An autoregressive conditional filtering process to remove intraday seasonal volatility and its application to testing the noisy rational expectations model

We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.

Conjugated Polymer Nanoparticles for Biological Labeling and Delivery

Cancer remains one of the world’s most devastating diseases, with more than 10 million new cases every year. However, traditional treatments have proven insufficient for successful medical management of cancer due to the chemotherapeutics’ difficulty in achieving therapeutic concentrations at the target site, non-specific cytotoxicity to normal tissues, and limited systemic circulation lifetime. Although, a concerted effort has been placed in developing and successfully employing nanoparticle(NP)-based drug delivery vehicles successfully mitigate the physiochemical and pharmacological limitations of chemotherapeutics, work towards controlling the subcellular fate of the carrier, and ultimately its payload, has been limited. Because efficient therapeutic action requires drug delivery to specific organelles, the subcellular barrier remains critical obstacle to maximize the full potential of NP-based delivery vehicles. The aim of my dissertation work is to better understand how NP-delivery vehicles’ structural, chemical, and physical properties affect the internalization method and subcellular localization of the nanocarrier. In this work we explored how side-chain and backbone modifications affect the conjugated polymer nanoparticle (CPN) toxicity and subcellular localization. We discovered how subtle chemical modifications had profound consequences on the polymer’s accumulation inside the cell and cellular retention. We also examined how complexation of CPN with polysaccharides affects uptake efficiency and subcellular localization. This work also presents how changes to CPN backbone biodegradability can significantly affect the subcellular localization of the material. A series of triphenyl phosphonium-containing CPNs were synthesized and the effect of backbone modifications have on the cellular toxicity and intracellular fate of the material. A mitochondrial-specific polymer exhibiting time-dependent release is reported. Finally, we present a novel polymerization technique which allows for the controlled incorporation of electron-accepting benzothiadiazole units onto the polymer chain. This facilitates tuning CPN emission towards red emission. The work presented here, specifically, the effect that side-chain and structure, polysaccharide formulation and CPN degradability have on material’s uptake behavior, can help maximize the full potential of NP-based delivery vehicles for improved chemotherapeutic drug delivery.

An Autoregressive Conditional Filtering Process to remove Intraday Seasonal Volatility and its Application to Testing the Noisy Rational Expectations Model

We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.