DETERMINATION OF VOLATILITY AND MEAN RETURNS: AN EVIDENCE FROM AN EMERGING STOCK MARKET

In the present research we work with excess returns for an emerging stock market i.e. Jamaican Stock Price Index for the determination of volatility persistence and persistence in the mean returns series. We model excess returns in this stock market using state space or unobserved component models, which is a signal extraction approach. Our model encompass stable distributions to account for fat tails and GARCH-like effects to account for time varying volatility that may be present in the series. The study results that are obtained using the most general as well as the restricted versions of the state space models reveal statistically significant evidence of volatility persistence in the excess returns series. Further, there exist persistent predictable signals in returns series at 5 percent level of significance, and the value of an efficiently estimated excess returns series is percent per month (percent per annum). Further, the series encompass a stable characteristic exponent of showing a non-normal behavior in this market.stock return predictability, unobserved components, fat tails, stable distributions

Predictability in Stock Returns in an Emerging Market: Evidence from KSE 100 Stock Price Index

We investigate the persistence in monthly KSE100 excess stock returns over the Treasury bills rates using non-Gaussian state space or unobservable component model with stable distributions and volatility persistence. Results from our non-Gaussian state space model, which is an improvement over Conard and Kaul (1988), show that the conditional distribution has a stable of 1.748 and normality is rejected even after accounting for GARCH. There exists a statistically significant predictable component in the KSE 100 excess stock returns. The optimal predictor in the unconditional expectation of the series is estimated to be 0.18 percent per annum. An evidence of highly nonconstant scales in different periods of time exhibits a tendency towards stock market crashes which invites remedial policy action.Stock Return Predictability, Unobserved Components, Fat Tails, Stable Distributions

BUSINESS CYCLE ASYMMETRIES IN STOCK RETURNS: ROBUST EVIDENCE

In this study we employ augmented and switching time series models to find possible existence of business cycle asymmetries in U.S. stock returns. Our approach is fully parametric and testing strategy is robust to any conditional heteroskedasticity, and outliers that may be present. We also approximate in sample as well as out-of-sample forecasts from artificial neural networks for testing business cycle nonlinearities in U.S. stock returns. Our results based on nonlinear augmented and switching time series models show a strong evidence of business cycle asymmetries in conditional mean dynamics of U.S. stock returns. These results also show that conditional heteroskedasticity is unimportant when testing for asymmetries in conditional mean. Moreover, the conditional volatility in stock returns is asymmetric and is more pronounced in recessions than in expansion phase of business cycles. Similarly, the results based on neural network models show a statistically significant evidence of business cycle nonlinearities in US stock returns. The magnitude of these nonlinearities is more obvious in post World War II era than in the full sample period.asymmetries; business cycles; conditional heteroskedasticity; long memory; nonlinearities; outliers; excess returns; stable distributions

On Business Cycle Asymmetries in G7 Countries

We investigate whether business cycle dynamics in seven industrialized countries (the G7) are characterized by asymmetries in conditional mean. We provide evidence on this issue using a variety of time series models. Our approach is fully parametric. Our testing strategy is robust to any conditional heteroskedasticity, outliers, and / or long memory that may be present. Our results indicate fairly strong evidence of nonlinearities in the conditional mean dynamics of the GDP growth rates for Canada, Germany, Italy, Japan, and the US. For France and the UK, the conditional mean dynamics appear to be largely linear. Our study shows that while the existence of conditional heteroskedasticity and long memory does not have much affect on testing for linearity in the conditional mean, accounting for outliers does reduce the evidence against linearity.business cycles, asymmetries, nonlinearities, conditional heteroskedasticity, long memory, outliers, real GDP, stable distributions

Predictability in Stock Returns in an Emerging Market: Evidence from KSE 100 Stock Price Index

We investigate the persistence in monthly KSE100 excess stock returns over the Treasury bills rates using non-Gaussian state space or unobservable component model with stable distributions and volatility persistence. Results from our non-Gaussian state space model, which is an improvement over Conard and Kaul (1988), show that the conditional distribution has a stable of 1.748 and normality is rejected even after accounting for GARCH. There exists a statistically significant predictable component in the KSE 100 excess stock returns. The optimal predictor in the unconditional expectation of the series is estimated to be 0.18 percent per annum. An evidence of highly nonconstant scales in different periods of time exhibits a tendency towards stock market crashes which invites remedial policy action

Classification and immunophenotyping of acute Leukemias: a prospective study.

Over a period of 3-1/2 years, 86 cases of acute leukemia were analyzed by immunohistochemical (IHC) means on ficoll separated cytospin preparations of peripheral blood and/or bone marrow samples. Antibodies included in the panel were specific against Tdt, HLA-DR, CD19/CD2O/CD22, CALLA (CD1O), CD2, CD11C as well as against Ig heavy chains. Of 86 cases analyzed, 48 cases were of ALL, (25 of common pre-B ALL, 15 of pre-B/NULL and 8 of T ALL phenotype), twenty-four (24) out of 86 cases were of non­lymphoblastic (AML/AMML) type. In six cases, there was suggestion of a mixed lineage, while in 8 cases there was inconclusive diagnosis. Mean age was lower in common ALL sub-set of ALL as compared to pre-B/NuIl gmup (i.e., 8 vs 12 years), while in non-lymphoblastic group it was 36 years. T cell phenotype was invariably seen in young adults, who usually presented with a mediastinal mass

Dividend Paying Practices in the Non-Financial Sector of Pakistan: An Empirical Evidence from Karachi Stock Exchange

The study intends to investigate the dividend paying practices in the non-financial sectors of the Karachi Stock Exchange. All the dividend paying sectors of the Karachi Stock Exchange were probed into for the period 2002- 2008. A well developed mixture of six variables along with the descriptive statistics were made the use of to scrutinize the dividend paying behavior of sectors. Inconsistency, reluctance and trivial average rate from 1.5% to 5% of the dividends were being paid by the sectors. The profitability was not functioning adequately regarding the dividends and the highly profitable sectors were also in the habit paying nominally. Most of the funds were noticed to be used for financing the growth opportunities and only the mature and highly profitable sectors were keeping pace with the growth opportunities and endeavoring to transform it to the shareholders. The market capitalization was observed to oppose the trend of the dividends in almost all the sectors but the lick of it varied with the rate of surge in the market capitalization numbers. All the sectors were having a trend to disburse and dwindle the rate of the dividend at the very beginning, middle and at last to make a drift in it during the last couple of years, particularly in 2008. The corporate governance should be strengthened in order to protect the rights of the individual shareholders. Keywords: Dividend Policy, Determinants and Paying Behavior

The impact of patients' chronic disease on family quality of life : an experience from 26 specialties

BACKGROUND: Previous studies have assessed family quality of life in individual disease areas and specialties. The aim of this study was to investigate the impact of disease on family members of patients over a wide range of specialties and identify key impact areas. This information is essential in order to reveal the extent of this impact and to allow strategies to be developed to support the family members of patients with chronic disease. METHODS: Semi-structured interviews were carried out with 133 family members of mostly chronically ill patients from 26 medical specialties. Family members were invited to discuss all areas of their lives that had been affected by having an unwell relative. Thematic analysis was carried out using NVivo9® software. RESULTS: Most family members were female (61%), the partner or spouse of the patient (56%), or the parent (22%). Their mean age was 56.1 years (range: 21-85 years) and the mean duration of the patient's disease was 8.9 years (range: 1 month to 60 years). Ten key themes of family quality of life were identified from interviews. The median number of themes reported by family members was six (range: 1-10). The key themes included: emotional impact (mentioned by 92% of subjects), daily activities (91%), family relationships (69%), sleep and health (67%), holidays (62%), involvement in medical care and support given to family members (61%), work and study (52%), financial impact (51%), social life (37%), and time planning (14%). Relationships between the themes were identified. CONCLUSION: This large scale multi-specialty study has demonstrated the significant, yet similar, impact that illness can have on the quality of life of patients' family members. Family quality of life is a previously neglected area of health care which needs to be addressed in order to provide appropriate support for the patient and the family unit.Peer reviewedFinal Published versio

Knowledge based system implementation for lean process in low volume automotive manufacturing (LVAM) with reference to process manufacturing

YesGlobal manufacturing industry mostly depends on new product development and processes to become competitive. The product development process for automotive industry is normally complicated, lengthy, expensive, and risky. Hence, a study of lean manufacturing processes for low volume manufacturing in automotive industry is proposed to overcome this issue by eliminating all wastes in the lengthy process. This paper presents a conceptual design approach to the development of a hybrid Knowledge Based (KB) system for lean process in Low Volume Automotive Manufacturing (LVAM). The research concentrates on the low volume processes by using a hybrid KB system, which is a blend of KB system and Gauging Absences of Pre-requisites (GAP). The hybrid KB/GAP system identifies all potential waste elements of low volume process manufacturing. The KB system analyses the difference between the existing and the benchmark standards for lean process for an effective implementation through the GAP analysis technique. The proposed model explores three major lean process components, namely Employee Involvement, Waste Elimination, and Kaizen (continuous improvement). These three components provide valuable information in order for decision makers to design and implement an optimised low volume manufacturing process, but which can be applied in all process manufacturing, including chemical processing

Decomposition of manufacturing processes: a review

YesManufacturing is a global activity that started during the industrial revolution in the late 19th century to cater for the large-scale production of products. Since then, manufacturing has changed tremendously through the innovations of technology, processes, materials, communication and transportation. The major challenge facing manufacturing is to produce more products using less material, less energy and less involvement of labour. To face these challenges, manufacturing companies must have a strategy and competitive priority in order for them to compete in a dynamic market. A review of the literature on the decomposition of manufacturing processes outlines three main processes, namely: high volume, medium volume and low volume. The decomposition shows that each sub process has its own characteristics and depends on the nature of the firm’s business. Two extreme processes are continuous line production (fast extreme) and project shop (slow extreme). Other processes are in between these two extremes of the manufacturing spectrum. Process flow patterns become less complex with cellular, line and continuous flow compared with jobbing and project. The review also indicates that when the product is high variety and low volume, project or functional production is applied.The financial support by the Malaysian Government, Universiti Malaysia Pahang and Bradford University for this research is gratefully acknowledged