Sleep Disturbance in the Homeless Population: The Relationship between Homelessness, Sleep and Health

Little is known about how sleep disruption impacts physical health among the homeless. The association between homelessness, quality of sleep and physical health were investigated in the current study. Convenience sampling was used to select participants from a pool of people attending the programs of Ecclesia Ministries. Interviews were conducted with 32 persons from the Boston metropolitan area, of whom 23 were currently homeless. The researcher assessed level of sleep disturbance, number of health problems and degree of homelessness using a standard demographic questionnaire, the General Health Questionnaire-12 (GHQ-12) and the Pittsburgh Sleep Quality Index (PSQI). Our results found evidence of significant sleep disturbance as well as significant mental and physical health problems in the sample. Correlational analyses provided partial support for the hypothesis that degree of homelessness impacts both sleep quality and physical health. Future work should investigate whether change in homelessness status alters sleep quality and physical health and also whether interventions may be utilized in this understudied and vulnerable population.

Empirical Analysis of Credit Risk Regime Switching and Temporal Conditional Default Correlation in Credit Default Swap Valuation: The Market liquidity effect

In this paper, we extend the debate concerning Credit Default Swap valuation to include time varying correlation and co-variances. Traditional multi-variate techniques treat the correlations between covariates as constant over time; however, this view is not supported by the data. Secondly, since financial data does not follow a normal distribution because of its heavy tails, modeling the data using a Generalized Linear model (GLM) incorporating copulas emerge as a more robust technique over traditional approaches. This paper also includes an empirical analysis of the regime switching dynamics of credit risk in the presence of liquidity by following the general practice of assuming that credit and market risk follow a Markov process. The study was based on Credit Default Swap data obtained from Bloomberg that spanned the period January 1st 2004 to August 08th 2006. The empirical examination of the regime switching tendencies provided quantitative support to the anecdotal view that liquidity decreases as credit quality deteriorates. The analysis also examined the joint probability distribution of the credit risk determinants across credit quality through the use of a copula function which disaggregates the behavior embedded in the marginal gamma distributions, so as to isolate the level of dependence which is captured in the copula function. The results suggest that the time varying joint correlation matrix performed far superior as compared to the constant correlation matrix; the centerpiece of linear regression models.