9 resultados para Capital assets pricing model

em Duke University


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I explore and analyze a problem of finding the socially optimal capital requirements for financial institutions considering two distinct channels of contagion: direct exposures among the institutions, as represented by a network and fire sales externalities, which reflect the negative price impact of massive liquidation of assets.These two channels amplify shocks from individual financial institutions to the financial system as a whole and thus increase the risk of joint defaults amongst the interconnected financial institutions; this is often referred to as systemic risk. In the model, there is a trade-off between reducing systemic risk and raising the capital requirements of the financial institutions. The policymaker considers this trade-off and determines the optimal capital requirements for individual financial institutions. I provide a method for finding and analyzing the optimal capital requirements that can be applied to arbitrary network structures and arbitrary distributions of investment returns.

In particular, I first consider a network model consisting only of direct exposures and show that the optimal capital requirements can be found by solving a stochastic linear programming problem. I then extend the analysis to financial networks with default costs and show the optimal capital requirements can be found by solving a stochastic mixed integer programming problem. The computational complexity of this problem poses a challenge, and I develop an iterative algorithm that can be efficiently executed. I show that the iterative algorithm leads to solutions that are nearly optimal by comparing it with lower bounds based on a dual approach. I also show that the iterative algorithm converges to the optimal solution.

Finally, I incorporate fire sales externalities into the model. In particular, I am able to extend the analysis of systemic risk and the optimal capital requirements with a single illiquid asset to a model with multiple illiquid assets. The model with multiple illiquid assets incorporates liquidation rules used by the banks. I provide an optimization formulation whose solution provides the equilibrium payments for a given liquidation rule.

I further show that the socially optimal capital problem using the ``socially optimal liquidation" and prioritized liquidation rules can be formulated as a convex and convex mixed integer problem, respectively. Finally, I illustrate the results of the methodology on numerical examples and

discuss some implications for capital regulation policy and stress testing.

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We investigate the applicability of the present-value asset pricing model to fishing quota markets by applying instrumental variable panel data estimation techniques to 15 years of market transactions from New Zealand's individual transferable quota (ITQ) market. In addition to the influence of current fishing rents, we explore the effect of market interest rates, risk, and expected changes in future rents on quota asset prices. The results indicate that quota asset prices are positively related to declines in interest rates, lower levels of risk, expected increases in future fish prices, and expected cost reductions from rationalization under the quota system. © 2007 American Agricultural Economics Association.

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This dissertation extends the empirical industrial organization literature with two essays on strategic decisions of firms in imperfectly competitive markets and one essay on how inertia in consumer choice can result in significant welfare losses. Using data from the airline industry I study a well-known puzzle in the literature whereby incumbent firms decrease fares when Southwest Airlines emerges as a potential entrant, but is not (yet) competing directly. In the first essay I describe this so-called Southwest Effect and use reduced-form analysis to offer possible explanations for why firms may choose to forgo profits today rather than wait until Southwest operates the route. The analysis suggests that incumbent firms are attempting to signal to Southwest that entry is unprofitable so as to deter its entry. The second essay develops this theme by extending a classic model from the IO literature, limit pricing, to a dynamic setting. Calibrations indicate the price cuts observed in the data can be captured by a dynamic limit pricing model. The third essay looks at another concentrated industry, mobile telecoms, and studies how inertia in choice (be it inattention or switching costs) can lead to consumers being on poorly matched cellphone plans and how a simple policy proposal can have a considerable effect on welfare.

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There is a general presumption in the literature and among policymakers that immigrant remittances play the same role in economic development as foreign direct investment and other capital flows, but this is an open question. We develop a model of remittances based on the economics of the family that implies that remittances are not profit-driven, but are compensatory transfers, and should have a negative correlation with GDP growth. This is in contrast to the positive correlation of profit-driven capital flows with GDP growth. We test this implication of our model using a new panel data set on remittances and find a robust negative correlation between remittances and GDP growth. This indicates that remittances may not be intended to serve as a source of capital for economic development. © 2005 International Monetary Fund.

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This dissertation consists of three separate studies that examine patterns of immigrant incorporation in the United States. The first study tests competing hypotheses derived from conflicting theoretical frameworks−transnational perspective and cross-national framework− to determine whether transnational engagement and incorporation are concurrent processes among Chinese, Indian, and Mexican immigrants. This study measures transnational engagement and incorporation as home and home country asset ownership using multi-panel, nationally representative data from the New Immigrant Survey (NIS) collected in 2003 and 2007. Results support a cross-border framework and indicate that transnational asset ownership decreases among all immigrant groups, while U.S. asset ownership increases. Findings from this study also indicate that due to disadvantaged pre-migration SES and low human capital, Mexican immigrants are less likely than other immigrants to own home country assets during the year after receiving their green card.

The second study examines the doubly disadvantaged position of elderly immigrants in the U.S. wealth distribution by applying the life course perspective to the dominance-differentiation theory of immigrant wealth stratification. I analyze elderly immigrant wealth in respect to U.S.-born seniors and younger immigrant cohorts using two data sets: the Survey of Income and Program Participation (SIPP) and the New Immigrant Survey (NIS). The Survey of Income and Program Participation (2001 to 2005) is a nationally representative survey of U.S. households. The first series of analyses reveals a significant wealth gap between U.S.- and foreign-born seniors which is most pronounced among the wealthiest households in my sample; however, U.S. tenure explains much of this difference. The second series of analyses suggests that elderly immigrants experience greater barriers to incorporation compared to their younger counterparts.

In the third study, I apply a transnational lens to the forms-of-capital and opportunity structure models of entrepreneurship in order to analyze the role of foreign resources in immigrant business start-ups. I propose that home country property use represents financial, social, and class resources that facilitate immigrant entrepreneurship. I test my hypotheses using survey data on Latin American immigrants from the Comparative Immigrant Entrepreneurship Project. Findings from these analyses suggest that home country asset ownership provides financial and social capital that is related to an increased likelihood of immigrant entrepreneurship.

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I study the link between capital markets and sources of macroeconomic risk. In chapter 1 I show that expected inflation risk is priced in the cross section of stock returns even after controlling for cash flow growth and volatility risks. Motivated by this evidence I study a long run risk model with a built-in inflation non-neutrality channel that allows me to decompose the real stochastic discount factor into news about current and expected cash flow growth, news about expected inflation and news about volatility. The model can successfully price a broad menu of assets and provides a setting for analyzing cross sectional variation in expected inflation risk premium. For industries like retail and durable goods inflation risk can account for nearly a third of the overall risk premium while the energy industry and a broad commodity index act like inflation hedges. Nominal bonds are exposed to expected inflation risk and have inflation premiums that increase with bond maturity. The price of expected inflation risk was very high during the 70's and 80's, but has come down a lot since being very close to zero over the past decade. On average, the expected inflation price of risk is negative, consistent with the view that periods of high inflation represent a "bad" state of the world and are associated with low economic growth and poor stock market performance. In chapter 2 I look at the way capital markets react to predetermined macroeconomic announcements. I document significantly higher excess returns on the US stock market on macro release dates as compared to days when no macroeconomic news hit the market. Almost the entire equity premium since 1997 is being realized on days when macroeconomic news are released. At high frequency, there is a pattern of returns increasing in the hours prior to the pre-determined announcement time, peaking around the time of the announcement and dropping thereafter.

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Background: Since 2007, there has been an ongoing collaboration between Duke University and Mulago National Referral Hospital (NRH) in Kampala, Uganda to increase surgical capacity. This program is prepared to expand to other sites within Uganda to improve neurosurgery outside of Kampala as well. This study assessed the existing progress at Mulago NRH and the neurosurgical needs and assets at two potential sites for expansion. Methods: Three public hospitals were visited to assess needs and assets: Mulago NRH, Mbarara Regional Referral Hospital (RRH), and Gulu RRH. At each site, a surgical capacity tool was administered and healthcare workers were interviewed about perceived needs and assets. A total of 39 interviews were conducted between the three sites. Thematic analysis of the interviews was conducted to identify the reported needs and assets at each hospital. Results: Some improvements are needed to the Duke-Mulago Collaboration model prior to expansion; minor changes to the neurosurgery residency program as well as the method for supply donation and training provided during neurosurgery camps need to examined. Neurosurgery can be implemented at Mbarara RRH currently but the hospital needs a biomedical equipment technician on staff immediately. Gulu RRH is not well positioned for Neurosurgery until there is a CT Scanner somewhere in the Northern Region of Uganda or at the hospital. Conclusions: Neurosurgery is already present in Uganda on a small scale and needs rapid expansion to meet patient needs. This progression is possible with prudent allocation of resources on strategic equipment purchases, human resources including clinical staff and biomedical staff, and changes to the supply chain management system.

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This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.

The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.

The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.

Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.