Impact study of volatility modelling of Bangladesh stock index using non-normal density

This article examines a wide variety of popular volatility models for stock index return, including the random walk (RW), autoregressive, generalized autoregressive conditional heteroscedasticity (GARCH), and asymmetric GARCH models with normal and non-normal (Student's t and generalized error) distributional assumption. Fitting these models to the Chittagong stock index return data from the period 2 January 1999 to 29 December 2005, we found that the asymmetric GARCH/GARCH model fits better under the assumption of non-normal distribution than under normal distribution. Non-parametric specification tests show that the RW-GARCH, RW-TGARCH, RW-EGARCH, and RW-APARCH models under the Student's t-distributional assumption are significant at the 5% level. Finally, the study suggests that these four models are suitable for the Chittagong Stock Exchange of Bangladesh. We believe that this study would be of great benefit to investors and policy makers at home and abroad.     

Social Work Practice for Social Development in Bangladesh: Issues and Challenges

Social work addresses social issues that constrain the betterment of a community. However, social work practice is struggling to deal effectively with the development challenges of Bangladesh. Based on a literature review and the experiences of the authors, this paper explores the emerging issues where social work practice is anticipated as a promising alternative for bringing sustainable social development in Bangladesh. Simultaneously, the paper discovers the limitation of social work practice in Bangladesh addressing these challenges.     

Simultaneous estimation of several CDF’s: homogeneity constraint

Let {x_ij(1 ? j ? n_i)|i = 1, 2, …, k} be k independent samples of size n_j from respective distributions of functions F_j(x)(1 ? j ? k). A classical statistical problem is to test whether these k samples came from a common distribution function, F(x) whose form may or may not be known. In this paper, we consider the complementary problem of estimating the distribution functions suspected to be homogeneous in order to improve the basic estimator known as “empirical distribution function” (edf), in an asymptotic setup. Accordingly, we consider four additional estimators, namely, the restricted estimator (RE), the preliminary test estimator (PTE), the shrinkage estimator (SE), and the positive rule shrinkage estimator (PRSE) and study their characteristic properties based on the mean squared error (MSE) and relative risk efficiency (RRE) with tables and graphs. We observed that for k ? 4, the positive rule SE performs uniformly better than both shrinkage and the unrestricted estimator, while PTEs works reasonably well for k < 4.     

Nonmetropolitan Residential Segregation Revisited

Abstract Nonmetropolitan (nonmetro) residential segregation in 1990 and change in the preceding decade have received insufficient attention. A set of empirical hypotheses are derived and assessed using nonmetro and metropolitan (metro) counties in Texas. Places in nonmetro counties were more segregated than places in metro counties in 1990 as in 1980. Substantial declines in segregation occurred in both nonmetro and metro places but were largest in growing places in nonmetro counties. An analysis controlling for other determinants of segregation supports the premise that population change was a major determinant of 1980–1990 change in segregation. Implications for nonmetro areas in the 1990s are discussed.     

On the structural estimation of certain correlation coefficients

In this paper, the estimation of a real-valued function of the parameter by minimizing the expected value of the quadratic loss function relative to the structural distribution of the parameter is proposed; this is called structural estimation. The general formulae developed have been used to obtain the structural estimate of the bivariate correlation coefficient and of the intraclass correlation coefficient.     

Correlates of Susceptibility to Scams in Older Adults Without Dementia

This study examined correlates of susceptibility to scams in 639 community-dwelling older adults without dementia from a cohort study of aging. Regression models adjusted for age, sex, education, and income were used to examine associations between susceptibility to scams, measured by a five-item self-report measure, and a number of potential correlates. Susceptibility was positively associated with age and negatively associated with income, cognition, psychological well-being, social support, and literacy. Fully adjusted models indicated that older age and lower levels of cognitive function, decreased psychological well-being, and lower literacy in particular may be markers of susceptibility to financial victimization in old age.     

Crack ‘Hos and Skeezers: Traumatic experiences of women crack users

The involvement of women in crack cocaine abuse has had a severe impact on their health, the health of their children and the stability of their communities. Of particular concern has been the development of a system of barter in which crack‐for‐sex exchanges are the means through which women obtain the drug. Earlier studies have suggested that drug abuse may be related to and exacerbated by trauma. In the project described herein, we interviewed women crack users in Harlem to study the relationship between trauma, crack use, and crack‐related sexual behavior. Results suggested the existence of three types of trauma: (1) traumas that predate the respondent's onset of crack use; (2) traumas that were the direct sequelae of crack use; and (3) stigma trauma, that is, trauma that results from membership in a despised or oppressed group. We observed a complex inter‐relationship involving crack use, crack‐for‐sex transactions, and these three types of trauma. Treatment of the eventual co‐morbidity of trauma and addiction is an urgent challenge.     

The effect of undercount on the accuracy of small-area population estimates: Implications for the use of administrative data for improving population enumeration

The effects of underenumeration on the accuracy of alternative methods of population estimation have not been sufficiently analyzed. Although the US Bureau of the Census has decided not to adjust either the counts or its estimates for underenumeration in 1990, the extent to which local population estimates may account for underenumeration is of importance both for those who may wish to adjust existing estimates and in anticipation of future census adjustments. This paper examines the accuracy of small-area population estimation methods with and without adjustment. Mean Percent Errors, Mean Absolute Percent Errors, and Mean Percent Absolute Differences between local estimates for 1990 and 1990 adjusted and unadjusted census counts are computed. Population estimates for 1990 made using housing unit, ratio correlation, and component methods are compared for 451 counties and 2,633 places in the states of California, Florida, Texas, and Wisconsin. An analysis of the data for counties shows little indication that local estimates more accurately estimate the adjusted than the unadjusted population counts. The results for places show clear improvements in accuracy for places in Florida and Texas. Implications of the findings for issues related to undercount adjustment and local population estimates are discussed.     

Pitman-closeness of some preliminary test and shrinkage estimators

For the model X ～ N_p: (θ,I)preliminary test estimator (PTE), shrinkage and positive-rule versions of the MLE (X) of θare mutually compared in the light of the Pitman closeness measure. The usual dominance properties of these estimators pertaining to the conventional quadratic loss criterion are shown to remain intact in the current context too. In an asymptotic setup, the conclusions hold for a much wider class of estimators pertaining to general parametric and nonparametric models.