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This paper examines volatility of Indian Stock Market returns using GARCH models that capture the volatility clustering and leverage effect. The analysed data used in the study are daily closing prices of Nifty index during 2005 to 2019. GARCH (1,1), EGARCH (1,1) and TGARCH (1,1) models are employed after confirming unit root test, volatility clustering and ARCH effect. Asymmetric GARCH models reveal a presence of leverage effect and also confirm the effect of conditional volatility. Findings exhibit that the coefficient has a likely indication both in EGARCH (negative, significant) and TGARCH (positive, significant) models. EGARCH (1,1) model fits better to capture the asymmetric volatility. [ABSTRACT FROM AUTHOR]

Quality in pharmaceutical products is a quintessential requisite. Indeed, the difference between medicine and poison is that of quality/quantity and not of kind. Same medication can a cure a patient or kill him/her depending upon the quality levels inherent into it. Quality therefore is ardently built in the products and is sought to be maintained throughout their shelf-life by the regulatory authorities as well as the marketers themselves. Good manufacturing practices have evolved exceptionally well in the pharmaceutical companies due to regulatory pressures and technological developments. The human integrity also has improved in this context. Yet the question remains whether or not quality is preserved with the same commitment during the supply chain of pharmaceutical products. This study was undertaken to measure the good distribution practices of pharmaceutical products in India. Three variables namely temperature control, humidity control and microbiological control were selected and the awareness, knowledge, willingness, availability of equipment/facilities, availability of power to run the facilities and log book of records about these variables was measured. The results clearly establish that existing mechanisms are grossly inadequate in the existing supply chain of pharmaceutical products. [ABSTRACT FROM AUTHOR]

Defining business model constitutes a major challenge. This is primarily because there are many different aspects to a business, hence when we talk of business models; it means different things to different people. Difference lies in the very concept of what constitutes a business (key aspects) and how such a concept can be represented using a common notation. One solution to the problem is to use ontology to communicate. Ontologies are agreed upon frameworks for representing concepts in any domain area. Hence, ontologies are used to represent knowledge, processes, business motivations, business strategies,enterprise structure, and more including business models. The Business Model Ontology (BMO) is one such ontology. Designed and developed by Alexander Osterwalder, it is aimed specifically at representing, understanding, communicating and analyzing business models. This paper is an evaluation of the Business Model Ontology (BMO).The paper consists of two parts. In the first part the researchers describe the four pillars, the nine elements and their sub-elements comprising the ontology. In the second part the ontology is reviewed and evaluated using nine criteria. The fundamental aim is to examine the ontology capabilities – its strength and weaknesses. [ABSTRACT FROM AUTHOR]