Many Uninsured Children Qualify for Medi-Cal or Healthy Families

Examines the public health insurance eligibility of children in California who did not have health insurance coverage for some or all of the year in 2002, to highlight the geographic variations in children's uninsured eligibility rates

Testing Co-Volatility Spillovers for Natural Gas Spot, Futures and ETF Spot using Dynamic Conditional Covariances

There is substantial empirical evidence that energy and financial markets are closely connected. As one of the most widely-used energy resources worldwide, natural gas has a large daily trading volume. In order to hedge the risk of natural gas spot markets, a large number of hedging strategies can be used, especially with the rapid development of natural gas derivatives markets. These hedging instruments include natural gas futures and options, as well as Exchange Traded Fund (ETF) prices that are related to natural gas stock prices. The volatility spillover effect is the delayed effect of a returns shock in one physical, biological or financial asset on the subsequent volatility or co-volatility of another physical, biological or financial asset. Investigating volatility spillovers within and across energy and financial markets is a crucial aspect of constructing optimal dynamic hedging strategies. The paper tests and calculates spillover effects among natural gas spot, futures and ETF markets using the multivariate conditional volatility diagonal BEKK model. The data used include natural gas spot and futures returns data from two major international natural gas derivatives markets, namely NYMEX (USA) and ICE (UK), as well as ETF data of natural gas companies from the stock markets in the USA and UK. The empirical results show that there are significant spillover effects in natural gas spot, futures and ETF markets for both USA and UK. Such a result suggests that both natural gas futures and ETF products within and beyond the country might be considered when constructing optimal dynamic hedging strategies for natural gas spot prices

Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice

Energy and agricultural commodities and markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of agricultural commodities and markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and agricultural markets. Modelling and testing spillovers between the energy and agricultural markets has typically been based on estimating multivariate conditional volatility models, specifically the Baba, Engle, Kraft, and Kroner (BEKK) and dynamic conditional correlation (DCC) models. A serious technical deficiency is that the Quasi-Maximum Likelihood Estimates (QMLE) of a Full BEKK matrix, which is typically estimated in examining volatility spillover effects, has no asymptotic properties, except by assumption, so that no valid statistical test of volatility spillovers is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and agricultural markets using the multivariate Full BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria

Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice

Energy and agricultural commodities and markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of agricultural commodities and markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and agricultural markets. Modelling and testing spillovers between the energy and agricultural markets has typically been based on estimating multivariate conditional volatility models, specifically the Baba, Engle, Kraft, and Kroner (BEKK) and dynamic conditional correlation (DCC) models. A serious technical deficiency is that the Quasi-Maximum Likelihood Estimates (QMLE) of a Full BEKK matrix, which is typically estimated in examining volatility spillover effects, has no asymptotic properties, except by assumption, so that no valid statistical test of volatility spillovers is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and agricultural markets using the multivariate Full BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria

A personal identification biometric system based on back-of-hand vein patterns

This report describes research on the use of back-of-hand vein patterns as a means of uniquely identifying people. In particular it describes a prototype biometric system developed by the Australian Institute of Security and Applied Technology (AISAT). This system comprises an infrared cold source, a monochrome CCD camera, a monochrome frame-grabber, a personal computer, and custom image acquisition, processing, registration, and matching software. The image processing algorithms are based on Mathematical Morphology. Registration is performed using rotation and translation with respect to the centroid of the two-dimensional domain of a hand. Vein patterns are stored as medial axis representations. Matching involves comparing a given medial axis pattern against a library of patterns using constrained sequential correlation. The matching is two-fold: a newly acquired signature is matched against a dilated library signature, and then the library signature is matched against the dilated acquired signature; this is necessary because of the positional noise exhibited by the back-of-hand veins. The results of a cross-matching experiment for a sample of 20 adults and more than 100 hand images is detailed. In addition preliminary estimates of the false acceptance rate (FAR) and false rejection rate (FRR) for the prototype system are given. Fuzzy relaxation on an association graph is discussed as an alternative to sequential correlation for the matching of vein signatures. An example is provided (including a C program) illustrating the matching process for a pair of signatures obtained from the same hand. The example demonstrates the ability of the fuzzy relaxation method to deal with segmentation errors

A Statistical Analysis of Industrial Penetration and Internet Intensity in Taiwan

This paper investigates the effect of industrial penetration (geographic concentration of industries) and internet intensity (the proportion of enterprises that use the internet) for Taiwan manufacturing firms, and analyses whether the relationships are substitutes or complements. The sample observations are based on 153,081 manufacturing plants, and covers 26 two-digit industry categories and 358 geographical townships in Taiwan. The Heckman selection model is used to accommodate sample selectivity for unobservable data for firms that use the internet. The empirical results from two-stage estimation show that: (1) a higher degree of industrial penetration will not affect the probability that firms will use the internet, but will affect the total expenditure on internet intensity; (2) for two-digit SIC industries, industrial penetration generally decreases the total expenditure on internet intensity; and (3) industrial penetration and internet intensity are substitutes

Industrial Penetration and Internet Intensity

This paper investigates the effect of industrial penetration and internet intensity for Taiwan manufacturing firms, and analyses whether the relationships are substitutes or complements. The sample observations are based on 153,081 manufacturing plants, and covers 26 two-digit industry categories and 358 geographical townships in Taiwan. The Heckman selection model is used to accommodate sample selectivity for unobservable data for firms that use the internet. The empirical results from two-stage estimation show that: (1) a higher degree of industrial penetration will not affect the probability that firms will use the internet, but will affect the total expenditure on internet intensity; (2) for two-digit industries, industrial penetration generally decreases the total expenditure on internet intensity; and (3) industrial penetration and internet intensity are substitutes

Modelling Volatility Spillovers for Bio-ethanol, Sugarcane and Corn Spot and Futures Prices

The recent and rapidly growing interest in biofuel as a green energy source has raised concerns about its impact on the prices, returns and volatility of related agricultural commodities. Analyzing the spillover effects on agricultural commodities and biofuel helps commodity suppliers hedge their portfolios, and manage the risk and co-risk of their biofuel and agricultural commodities. There have been many papers concerned with analyzing crude oil and agricultural commodities separately. The purpose of this paper is to examine the volatility spillovers for spot and futures returns on bio-ethanol and related agricultural commodities, specifically corn and sugarcane. The diagonal BEKK model is used as it is the only multivariate conditional volatility model with well-established regularity conditions and known asymptotic properties. The daily data used are from 31 October 2005 to 14 January 2015. The empirical results show that, in 2 of 6 cases for the spot market, there were significant negative co-volatility spillover effects: specifically, corn on subsequent sugarcane co-volatility with corn, and sugarcane on subsequent corn co-volatility with sugarcane. In the other 4 cases, there are no significant co-volatility spillover effects. There are significant positive co-volatility spillover effects in all 6 cases, namely between corn and sugarcane, corn and ethanol, and sugarcane and ethanol, and vice-versa, for each of the three pairs of commodities. It is clear that the futures prices of bio-ethanol and the two agricultural commodities, corn and sugarcane, have stronger co-volatility spillovers than their spot price counterparts. These empirical results suggest that the bio-ethanol and agricultural commodities should be considered as viable futures products in financial portfolios for risk managemen

Symbiotic Bright Solitary Wave Solutions of Coupled Nonlinear Schrodinger Equations

Conventionally, bright solitary wave solutions can be obtained in self-focusing nonlinear Schrodinger equations with attractive self-interaction. However, when self-interaction becomes repulsive, it seems impossible to have bright solitary wave solution. Here we show that there exists symbiotic bright solitary wave solution of coupled nonlinear Schrodinger equations with repulsive self-interaction but strongly attractive interspecies interaction. For such coupled nonlinear Schrodinger equations in two and three dimensional domains, we prove the existence of least energy solutions and study the location and configuration of symbiotic bright solitons. We use Nehari's manifold to construct least energy solutions and derive their asymptotic behaviors by some techniques of singular perturbation problems.Comment: to appear in Nonlinearit

Improving Sparse Representation-Based Classification Using Local Principal Component Analysis

Sparse representation-based classification (SRC), proposed by Wright et al., seeks the sparsest decomposition of a test sample over the dictionary of training samples, with classification to the most-contributing class. Because it assumes test samples can be written as linear combinations of their same-class training samples, the success of SRC depends on the size and representativeness of the training set. Our proposed classification algorithm enlarges the training set by using local principal component analysis to approximate the basis vectors of the tangent hyperplane of the class manifold at each training sample. The dictionary in SRC is replaced by a local dictionary that adapts to the test sample and includes training samples and their corresponding tangent basis vectors. We use a synthetic data set and three face databases to demonstrate that this method can achieve higher classification accuracy than SRC in cases of sparse sampling, nonlinear class manifolds, and stringent dimension reduction.Comment: Published in "Computational Intelligence for Pattern Recognition," editors Shyi-Ming Chen and Witold Pedrycz. The original publication is available at http://www.springerlink.co