Three Essays in Empirical Asset Pricing

The financial crisis of 2007-2008 led to extraordinary government intervention in firms and markets. The scope and depth of government action rivaled that of the Great Depression. Many traded markets experienced dramatic declines in liquidity leading to the existence of conditions normally assumed to be promptly removed via the actions of profit seeking arbitrageurs. These extreme events motivate the three essays in this work. The first essay seeks and fails to find evidence of investor behavior consistent with the broad 'Too Big To Fail' policies enacted during the crisis by government agents. Only in limited circumstances, where government guarantees such as deposit insurance or U.S. Treasury lending lines already existed, did investors impart a premium to the debt security prices of firms under stress. The second essay introduces the Inflation Indexed Swap Basis (IIS Basis) in examining the large differences between cash and derivative markets based upon future U.S. inflation as measured by the Consumer Price Index (CPI). It reports the consistent positive value of this measure as well as the very large positive values it reached in the fourth quarter of 2008 after Lehman Brothers went bankrupt. It concludes that the IIS Basis continues to exist due to limitations in market liquidity and hedging alternatives. The third essay explores the methodology of performing debt based event studies utilizing credit default swaps (CDS). It provides practical implementation advice to researchers to address limited source data and/or small target firm sample size.

México: capital humano e ingresos. Retornos a la educación, 1994-2005

Incluye Bibliografía

Measuring Returns to Education and Human Capital in the Southern Mediterranean. MEDPRO Technical Report No. 17/September 2012

Measuring human capital has been a significant challenge for economists because the main variable of interest is intangible and not directly observable. In the Middle Eastern and Northern African region the task is further complicated by the general scarcity of comparable and reliable data. This study overcomes these challenges by relying on a unique international survey that covers most of the region and by deriving a market-based measure that uses returns to education and various labour market factors as guidance. The results show that private returns to schooling are relatively low in most southern Mediterranean countries (SMC). Israel and Turkey are clear outliers, surpassing even the EU-MED averages. In Algeria and Jordan, the returns are almost flat, implying that earnings do not respond significantly to education levels. Despite high attainment levels, Greece, Spain and Portugal also perform badly; only marginally surpassing some of the bottom-ranked SMC, providing evidence of problems in absorption capacity. The baseline scenarios for 2030 show substantial sensitivity to current estimates on returns to education. In particular, improving attainment levels can produce measurable gains in the future only when the returns to education are already high. Such is the case for Egypt, Morocco and Turkey, which substantially improve their human capital stocks under the baseline scenarios, surpassing several EU-MED countries with little or no room for improvement.

Selling new neighborhoods as good for walking : issues for measuring self-selection

Background: Self-selection-whether individuals inclined to walk more seek to live in walkable environments-must be accounted for when studying built environment influences on walking. The way neighborhoods are marketed to future residents has the potential to sway residential location choice, and may consequently affect measures of self-selection related to location preferences. We assessed how walking opportunities are promoted to potential buyers, by examining walkability attributes in marketing materials for housing developments. Methods: A content analysis of marketing materials for 32 new housing developments in Perth, Australia was undertaken, to assess how walking was promoted in the text and pictures. Housing developments designed to be pedestrian-friendly (LDs) were compared with conventional developments (CDs). Results: Compared with CDs, LD marketing materials had significantly more references to 'public transport,' small home sites,' walkable parks/open space,' ease of cycling,' safe environment,' and 'boardwalks.' Other walkability attributes approached significance. Conclusion: Findings suggest the way neighborhoods are marketed may contribute to self-reported reasons for choosing particular neighborhoods, especially when attributes are not present at the time of purchase. The marketing of housing developments may be an important factor to consider when measuring self-selection, and its influence on the built environment and walking relationship.

Measuring Online user engagement : the limitations of Web analytics

The ability to identify and assess user engagement with transmedia productions is vital to the success of individual projects and the sustainability of this mode of media production as a whole. It is essential that industry players have access to tools and methodologies that offer the most complete and accurate picture of how audiences/users engage with their productions and which assets generate the most valuable returns of investment. Drawing upon research conducted with Hoodlum Entertainment, a Brisbane-based transmedia producer, this chapter outlines an initial assessment of the way engagement tends to be understood, why standard web analytics tools are ill-suited to measuring it, how a customised tool could offer solutions, and why this question of measuring engagement is so vital to the future of transmedia as a sustainable industry.

STM verification of the reduction of the Young's modulus of CdS nanoparticles at smaller sizes

We demonstrate the first STM evaluation of the Young's modulus (E) of nanoparticles (NPs) of different sizes. The sample deformation induced by tip-sample interaction has been determined using current-distance (I-Z) spectroscopy. As a result of tip-sample interaction, and the induced surface deformations, the I-z curves deviates from pure exponential dependence. Normally, in order to analyze the deformation quantitatively, the tip radius must be known. We show, that this necessity is eliminated by measuring the deformation on a substrate with a known Young's modulus (Au(111)) and estimating the tip radius, and afterwards, using the same tip (with a known radius) to measure the (unknown) Young's modulus of another sample (nanoparticles of CdS). The Young's modulus values found for 3 NP's samples of average diameters of 3.7, 6 and 7.5 nm, were E similar to 73%, 78% and 88% of the bulk value, respectively. These results are in a good agreement with the theoretically predicted reduction of the Young's modulus due to the changes in hydrostatic stresses which resulted from surface tension in nanoparticles with different sizes. Our calculation using third order elastic constants gives a reduction of E which scales linearly with 1/r (r is the NP's radius). This demonstrates the applicability of scanning tunneling spectroscopy for local mechanical characterization of nanoobjects. The method does not include a direct measurement of the tip-sample force but is rather based on the study of the relative elastic response. (C) 2014 Elsevier B.V. All rights reserved.

Measuring the impact of short and lending practices using the DGTW benchmarks

Mutual fund managers increasingly lend their holdings and/or use short sales to generate higher returns for their funds. This project presents a first look into the impact these practices on performance using the performance measures: i) Characteristic Selectivity (CS), the ability of the fund's managers to choose stocks that outperform their benchmarks; ii) Characteristic Timing (CT), the ability of the manager to time the market; iii) and Average Style (AS), the returns from funds systematically holding stocks with certain characteristics. These returns are computed through the DGTW benchmarks. The effect of other variables that have also been shown to impact fund’s returns – total net assets under management, investment styles, turnover and expense ratios – will also be analyzed. I find that managers who use short-sales do not exhibit better stock picking abilities than those who do not, while mutual funds that lend do present higher CS returns. In addition, while lending is not significant for the total performance of a fund, the employment of short-sales and of both short-sales and lending has a negative impact on the fund’s performance.

Exploring users inter-temporal preferences for measuring transport social equity effects

There exist different ways for defining a welfare function. Traditionally, welfare economic theory foundation is based on the Net Present Value (NPV) calculation where the time dependent preferences of considered agents are taken into account. However, the time preferences, remains a controversial subject. Currently, the traditional approach employs a unique discount rate for various agents. Nevertheless, this way of discounting appears inconsistent with sustainable development. New research work suggests that the discount rate may not be a homogeneous value. The discount rates may change following the individual’s preferences. A significant body of evidence suggests that people do not behave following a constant discount rate. In fact, UK Government has quickly recognized the power of the arguments for time-varying rates, as it has done in its official guidance to Ministries on the appraisal of investments and policies. Other authors deal with not just time preference but with uncertainty about future income (precautionary saving). In a situation in which economic growth rates are similar across time periods, the rationale for declining social optimal discount rates is driven by the preferences of the individuals in the economy, rather than expectations of growth. However, these approaches have been mainly focused on long-term policies where intergenerational risks may appear. The traditional cost-benefit analysis (CBA) uses a unique discount rate derived from market interest rates or investment rates of return for discounting the costs and benefits of all social agents included in the CBA. However, recent literature showed that a more adequate measure of social benefit is possible by using different discount rates including inter-temporal preferences rate of users, private investment discount rate and intertemporal preferences rate of government. Actually, the costs of opportunity may differ amongst individuals, firms, governments, or society in general, as do the returns on savings. In general, the firms or operators require an investment rate linked to the current return on savings, while the discount rate of consumers-users depends on their time preferences with respect of the current and the future consumption, as well as society can take into account the intergenerational well-being, adopting a lower discount rate for today’s generation. Time discount rate of social actors (users, operators, government and society) places a lower value in a future gain, but the uncertainty about future income strongly determines the individual preferences. These time and uncertainty depends on preferences and should be integrated into a transport policy formulation that may have significant social impacts. The discount rate of a user cannot be the same than the operator’s discount rate. The preferences of both are different. In addition, another school of thought suggests that people, such as a social group, may have different attitudes towards future costs and benefits. Particularly, the users have different discount rates related to their income. Some research work tried to modify user discount rates using a compensating weight which represents the inverse of household income level. The inter-temporal preferences are a proxy of the willingness to pay during the time. Its consideration is important in order to make acceptable or not a policy or investment

Measuring science: An exploration

This paper examines the available United States data on academic research and development (R&D) expenditures and the number of papers published and the number of citations to these papers as possible measures of “output” of this enterprise. We look at these numbers for science and engineering as a whole, for five selected major fields, and at the individual university field level. The published data in Science and Engineering Indicators imply sharply diminishing returns to academic R&D using published papers as an “output” measure. These data are quite problematic. Using a newer set of data on papers and citations, based on an “expanding” set of journals and the newly released Bureau of Economic Analysis R&D deflators, changes the picture drastically, eliminating the appearance of diminishing returns but raising the question of why the input prices of academic R&D are rising so much faster than either the gross domestic product deflator or the implicit R&D deflator in industry. A production function analysis of such data at the individual field level follows. It indicates significant diminishing returns to “own” R&D, with the R&D coefficients hovering around 0.5 for estimates with paper numbers as the dependent variable and around 0.6 if total citations are used as the dependent variable. When we substitute scientists and engineers in place of R&D as the right-hand side variables, the coefficient on papers rises from 0.5 to 0.8, and the coefficient on citations rises from 0.6 to 0.9, indicating systematic measurement problems with R&D as the sole input into the production of scientific output. But allowing for individual university field effects drives these numbers down significantly below unity. Because in the aggregate both paper numbers and citations are growing as fast or faster than R&D, this finding can be interpreted as leaving a major, yet unmeasured, role for the contribution of spillovers from other fields, other universities, and other countries.

Measuring market power in electricity generation: A long-term perspective using a programming model

This paper focuses on measuring the extent to which market power has been exercised in a recently deregulated electricity generation sector. Our study emphasises the need to consider the concept of market power in a long-run dynamic context. A market power index is constructed focusing on differences between actual market returns and long-run competitive returns, estimated using a programming model devised by the authors. The market power implications of hedge contracts are briefly considered. The state of Queensland Australia is used as a context for the analysis. The results suggest that generators have exercised significant market power since deregulation.

Using extreme value theory to value stock market returns

Extreme stock price movements are of great concern to both investors and the entire economy. For investors, a single negative return, or a combination of several smaller returns, can possible wipe out so much capital that the firm or portfolio becomes illiquid or insolvent. If enough investors experience this loss, it could shock the entire economy. An example of such a case is the stock market crash of 1987. Furthermore, there has been a lot of recent interest regarding the increasing volatility of stock prices. ^ This study presents an analysis of extreme stock price movements. The data utilized was the daily returns for the Standard and Poor's 500 index from January 3, 1978 to May 31, 2001. Research questions were analyzed using the statistical models provided by extreme value theory. One of the difficulties in examining stock price data is that there is no consensus regarding the correct shape of the distribution function generating the data. An advantage with extreme value theory is that no detailed knowledge of this distribution function is required to apply the asymptotic theory. We focus on the tail of the distribution. ^ Extreme value theory allows us to estimate a tail index, which we use to derive bounds on the returns for very low probabilities on an excess. Such information is useful in evaluating the volatility of stock prices. There are three possible limit laws for the maximum: Gumbel (thick-tailed), Fréchet (thin-tailed) or Weibull (no tail). Results indicated that extreme returns during the time period studied follow a Fréchet distribution. Thus, this study finds that extreme value analysis is a valuable tool for examining stock price movements and can be more efficient than the usual variance in measuring risk. ^

Measuring, Modeling, and Forecasting Volatility and Correlations from High-Frequency Data

This dissertation contains four essays that all share a common purpose: developing new methodologies to exploit the potential of high-frequency data for the measurement, modeling and forecasting of financial assets volatility and correlations. The first two chapters provide useful tools for univariate applications while the last two chapters develop multivariate methodologies. In chapter 1, we introduce a new class of univariate volatility models named FloGARCH models. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the models in a realistic numerical study and on the basis of a data set composed of 65 equities. Using more than 10 years of high-frequency transactions, we document significant statistical gains related to the FloGARCH models in terms of in-sample fit, out-of-sample fit and forecasting accuracy compared to classical and Realized GARCH models. In chapter 2, using 12 years of high-frequency transactions for 55 U.S. stocks, we argue that combining low-frequency exogenous economic indicators with high-frequency financial data improves the ability of conditionally heteroskedastic models to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multi-period Value-at-Risk. Using a refined version of the Realized LGARCH model allowing for time-varying intercept and implemented with realized kernels, we document that nominal corporate profits and term spreads have strong long-run predictive ability and generate accurate risk measures forecasts over long-horizon. The results are based on several loss functions and tests, including the Model Confidence Set. Chapter 3 is a joint work with David Veredas. We study the class of disentangled realized estimators for the integrated covariance matrix of Brownian semimartingales with finite activity jumps. These estimators separate correlations and volatilities. We analyze different combinations of quantile- and median-based realized volatilities, and four estimators of realized correlations with three synchronization schemes. Their finite sample properties are studied under four data generating processes, in presence, or not, of microstructure noise, and under synchronous and asynchronous trading. The main finding is that the pre-averaged version of disentangled estimators based on Gaussian ranks (for the correlations) and median deviations (for the volatilities) provide a precise, computationally efficient, and easy alternative to measure integrated covariances on the basis of noisy and asynchronous prices. Along these lines, a minimum variance portfolio application shows the superiority of this disentangled realized estimator in terms of numerous performance metrics. Chapter 4 is co-authored with Niels S. Hansen, Asger Lunde and Kasper V. Olesen, all affiliated with CREATES at Aarhus University. We propose to use the Realized Beta GARCH model to exploit the potential of high-frequency data in commodity markets. The model produces high quality forecasts of pairwise correlations between commodities which can be used to construct a composite covariance matrix. We evaluate the quality of this matrix in a portfolio context and compare it to models used in the industry. We demonstrate significant economic gains in a realistic setting including short selling constraints and transaction costs.

Clinical Accuracy of the MedGem Indirect Calorimeter for Measuring Resting Energy Expenditure in Cancer Patients

OBJECTIVE: To compare, in patients with cancer and in healthy subjects, measured resting energy expenditure (REE) from traditional indirect calorimetry to a new portable device (MedGem) and predicted REE. DESIGN: Cross-sectional clinical validation study. SETTING: Private radiation oncology centre, Brisbane, Australia. SUBJECTS: Cancer patients (n = 18) and healthy subjects (n = 17) aged 37-86 y, with body mass indices ranging from 18 to 42 kg/m(2). INTERVENTIONS: Oxygen consumption (VO(2)) and REE were measured by VMax229 (VM) and MedGem (MG) indirect calorimeters in random order after a 12-h fast and 30-min rest. REE was also calculated from the MG without adjustment for nitrogen excretion (MGN) and estimated from Harris-Benedict prediction equations. Data were analysed using the Bland and Altman approach, based on a clinically acceptable difference between methods of 5%. RESULTS: The mean bias (MGN-VM) was 10% and limits of agreement were -42 to 21% for cancer patients; mean bias -5% with limits of -45 to 35% for healthy subjects. Less than half of the cancer patients (n = 7, 46.7%) and only a third (n = 5, 33.3%) of healthy subjects had measured REE by MGN within clinically acceptable limits of VM. Predicted REE showed a mean bias (HB-VM) of -5% for cancer patients and 4% for healthy subjects, with limits of agreement of -30 to 20% and -27 to 34%, respectively. CONCLUSIONS: Limits of agreement for the MG and Harris Benedict equations compared to traditional indirect calorimetry were similar but wide, indicating poor clinical accuracy for determining the REE of individual cancer patients and healthy subjects.