos para considerar a análise de decisões de alocação de investimento no contexto do modelo de crescimento pós-Keynesiano. Ao adotar essa abordagem é possível introduzir características distributivas no modelo de Feldman-Mahalanobis que nos permitem determinar a taxa de alocação de investimentos de acordo com as decisões de equilíbrio entre investimento e poupança. Finalmente, uma condição adicional é adicionada ao modelo de crescimento pós-keynesiano, a fim de caracterizar plenamente o caminho de equilíbrio em uma versão estendida deste, onde bens de capital também são necessários para produzir bens de capital. _________________________________________________________________________________ ABSTRACT

Multi-level, Multi-stage and Stochastic Optimization Models for Energy Conservation in Buildings for Federal, State and Local Agencies

Energy Conservation Measure (ECM) project selection is made difficult given real-world constraints, limited resources to implement savings retrofits, various suppliers in the market and project financing alternatives. Many of these energy efficient retrofit projects should be viewed as a series of investments with annual returns for these traditionally risk-averse agencies. Given a list of ECMs available, federal, state and local agencies must determine how to implement projects at lowest costs. The most common methods of implementation planning are suboptimal relative to cost. Federal, state and local agencies can obtain greater returns on their energy conservation investment over traditional methods, regardless of the implementing organization. This dissertation outlines several approaches to improve the traditional energy conservations models. Any public buildings in regions with similar energy conservation goals in the United States or internationally can also benefit greatly from this research. Additionally, many private owners of buildings are under mandates to conserve energy e.g., Local Law 85 of the New York City Energy Conservation Code requires any building, public or private, to meet the most current energy code for any alteration or renovation. Thus, both public and private stakeholders can benefit from this research. The research in this dissertation advances and presents models that decision-makers can use to optimize the selection of ECM projects with respect to the total cost of implementation. A practical application of a two-level mathematical program with equilibrium constraints (MPEC) improves the current best practice for agencies concerned with making the most cost-effective selection leveraging energy services companies or utilities. The two-level model maximizes savings to the agency and profit to the energy services companies (Chapter 2). An additional model presented leverages a single congressional appropriation to implement ECM projects (Chapter 3). Returns from implemented ECM projects are used to fund additional ECM projects. In these cases, fluctuations in energy costs and uncertainty in the estimated savings severely influence ECM project selection and the amount of the appropriation requested. A risk aversion method proposed imposes a minimum on the number of “of projects completed in each stage. A comparative method using Conditional Value at Risk is analyzed. Time consistency was addressed in this chapter. This work demonstrates how a risk-based, stochastic, multi-stage model with binary decision variables at each stage provides a much more accurate estimate for planning than the agency’s traditional approach and deterministic models. Finally, in Chapter 4, a rolling-horizon model allows for subadditivity and superadditivity of the energy savings to simulate interactive effects between ECM projects. The approach makes use of inequalities (McCormick, 1976) to re-express constraints that involve the product of binary variables with an exact linearization (related to the convex hull of those constraints). This model additionally shows the benefits of learning between stages while remaining consistent with the single congressional appropriations framework.

Developing real option game models

By mixing concepts from both game theoretic analysis and real options theory, an investment decision in a competitive market can be seen as a ‘‘game’’ between firms, as firms implicitly take into account other firms’ reactions to their own investment actions. We review two decades of real option game models, suggesting which critical problems have been ‘‘solved’’ by considering game theory, and which significant problems have not been yet adequately addressed. We provide some insights on the plausible empirical applications, or shortfalls in applications to date, and suggest some promising avenues for future research.

Characterization Of Microsatellite Markers Developed From Prosopis Rubriflora And Prosopis Ruscifolia (leguminosae - Mimosoideae), Legume Species That Are Used As Models For Genetic Diversity Studies In Chaquenian Areas Under Anthropization In South America.

Prosopis rubriflora and Prosopis ruscifolia are important species in the Chaquenian regions of Brazil. Because of the restriction and frequency of their physiognomy, they are excellent models for conservation genetics studies. The use of microsatellite markers (Simple Sequence Repeats, SSRs) has become increasingly important in recent years and has proven to be a powerful tool for both ecological and molecular studies. In this study, we present the development and characterization of 10 new markers for P. rubriflora and 13 new markers for P. ruscifolia. The genotyping was performed using 40 P. rubriflora samples and 48 P. ruscifolia samples from the Chaquenian remnants in Brazil. The polymorphism information content (PIC) of the P. rubriflora markers ranged from 0.073 to 0.791, and no null alleles or deviation from Hardy-Weinberg equilibrium (HW) were detected. The PIC values for the P. ruscifolia markers ranged from 0.289 to 0.883, but a departure from HW and null alleles were detected for certain loci; however, this departure may have resulted from anthropic activities, such as the presence of livestock, which is very common in the remnant areas. In this study, we describe novel SSR polymorphic markers that may be helpful in future genetic studies of P. rubriflora and P. ruscifolia.

Censored Linear Regression Models For Irregularly Observed Longitudinal Data Using The Multivariate-t Distribution.

In acquired immunodeficiency syndrome (AIDS) studies it is quite common to observe viral load measurements collected irregularly over time. Moreover, these measurements can be subjected to some upper and/or lower detection limits depending on the quantification assays. A complication arises when these continuous repeated measures have a heavy-tailed behavior. For such data structures, we propose a robust structure for a censored linear model based on the multivariate Student's t-distribution. To compensate for the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is employed. An efficient expectation maximization type algorithm is developed for computing the maximum likelihood estimates, obtaining as a by-product the standard errors of the fixed effects and the log-likelihood function. The proposed algorithm uses closed-form expressions at the E-step that rely on formulas for the mean and variance of a truncated multivariate Student's t-distribution. The methodology is illustrated through an application to an Human Immunodeficiency Virus-AIDS (HIV-AIDS) study and several simulation studies.

Augmented Mixed Models For Clustered Proportion Data.

Often in biomedical research, we deal with continuous (clustered) proportion responses ranging between zero and one quantifying the disease status of the cluster units. Interestingly, the study population might also consist of relatively disease-free as well as highly diseased subjects, contributing to proportion values in the interval [0, 1]. Regression on a variety of parametric densities with support lying in (0, 1), such as beta regression, can assess important covariate effects. However, they are deemed inappropriate due to the presence of zeros and/or ones. To evade this, we introduce a class of general proportion density, and further augment the probabilities of zero and one to this general proportion density, controlling for the clustering. Our approach is Bayesian and presents a computationally convenient framework amenable to available freeware. Bayesian case-deletion influence diagnostics based on q-divergence measures are automatic from the Markov chain Monte Carlo output. The methodology is illustrated using both simulation studies and application to a real dataset from a clinical periodontology study.