ISSN: 2225-8329
Open access
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.
Allen, D. E., & Powell, R. (2009) Transitional credit modelling and its relationship to market at Value at Risk: An Australian sectoral perspective', Accounting and Finance, vol.49, no.3, pp. 425-444.
Bali, T. G., Gokcan, S., & Liang, B. (2007). Value-at-Risk and the cross-section of hedge fund returns. Journal of Banking and Finance, vol.31, no.4, pp. 1135-1166.
Bank for International Settlements (2006). Amendment to the Capital Accord to Incorporate Market Risks.
Brown, A. (2007). ‘On Stressing the Right Size’ GARP Risk Review.
Chiu, Y., Chang, I., & Lai, J. (2010) The performance of composite forecast models of Value-at Risk in the energy markets.', Energy Economics, vol.25, pp. 239-253.
Choudhry, M. (2004). Fixed Income Markets. Instruments, Applications, Mathematics, John Wiley & Sons (Asia) Pty Ltd, Singapore.
Einhorn, D. (2008) Private Profits and Socialized Risk, GARP Risk Review
Glasserman, P. (2004). Monte Carlo Methods in Financial Engineering. Springer.
Haug, E. (2007). Derivative Models on Models. John Wiley & Sons.
Markovich, N. (2007). Nonparametric analysis of univariate heavy-tailed data, Wiley & Sons.
Nicholas, T. N. (2007). The Black Swan: The Impact of the Highly Improbable. New York: Random.
Novak, S. Y. (2011). Extreme value methods with applications to finance. Chapman & Hall/CRC Press.
Taleb, N. (1997). The Jorion-Taleb Debate, Derivatives Strategy.
In-Text Citation: (Antwi et al., 2014)
To Cite this Article: Antwi, H. A., Lulin, Z., Yiranbon, E., Ayegba, J. O., Yebaoh, M-A., & Bonsu, E. O. (2014). Risk Modelling in Healthcare Markets: A Comparative Analysis of three Risk Measurement Approaches. International Journal of Academic Research in Accounting Finance and Management Sciences. 4(2), 353 – 366.
Copyright: © 2014 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
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