Thesis advisor: Zhijie Xiao / This dissertation contains three essays. It provides an application of quantile regression in Financial Economics. The first essay investigates whether tail dependence makes a difference in the estimation of systemic risk. This chapter develops a common framework based on a copula model to estimate several popular return-based systemic risk measures: Delta Conditional Value at Risk (ΔCoVaR) and its modification; and Marginal Expected Shortfall (MES) and its extension, systemic risk measure (SRISK). By eliminating the discrepancy of the marginal distribution, copula models provide the flexibility to concentrate only on the effects of dependence structure on the systemic risk measure. We estimate the systemic risk contributions of four financial industries consisting of a large number of institutions for the sample period from January 2000 to December 2010. First, we found that the linear quantile regression estimation of ΔCoVaR, proposed by Adrian and Brunnermeier (AB hereafter) (2011), is inadequate to completely capture the non-linear contagion tail effect, which tends to underestimate systemic risk in the presence of lower tail dependence. Second, ΔCoVaR originally proposed by AB (2011) is in conflict with dependence measures. By comparison, the modified version of ΔCoVaR put forward by Girardi et al. (2011) and MES, proposed by Acharya et al. (2010), are more consistent with dependence measures, which conforms with the widely held notion that stronger dependence strength results in higher systemic risk. Third, the modified ΔCoVaR is observed to have a strong correlation with tail dependence. In contrast, MES is found to have a strong empirical relationship with firms' conditional CAPM beta. SRISK, however, provides further connection with firms' level characteristics by accounting for information on market capitalization and liability. This stylized fact seems to imply that ΔCoVaR is more in line with the ``too interconnected to fail" paradigm, while SRISK is more related to the ``too big to fail" paradigm. In contrast, MES offers a compromise between these two paradigms. The second essay proposes a quantile regression approach to stock return prediction. I show that incorporating distributional information together with combining model information can produce a superior forecast for the conditional mean as well as the entire distribution of future equity premium, which significantly outperforms the forecast that utilizes either source of information alone. Meanwhile, the order of combination strategies appears to make a difference in the efficiency of pooling both distributional information and model information. It turns out that aggregating distributional information in the first step, followed by combining model information in the second step is more advantageous in return forecast than the alternative combination strategies which reverse the order of combination strategy. Furthermore, the forecast based on LASSO model selection can be significantly improved as well if the distributional information is further incorporated. In other word, aggregating distributional information via combining multiple quantiles estimators contributes to the improvement of forecasts obtained either from model combination or model selection. This paper not only investigates the forecast of conditional mean, but also studies the forecast of the whole distribution of future stock returns. The approaches of quantile combination together with either model combination or model selection turn out to deliver statistically and economically significant out-of-sample forecasts relative to a historical average benchmark. The third essay proposes a quantile-based approach to efficiently estimate the conditional beta coefficient without assuming a parametric structure on the distribution of data generating process. Multiple quantiles estimates are combined in a weighting scheme to utilize distributional information across different quantile of the distribution. Monte Carlo simulation demonstrated that combining multiple quantile estimates can substantially improve the estimation efficiency for beta risk estimates in the absence of Gaussian distribution. The robustness of quantile-based beta estimates are pronounced during financial crisis when the distribution of stock returns deviates most from normality. I also explored the performance of different beta estimators in an application of portfolio management analysis and found that beta estimates from the proposed quantile combination approaches are superior to the OLS estimates in constructing Global Minimum Variance Portfolio, which generates lower variance of portfolio but does not come at the expense of persistent lower returns. / Thesis (PhD) — Boston College, 2013. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

The goal of this thesis is to propose a new quantile regression approach to identify and estimate the quantiles of the private value conditional distribution in ascending and rst price auctions under the Independent Private Value (IPV) paradigm. The quantile regression framework provides a exible and convenient parametrization of the private value distribution, which is not a ected by the curse of dimensionality. The rst Chapter of the thesis introduces a quantile regression methodology for ascending auctions. The Chapter focuses on revenue analysis, optimal reservation price and its associated screening level. An empirical application for the USFS timber auctions suggests an optimal reservation price policy with a probability of selling the good as low as 58% for some auctions with two bidders. The second Chapter tries to address this issue by considering a risk averse seller with a CRRA utility function. A numerical exercise based on the USFS timber auctions shows that increasing the CRRA of the sellers is su cient to give more reasonable policy recommendations and a higher probability of selling the auctioned timber lot. The third Chapter develops a quantile regression methodology for rst-price auction. The estimation method combines local polynomial, quantile regression and additive sieve methods. It is shown in addition that the new quantile regression methodology is not subject to boundary issues. The choice of smoothing parameters is also discussed.

The motivation for this thesis came from the provision of a large data set from Saudi Arabia giving anthropometric measurements of children and adolescents from birth to eighteen years of age, with a requirement to construct growth charts. The construction of these growth charts revealed a number of issues particularly in the respect to statistical inference relating to quantile regression. To investigate a range of different statistical inference procedures in parametric quantile regression in particular the estimation of the confidence limits of the ?th (?? [0, 1]) quantile, a number of sets of simulated data in which various error structures are imposed including homoscedastic and heteroscedastic structures were developed. Methods from the statistical literature were then compared with a method proposed within this thesis based on the idea of Silverman's (1986) kernel smoothing. This proposed bootstrapping method requires the estimation of the conditional variance function of the fitted quantile. The performance of a variety of variance estimation methods combined within the proposed bootstrapping procedure are assessed under various data structures in order to examine the performance of the proposed bootstrapping approach. The validity of the proposed bootstrapping method is then illustrated using the Saudi Arabian anthropometric data.

Statistics, as a critical component in transportation research, has been widely used to analyze driver safety, travel time, traffic flow and numerous other problems. Many of these popular topics can be interpreted as to establish the statistical models for the latent structure of data. Over the past several years, the interest in latent class models has continuously increased due to their great potential in solving practical problems. In this dissertation, I developed several latent class models to quantitatively analyze the hidden structure of transportation data and addressed related application issues. The first model is focused on the uncertainty of travel time, which is critical for assessing the reliability of transportation systems. Travel time is random in nature, and contains substantial variability, especially under congested traffic conditions. A Bayesian mixture model, with the ability to incorporate the influence from covariates such as traffic volume, has been proposed. This model advances the previous multi-state travel time reliability model in which the relationship between response and predictors was lacking. The Bayesian mixture travel time model, however, lack the power to accurately predict the future travel time. The analysis indicates that the independence assumption, which is difficult to justify in real data, could be a potential issue. Therefore, I proposed a Hidden Markov model to accommodate dependency structure, and the modeling results were significantly improved. The second and third parts of the dissertation focus on the driver safety identification. Given the demographic information and crash history, the number of crashes, as a type of count data, is commonly modeled by Poisson regression. However, the over-dispersion issue within the data implies that a single Poisson distribution is insufficient to depict the substantial variability. Poisson mixture model is proposed and applied to identify risky and safe drivers. The lower bound of the estimated misclassification rate is evaluated using the concept of overlap probability. Several theoretical results have been discussed regarding the overlap probability. I also introduced quantile regression based on discrete data to specifically model the high-risk drivers. In summary, the major objective of my research is to develop latent class methods and explore the hidden structure within the transportation data, and the approaches I employed can also be implemented for similar research questions in other areas. / Ph. D.

Thesis advisor: Zhijie Xiao / My dissertation research examines empirical issues in financial economics with a special focus on the application of quantile regression. This dissertation is composed by two self-contained papers, which center around: (1) robust estimation of conditional idiosyncratic volatility of asset returns to offer better understanding of market microstructure and asset pricing anomalies; (2) implementation of coherent risk measures in portfolio selection and financial risk management. The first chapter analyzes the roles of idiosyncratic risk and firm-level conditional skewness in determining cross-sectional returns. It is shown that the traditional EGARCH estimates of conditional idiosyncratic volatility may bring significant finite sample estimation errors in the presence of non-Gaussianity, casting strong doubt on the positive intertemporal idiosyncratic volatility effect reported in the literature. We propose an alternative estimator for conditional idiosyncratic volatility for GARCH-type models. The proposed estimation method does not require error distribution assumptions and is robust non-Gaussian innovations. Monte Carlo evidence indicates that the proposed estimator has much improved sampling performance over the EGARCH MLE in the presence of heavy-tail or skewed innovations. Our cross-section portfolio analysis demonstrates that the idiosyncratic volatility puzzle documented by Ang, Hodrick, Xiang and Zhang (2006) exists intertemporally, i.e., stocks with high conditional idiosyncratic volatility earn abnormally low returns. We solve the major piece of this puzzle by pointing out that previous empirical studies have failed to consider both idiosyncratic variance and individual conditional skewness in determining cross-sectional returns. We introduce a new concept - the "expected windfall" - as an alternative measure of conditional return skewness. After controlling for these two additional factors, cross-sectional regression tests identify a positive relationship between conditional idiosyncratic volatility and expected returns for over 99% of the total market capitalization of the NYSE, NASDAQ, and AMEX stock exchanges. The second chapter examines portfolio allocation decision for investors with general pessimistic preferences (GPP) regarding downside risk aversion and out-performing benchmark returns. I show that the expected utility of pessimistic investors can be robustly estimated within a quantile regression framework without assuming asset return distributions. The asymptotic properties of the optimal portfolio weights are derived. Empirically, this method is introduced to construct the optimal fund of CSFB/Tremont hedge-fund indices. Both the in-sample and out-of-sample backtesting results confirm that the optimal mean-GPP portfolio outperforms the mean-variance and mean-conditional VaR portfolios. / Thesis (PhD) — Boston College, 2009. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

Governments around the world have regulated multinational enterprises to adopt arm¡¦s length transactions to facilitate identifications and comparisons between non-transfer pricing transactions with independent, non-related enterprises and transactions with related enterprises that are suspected of transfer pricing. Currently, most of the optimal transfer pricing methods for establishing arm¡¦s length principles for multinational enterprises have been addressed in Organization for Economic Cooperation and Development's "Transfer Pricing Guidelines for Multinational Enterprises and Tax Administrations". These guidelines emphasize the establishment of a range of arm¡¦s length transactions through the comparability analysis and the economic analysis of transfer pricing transactions; a taxpayer's returns from transactions with related companies are then compared to the range of arm¡¦s length transactions. Currently the academic world is taking the initiative in the development of relevant models to describe corporate transfer pricing decisions or to measure the net income of corporate transfer pricing transactions. This research stems from these purposes and attempts to describe transfer pricing decisions in real practice through stringent modelling; this model is then used to measure the net income of transfer pricing transactions that took place among electronic industry participants who are publicly listed in the TSE or OTC in Taiwan. We further investigated the main factors that affect the levels of net income transferred by enterprises. Based on the empirical results of this research, we discovered that the impact of raw material costs is highly significant to the corporate transfer pricing decisions, and the magnitude of impacts vary depending on the allocation of net income from transfer pricing. We recommend that the tax administration detect corporate transfer pricing decisions by monitoring the weight of raw material costs in a company.

Yes / This note highlights the value of using UQR for addressing the limitations inherent within previous methods involving conditional parameter distributions for spending analysis (QR and OLS). Using unique data and robust analysis using improved methods, our paper clearly demonstrates the over-importance attached to length of stay and the inadequate attention given to business travelers in previous research. There are clear benefits from UQR’s methodological robustness for assessing the multitude of variables related to tourist expenditures, particularly given UQR’s ability to inform across the spending distribution. Given tourism’s importance for the UK it is critical for expensive promotional activities to be targeted efficiently for ensuring effective policy making.

Yes / We employ unconditional quantile regression with region of origin fixed effects, whereby we find that attending live football matches significantly increases expenditures by inbound tourist in the UK, and surprisingly we find that such effects are strongest for those who overall spend the least. Higher spending individuals spend significantly more than those who do not attend football matches, even when such individuals are otherwise similar. We analyse the impact of football attendance across the tourism expenditure distribution which is a relatively neglected aspect within previous research.

Yes / The inbound tourist expenditure generating role of football (soccer), particularly the English Premier League 15 (EPL) is evaluated. An enhanced economic and management understanding of the role of regular sporting fixtures emerges, as well as quantification of their impact. Expenditure on football tickets is isolated to identify local economic spillovers outside the stadium walls. Using the UK International Passenger Survey, unconditional quantile regressions (UQR) is used to evaluate the distributional impact of football attendance on tourist expenditures. Both total expenditure and a new measure which adjusts expenditures for football ticket prices are considered. UQR is a novel technique which is as yet underexploited within sport economics and confers important methodological advantages over both OLS and quantile regressions. Significant cross quantile variation is found. High spending football fans spend more, even after ticket prices are excluded. Surprisingly, spending effects owing to attendance are strongest for those who overall spend the least, confirming the role of sport as a generator of tourist expenditure unlike most others. Though the attendance effect is smaller for higher aggregate spenders, there is nevertheless a significant impact across the distribution. Distributional expenditure impacts highlight clear differentials between attendance by high and low spenders. Similar analysis is applicable to other global brands such as the National Football League (NFL) in the United States (American football) and the Indian Premier (cricket) League. The EPL’s global popularity can be leveraged for achieving enhanced tourist expenditure.

This paper applies and extends Machado and Mata's parametric quantile decomposition method and a similar nonparametric technique to explore changes in welfare in Zimbabwe between 1995 and 2001. These methods allow us to construct a counterfactual distribution in order to decompose the shift into the part due to changes in endowments and that due to changes in returns. We examine two subsets of a nationally representative dataset and find that endowments had a positive effect but that returns account for more of the difference. In communal farming areas, the effect of returns was positive while, in urban Harare, it was negative. / Master of Science