SavedAdd your journal

Journals / Sigma Journal of Engineering and Natural Sciences / 2015 / Cilt: 33 Sayı: 1

RISK MEASUREMENT FOR MOTOR THIRD PARTY LIABILITY INSURANCE IN TURKEY

Aslıhan ŞENTÜRK ACAR
Uğur KARABEY
Pages
103–111
Publication Date
2015-03-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

With the risk measures calculated for three different confidence levels, maximum losses for various risk classes are achieved and the results are compared.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, a motor third party liability insurance data set is examined, claim amounts are modeled and risk measures are calculated according to the significant explanatory variables.
📊
Key Findings & Data:Value at Risk and Expected Short Fall methods are used for the risk measures.
💡
Conclusion & Impact:With the risk measures calculated for three different confidence levels, maximum losses for various risk classes are achieved and the results are compared.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Risk measure · generalized linear models · motor third party liability insurance.

Abstract

In this study, a motor third party liability insurance data set is examined, claim amounts are modeled and risk measures are calculated according to the significant explanatory variables. Value at Risk and Expected Short Fall methods are used for the risk measures. With the risk measures calculated for three different confidence levels, maximum losses for various risk classes are achieved and the results are compared.

🔖

Cite This Article

ACAR, A. Ş., KARABEY, U. (2015). RISK MEASUREMENT FOR MOTOR THIRD PARTY LIABILITY INSURANCE IN TURKEY. Sigma Journal of Engineering and Natural Sciences, Cilt: 33 Sayı: 1, 103–111. https://ajindex.com/articles/897943

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (12)

  1. Ohlsson, E., Johansson, B, “Non-Life Insurance Pricing with Generalized Linear Models”, Springer-Verlag, Berlin, 2010
  2. Karabey U., “Risk Measures and Risk Capital Allocation”, İstatistikçiler Dergisi 5 (2012) 32-42, 2012
  3. Artzner P.,Delbaen F., Eber J. M., Heath D., “Coherent Measures of Risk”, Mathematical Finance, 9-3: 203-228,1999
  4. McCullagh, P., Nelder, J.A., “Generalized Linear Models”, Chapman and Hall, London, 1989
  5. Kaas, R., Goovaerts M., Dhaene J., Denuit M., “Modern Actuarial Risk Theory: Using R”, Springer Verlag Inc., Berlin, 2008
  6. De Jong, P., Heller G.Z., “Generalized Linear Models for Insurance Data”, Cambridge University Press, London, 2008
  7. Hardy M. R., “An Introduction to Risk Measures for Actuarial Applications”, Education and Examination Committee of SOA, Construction and Evaluation of Actuarial Models Study Note, 2006
  8. McNeil AJ, Frey R and Embrechts P, “Quantitative Risk Management: Concepts, Techniques and Tools”, Princeton University Press, 2005
  9. Artzner P., Delbaen F., Eber J.M., Heath D., “Thinking Coherently”, RISK, 10:68-71, 1997. [10] “Karayolları Motorlu Araçlar Zorunlu Mali Sorumluluk Sigortası Genel Şartları”, T.C. Resmi Gazete, 25197, 12 Ağustos 2003
  10. Brockman, M. J., Wright, T. S., “Statistical Model Rating: Making Effective Use of Your Data”, Journal of the Institute of Actuaries 119, 457, 1992
  11. Fu L., Moncher R. B., “Severity Distributions for GLMs, Gamma or Lognormal? Evidence from Monte Carlo Simulations”, Casualty Actuarial Society Discussion Paper,Arlington,Virginia, 2004
  12. Mildenhall, S.,J., “Systematic Relationship Between Minimum Bias and Generalized Linear Models”, PCAS LXXXVI,393-487,1999.