Dynamic evolution of cross-correlations in the Chinese stock market

We study the dynamic evolution of cross-correlations in the Chinese stock market mainly based on the random matrix theory (RMT). The correlation matrices constructed from the return series of 367 A-share stocks traded on the Shanghai Stock Exchange from January 4, 1999 to December 30, 2011 are calculated over a moving window with a size of 400 days. The evolutions of the statistical properties of the correlation coefficients, eigenvalues, and eigenvectors of the correlation matrices are carefully analyzed. We find that the stock correlations are significantly increased in the periods of two market crashes in 2001 and 2008, during which only five eigenvalues significantly deviate from the random correlation matrix, and the systemic risk is higher in these volatile periods than calm periods. By investigating the significant contributors of the deviating eigenvectors in different moving windows, we observe a dynamic evolution behavior in business sectors such as IT, electronics, and real estate, which lead the rise (drop) before (after) the crashes

Bloch's cycle complex and coherent dualizing complexes in positive characteristic

Let $X$ be a separated scheme of dimension $d$ of finite type over a perfect field $k$ of positive characteristic $p$. In this work, we show that Bloch's cycle complex $\mathbb{Z}^c_X$ of zero cycles mod $p^n$ is quasi-isomorphic to the Cartier operator fixed part of a certain dualizing complex from coherent duality theory. From this we obtain new vanishing results for the higher Chow groups of zero cycles with mod $p^n$ coefficients for singular varieties.Comment: 62 pages. Accepted for publication in Tran AMS. Compared to v2: Due to an update of arXiv:1703.02269 concerning the normality condition (more specifically, Thm. 8.6), Cor. 8.12 in our article is now changed. (The statement in Cor. 0.2(6) remains valid.

Performance improvements of automobile communication protocols in electromagnetic interference environments

Electromagnetic Interference (EMI) is frequently encountered in automobile communication systems due to a large number of inductive nodes used in these systems. This thesis investigates the effects of EMI on two types of automobile communication systems, the Controller Area Network (CAN) and the FlexRay. It also proposes a modified Automatic Repeat reQuest (ARQ) scheme to improve the communication performances in EMI environments --Abstract, page iii

Hardware emulation of wireless communication fading channels

This dissertation investigates several main challenges to implementing hardware-based wireless fading channel emulators with emphasis on incorporating accurate correlation properties. Multiple-input multiple-output (MIMO) fading channels are usually triply-selective with three types of correlation: temporal correlation, inter-tap correlation, and spatial correlation. The proposed emulators implement the triply-selective fading Channel Impulse Response (CIR) by incorporating the three types of correlation into multiple uncorrelated frequency-flat Rayleigh fading waveforms while meeting real-time requirements for high data-rate, large-sized MIMO, and/or long CIR channels. Specifically, mixed parallel-serial computational structures are implemented for Kronecker products of the correlation matrices, which makes the best tradeoff between computational speed and hardware usage. Five practical fading channel examples are implemented for RF or underwater acoustic MIMO applications. The performance of the hardware emulators are verified with an Altera Field-Programmable Gate Array (FPGA) platform and the results match the software simulators in terms of statistical and correlation properties. The dissertation also contributes to the development of a 2-by-2 MIMO transceiver testbench that is used to measure real-world fading channels. Intensive channel measurements are performed for indoor fixed mobile-to-mobile channels and the estimated CIRs demonstrate the triply-selective correlation properties --Abstract, page iv

Robust Bayesian Variable Selection for Gene-Environment Interactions

Gene-environment (G×E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G×E studies have been commonly encountered, leading to the development of a broad spectrum of robust penalization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a robust Bayesian variable selection method for G×E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects. An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++