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.

Statistical modeling of protein lysate array data

The protein lysate array is an emerging technology for quantifying the protein concentration ratios in multiple biological samples. It is gaining popularity, and has the potential to answer questions about post-translational modifications and protein pathway relationships. Statistical inference for a parametric quantification procedure has been inadequately addressed in the literature, mainly due to two challenges: the increasing dimension of the parameter space and the need to account for dependence in the data. Each chapter of this thesis addresses one of these issues. In Chapter 1, an introduction to the protein lysate array quantification is presented, followed by the motivations and goals for this thesis work. In Chapter 2, we develop a multi-step procedure for the Sigmoidal models, ensuring consistent estimation of the concentration level with full asymptotic efficiency. The results obtained in this chapter justify inferential procedures based on large-sample approximations. Simulation studies and real data analysis are used to illustrate the performance of the proposed method in finite-samples. The multi-step procedure is simpler in both theory and computation than the single-step least squares method that has been used in current practice. In Chapter 3, we introduce a new model to account for the dependence structure of the errors by a nonlinear mixed effects model. We consider a method to approximate the maximum likelihood estimator of all the parameters. Using the simulation studies on various error structures, we show that for data with non-i.i.d. errors the proposed method leads to more accurate estimates and better confidence intervals than the existing single-step least squares method.