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Application of Discriminant Analysis on Romanian Insurance Market


  • Constantin Anghelache

    (Academy of Economic Studies, Bucharest)

  • Dan Armeanu

    (Academy of Economic Studies, Bucharest)


Discriminant analysis is a supervised learning technique that can be used in order to determine which variables are the best predictors of the classification of objects belonging to a population into predetermined classes. At the same time, discriminant analysis provides a powerful tool that enables researchers to make predictions regarding the classification of new objects into predefined classes. The main goal of discriminant analysis is to determine which of the N descriptive variables have the most discriminatory power, that is, which of them are the most relevant for the classification of objects into classes. In order to classify objects, we need a mathematical model that provides the rules for optimal allocation. This is the classifier. In this paper we will discuss three of the most important models of classification: the Bayesian criterion, the Mahalanobis criterion and the Fisher criterion. In this paper, we will use discriminant analysis to classify the insurance companies that operated on the Romanian market in 2006. We have selected a number of eigth (8) relevant variables: gross written premium (GR_WRI_PRE), net mathematical reserves (NET_M_PES), gross claims paid (GR_CL_PAID), net premium reserves (NET_PRE_RES), net claim reserves (NET_CL_RES), net income (NE—_INCOME), share capital (SHARE_CAP) and gross written premium ceded in Reinsurance (GR_WRI_PRE_CED). Before proceeding to discriminant analysis, we performed cluster analysis on the initial data in order to identify classes (clusters) that emerge from the data.

Suggested Citation

  • Constantin Anghelache & Dan Armeanu, 2008. "Application of Discriminant Analysis on Romanian Insurance Market," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 11(11(528)), pages 51-62, November.
  • Handle: RePEc:agr:journl:v:11(528):y:2008:i:11(528):p:51-62

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    References listed on IDEAS

    1. Maurice Obstfeld & Kenneth Rogoff, 2001. "The Six Major Puzzles in International Macroeconomics: Is There a Common Cause?," NBER Chapters,in: NBER Macroeconomics Annual 2000, Volume 15, pages 339-412 National Bureau of Economic Research, Inc.
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    5. Carmen M. Reinhart & Vincent Raymond Reinhart, 2002. "What Hurts Emerging Markets Most? G3 Exchange Rate or Interest Rate Volatility?," NBER Chapters,in: Preventing Currency Crises in Emerging Markets, pages 133-170 National Bureau of Economic Research, Inc.
    6. Ronald MacDonald, 1997. "What Determines Real Exchange Rates? The Long and Short of it," IMF Working Papers 97/21, International Monetary Fund.
    7. Edison, Hali J. & Pauls, B. Dianne, 1993. "A re-assessment of the relationship between real exchange rates and real interest rates: 1974-1990," Journal of Monetary Economics, Elsevier, vol. 31(2), pages 165-187, April.
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    Cited by:

    1. repec:spr:empeco:v:52:y:2017:i:4:d:10.1007_s00181-016-1107-3 is not listed on IDEAS
    2. Marín Díazaraque, Juan Miguel & Albarrán Lozano, Irene & Alonso, Pablo J., 2011. "Why using a general model in Solvency II is not a good idea : an explanation from a Bayesian point of view," DES - Working Papers. Statistics and Econometrics. WS ws113729, Universidad Carlos III de Madrid. Departamento de Estadística.


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