Browsing by Author "Li, Jia"

This dissertation consists of three essays. The first essay, "Volume, volatility and Macroeconomic Announcements" studies the relationship between trading intensity and price volatility and how it is affected by investors' disagreement on a common public signal around macroeconomic announcements. Inspired by a difference-of-opinion model in which investors agree to disagree, we use high frequency data and empirically show that the volume-volatility elasticity of SP 500 ETF is uniformly below 1. Besides, the elasticity decreases with disagreement measures such as the forecast dispersion on unemployment rate and uncertainty measures, as well as a textual based tone measure constructed using FOMC statements. This paper provides new evidences on how information is processed in financial market.

The second essay, "Investor Sentiment and Volume Volatility Relationship" shows that investor sentiment plays a role on information processing in financial markets. We incorporate a one-factor asset pricing model into the difference-of-opinion model to derive the volume-volatility relationship for individual stocks. We separate the sample into high and low sentiment periods and use high frequency data to show that investors' disagreement measures only significantly reduce volume-volatility elasticity around macroeconomic announcements during high-sentiment periods, for both the S&P 500 ETF and Dow Jones

30 components. This result is consistent with

changes in the confidence level of investors when sentiment regime shifts. Our estimates of elasticity also decrease significantly with the

ratio of idiosyncratic variance, which indicates that higher idiosyncratic risks introduce larger dispersion among investors.

In the third essay, "Efficient Estimation of Integrated Functional of Variance with Irregular

Observation Time", we propose an efficient estimator of the integrated functional of the variance with irregular observation time of prices. We propose the consistency and central limit theorems, and then validate the theorems through proofs and simulations.

The dissertation consists of two essays that apply nonparametric econometric tools to studying the financial market. The first essay, “Sentiment and Volume-Volatility Elasticity” is presented in Chapter 2 and explores the relationship between sentiment and market participants' trading activities around public news announcements. We develop a theoretical model under the short-sale constraint and the model predicts a nonlinear relationship between sentiment and the volume-volatility elasticity. We estimate parametric regression models and carry out nonparametric series estimation around important macroeconomic news announcements with high-frequency intraday trading volume and transaction price data of the S&P 500 E-mini futures contract. Empirical results not only corroborate predictions of the theoretical model, but also suggest varying effects of sentiment on the volume-volatility elasticity around announcements of different importance.

Chapter 3 presents the second essay, “Conditional Superior Performing Assets”. We utilize a novel functional test for conditional moment inequalities to select conditional superior performing assets (CSPA). We apply the CSPA test to evaluate the performance of U.S. mutual funds. The test is carried out with gross as well as risk-adjusted returns of domestic equity mutual funds bearing various investment objectives. By inverting the CSPA test for sets of benchmark assets, we obtain confidence sets for the uniformly most superior asset. Empirical results indicate superior performance of funds with various investment objectives over certain conditioning states of measures from various aspects of the economy. Additionally, the CSPA confidence sets can serve as criteria for fund selection. The usefulness of the CSPA test on fund evaluation is demonstrated through identifying significant conditional superior performance from assets that are indistinguishable unconditionally.

Invariant NKT (iNKT) cells represent a unique group of αβ T cells that have been classified based on their exclusive usage of the invariant Vα14Jα 18 TCRα –chain and their innate–like effector function. Thus far, the transcriptional programs that control Vα14Jα18 TCRα rearrangements and the population size of iNKT cells remain incompletely defined.

E protein transcription factors have been shown to play multiple roles in T cell development including lineage commitment, receptor gene recombination, proliferation and lineage choice. Inhibitor of DNA–binding (Id) proteins are the natural inhibitors of E protein transcription factors. The goal of this dissertation is to examine E protein functions in the development of iNKT cells in the mouse after combined deletion of genes encoding E protein inhibitors Id2 and Id3.

We revealed important roles of Id proteins and E proteins in regulating iNKT cell development. Deletion of Id2 and Id3 in T cell progenitors resulted in a partial block at the pre–TCR selection checkpoint and a dramatic increase in numbers of iNKT cells. This increase in iNKT cells is accompanied with a biased rearrangement involving Vα14 to Jα18 recombination at the double–positive stage and enhanced proliferation of iNKT cells. We further demonstrate that a 50 percent reduction of E proteins can cause a dramatic lineage shift from iNKT cells to innate–like gd T cells in Id2 and Id3 double–deficient mice. Collectively, these findings suggest that Id2– and Id3–mediated inhibition of E proteins controls iNKT development by restricting lineage choice and population expansion.

Our study also uncovered a novel role of Id proteins in development of NKT lymphoma. Id deficient NKT cells gradually progresses into NKT lymphoma, a rare form of tumor with no clearly defined etiology. Id and E proteins have been demonstrated to be involved in multiple lymphoma and cancer subtypes, but their role in the development of NKT lymphomas is unexplored. Adoptive transfer experiments confirmed that the malignant cells are able to invade healthy tissues. cDNA Microarray analysis of NKT lymphoma and pre–malignant NKT cells revealed alterations in several cytokine signaling pathways during tumor progression. These findings indicate that regulation of E proteins by Id2 and Id3 may play important roles in the development of NKT lymphoma. To our knowledge, this study represents the first mouse model in which NKT lymphoma develops at such high frequency and fast kinetics. Our double knockout mice provide a unique model to study mechanisms of human NKT lymphoma progression.

This dissertation contains four chapters. Chapter 1 gives an overall view of stochastic volatility and high-frequency financial econometrics. Chapter 2 proposes a new notion of realized autocovariance for stochastic volatility, which is an analogue of the standard autocovariance in a non-stationary non-ergodic continuous-time set- ting. I propose a plug-in type estimator for the realized autocovariance and show the central-limit theorem of it. Empirical applications are then provided.

Chapter 3 concerns the “localized” estimation of volatility, namely spot volatility estimation, under two noisy settings. The first setting concerns an additive-noise multivariate price process, with regular sampling and conditional independence in the noise; the second setting further allows for random sampling and long-run dependence in the noise, but it requires a one-dimensional underlying price process. I propose different pre-averaging estimators of the spot volatility of asset price and that of the microstructure noise, and then provide the uniform rates of convergence for these estimators.

Chapter 4 studies a semiparametric inference procedure for a finite-dimensional parameter in a continuous-time regression model in a large cross-section. The model studies the relationship between a dependent process and a possibly nonlinear trans- form of volatility over a fixed time span, and the coefficient is allowed to depend on a set of firm-specific characteristics. The construction of the estimator involves two steps: the nonparametric estimation of volatility processes, followed by a parametric second stage that uses the volatility estimates. I show that the estimator follows a central limit theorem and provide a feasible inference procedure based on a factor- analytic method. Lastly, I show in a realistically calibrated Monte Carlo setting that the performance of the inference procedure is reasonably good.

Finally, Chapter 5 concludes.