Risk Modelling in Healthcare Markets: a Comparative Analysis of three Risk Measurement Approaches

Henry Asante Antwi, Zhou Lulin (), Ethel Yiranbon, James Onuche Ayegba, Mary-Ann Yebaoh and Emmanuel Osei Bonsu

International Journal of Academic Research in Accounting, Finance and Management Sciences, 2014, vol. 4, issue 2, 271-281

Abstract: Health care, due to its high upfront costs and centrality to humankind, is often considered ‘different’ and best left outside the domain of markets. But such blanket opposition ignores valid reasons for not dismissing the value markets could bring. Since its inception in 1948 the NHS in England has gradually evolved (and devolved) into a very different being. No longer is it – in the words of health policy analyst Rudolf Klein the ‘secular church’, maintained and presided over by disciples of its founder, Aneurin Bevan. In its current state, the NHS functions on the basis of what has been variously called a ‘quasi’, ‘mimic’ or ‘internal’ market with its own risk levels for investors. For risk managers, at the centre of strong risk management is suitable risk metrics that are constructed using complex mathematical models. This study looks at the three approaches and how they apply in computing VaR. For the three techniques, VaR is defined and the main methodologies. Thereafter, the methods are applied, separately, to the data so as to compute VaR. The results is presented and analysed respectively. Next, we discuss the advantages and disadvantages associated with the three approaches. Furthermore, we will discuss the critical yet complex issues involved with the model accuracies and mapping up of positions related to risk factors and model volatility. The report concludes with a brief summary on the issues pertinent to VaR analysis.

Keywords: Risk Modelling; Healthcare Markets; Analytical Method; Monte Carlo Simulation; Historical Simulation (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hur:ijaraf:v:4:y:2014:i:2:p:271-281

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