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.

Surface Mean Flow and Turbulence Structure in Tropical Cyclone Winds

Hurricanes are one of the deadliest and costliest natural hazards affecting the Gulf coast and Atlantic coast areas of the United States. An effective way to minimize hurricane damage is to strengthen structures and buildings. The investigation of surface level hurricane wind behavior and the resultant wind loads on structures is aimed at providing structural engineers with information on hurricane wind characteristics required for the design of safe structures. Information on mean wind profiles, gust factors, turbulence intensity, integral scale, and turbulence spectra and co-spectra is essential for developing realistic models of wind pressure and wind loads on structures. The research performed for this study was motivated by the fact that considerably fewer data and validated models are available for tropical than for extratropical storms. Using the surface wind measurements collected by the Florida Coastal Monitoring Program (FCMP) during hurricane passages over coastal areas, this study presents comparisons of surface roughness length estimates obtained by using several estimation methods, and estimates of the mean wind and turbulence structure of hurricane winds over coastal areas under neutral stratification conditions. In addition, a program has been developed and tested to systematically analyze Wall of Wind (WoW) data, that will make it possible to perform analyses of baseline characteristics of flow obtained in the WoW. This program can be used in future research to compare WoW data with FCMP data, as gust and turbulence generator systems and other flow management devices will be used to create WoW flows that match as closely as possible real hurricane wind conditions. Hurricanes are defined as tropical cyclones for which the maximum 1-minute sustained surface wind speeds exceed 74 mph. FCMP data include data for tropical cyclones with lower sustained speeds. However, for the winds analyzed in this study the speeds were sufficiently high to assure that neutral stratification prevailed. This assures that the characteristics of those winds are similar to those prevailing in hurricanes. For this reason in this study the terms tropical cyclones and hurricanes are used interchangeably.

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.

The Spectral Distribution of Radiation in Two Neotropical Rainforests

The spectral quality of radiation in the understory of two neotropical rainforests, Barro Colorado Island in Panama and La Selva in Costa Rica, is profoundly affected by the density of the canopy. Understory light conditions in both forests bear similar spectral characteristics. In both the greatest changes in spectral quality occur at low flux densities, as in the transition from extreme shade to small light flecks. Change in spectral quality, as assessed by the red: far-red (R:FR) ratio, the ratio of radiant energy 400-700: 300-1100 nm, and the ratio of quantum flux density 400-700:300-1100 nm, is strongly correlated with a drop in percentage of solar radiation as measurable by a quantum radiometer. Thus, by knowing the percentage of photosynthetic photon flux density (PPFD) in relation to full sunlight, it is possible to estimate the spectral quality in the forest at a particular time and microsite.

Comparison of snail density, standing stock, and body size between Caribbean karst wetlands and other freshwater ecosystems

Synthesizing data from multiple studies generates hypotheses about factors that affect the distribution and abundance of species among ecosystems. Snails are dominant herbivores in many freshwater ecosystems, but there is no comprehensive review of snail density, standing stock, or body size among freshwater ecosystems. We compile data on snail density and standing stock, estimate body size with their quotient, and discuss the major pattern that emerges. We report data from 215 freshwater ecosystems taken from 88 studies that we placed into nine categories. Sixty-five studies reported density, seven reported standing stock, and 16 reported both. Despite the breadth of studies, spatial and temporal sampling scales were limited. Researchers used 25 different sampling devices ranging in area from 0.0015 to 2.5 m2. Most ecosystem categories had similar snail densities, standing stocks, and body sizes suggesting snails shared a similar function among ecosystems. Caribbean karst wetlands were a striking exception with much lower density and standing stock, but large body size. Disparity in body size results from the presence of ampullariids in Caribbean karst wetlands suggesting that biogeography affects the distribution of taxa, and in this case size, among aquatic ecosystems. We propose that resource quality explains the disparity in density and standing stock between Caribbean karst wetlands and other categories. Periphyton in Caribbean karst wetlands has high carbon-to-phosphorous ratios and defensive characteristics that inhibit grazers. Unlike many freshwater ecosystems where snails are key grazers, we hypothesize that a microbial loop captures much of the primary production in Caribbean karst wetlands.

The influence of neighbourhood formality status and socio-economic position on self-rated health among adult men and women: a multilevel, cross sectional, population study from Aleppo, Syria

Background There is substantial evidence from high income countries that neighbourhoods have an influence on health independent of individual characteristics. However, neighbourhood characteristics are rarely taken into account in the analysis of urban health studies from developing countries. Informal urban neighbourhoods are home to about half of the population in Aleppo, the second largest city in Syria (population>2.5 million). This study aimed to examine the influence of neighbourhood socioeconomic status (SES) and formality status on self-rated health (SRH) of adult men and women residing in formal and informal urban neighbourhoods in Aleppo. Methods The study used data from 2038 survey respondents to the Aleppo Household Survey, 2004 (age 18–65 years, 54.8% women, response rate 86%). Respondents were nested in 45 neighbourhoods. Five individual-level SES measures, namely education, employment, car ownership, item ownership and household density, were aggregated to the level of neighbourhood. Multilevel regression models were used to investigate associations. Results We did not find evidence of important SRH variation between neighbourhoods. Neighbourhood average of household item ownership was associated with a greater likelihood of reporting excellent SRH in women; odds ratio (OR) for an increase of one item on average was 2.3 (95% CI 1.3-4.4 (versus poor SRH)) and 1.7 (95% CI 1.1-2.5 (versus normal SRH)), adjusted for individual characteristics and neighbourhood formality. After controlling for individual and neighbourhood SES measures, women living in informal neighbourhoods were less likely to report poor SRH than women living in formal neighbourhoods (OR= 0.4; 95% CI (0.2- 0.8) (versus poor SRH) and OR=0.5; 95%; CI (0.3-0.9) (versus normal SRH). Conclusions Findings support evidence from high income countries that certain characteristic of neighbourhoods affect men and women in different ways. Further research from similar urban settings in developing countries is needed to understand the mechanisms by which informal neighbourhoods influence women’s health.