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Dissertation on generalized empirical likelihood estimators

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Schennach (2007) has shown that the Empirical Likelihood (EL) estimator may not be asymptotically normal when a misspecified model is estimated. This problem occurs because the empirical probabilities of individual observations are restricted to be positive. I find that even the EL estimator computed without the restriction can fail to be asymptotically normal for misspecified models if the sample moments weighted by unrestricted empirical probabilities do not have finite population moments. As a remedy for this problem, I propose a group of alternative estimators which I refer to as modified EL (MEL) estimators. For correctly specified models, these estimators have the same higher order asymptotic properties as the EL estimator. The MEL estimators are obtained by the Generalized Method of Moments (GMM) applied to an exactly identified model. The simulation results provide promising evidence for these estimators. In the second chapter, I introduce an alternative group of estimators to the Generalized Empirical Likelihood (GEL) family. The new group is constructed by employing demeaned moment functions in the objective function while using the original moment functions in the constraints. This designation modifies the higher-order properties of estimators. I refer to these new estimators as Demeaned Generalized Empirical Likelihood (DGEL) estimators. Although Newey and Smith (2004) show that the EL estimator in the GEL family has fewer sources of bias and is higher-order efficient after bias-correction, the demeaned exponential tilting (DET) estimator in the DGEL group has those superior properties. In addition, if data are symmetrically distributed, every estimator in the DGEL family shares the same higher-order properties as the best member.

ContributorsXiang, Jin (Author) / Ahn, Seung (Thesis advisor) / Wahal, Sunil (Thesis advisor) / Bharath, Sreedhar (Committee member) / Mehra, Rajnish (Committee member) / Tserlukevich, Yuri (Committee member) / Arizona State University (Publisher)

Created2013

High-Frequency Quoting, Trading, and the Efficiency of Prices

Description

We examine the relation between high frequency quotation and the behavior of stock prices between 2009 and 2011 for the full cross section of securities in the US. On average, higher quotation activity is associated with price series that more closely resemble a random walk, and significantly lower cost of trading. We also explore market resiliency during periods of exceptionally high low-latency trading: large liquidity drawdowns in which, within the same millisecond, trading algorithms systematically sweep large volume across multiple trading venues. Although such large drawdowns incur trading costs, they do not appear to degrade the price formation process or increase the subsequent cost of trading. In an out-of-sample analysis, we investigate an exogenous technological change to the trading environment on the Tokyo Stock Exchange that dramatically reduces latency and allows co-location of servers. This shock also results in prices more closely resembling a random walk and a sharp decline in the cost of trading.

ContributorsConrad, Jennifer (Author) / Wahal, Sunil (Author) / Xiang, Jin (Author) / W.P. Carey School of Business (Contributor)

Created2015-05-01