Estimating KDIC Target Guaranty Fund for Non-life Insurers*
Estimating KDIC Target Guaranty Fund for Non-life Insurers
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초록
Estimating the appropriate level of Target Guaranty Fund for financial institutions has been a challenging mission for the deposit insurance system because the financial safety of insurance policyholders will be at risk. The level of the Target Guaranty Fund should be sufficient to sustain financial distress of financial institutions but testing sustainability of the fund is not always available unless the financial market actually experienced the financial distress. The dramatic changes of market condition nowadays such as the uprising of stock market in 2007 and the collapse of global financial market in 2008 provide us the unique opportunity to test the sustainability. We examine Creditmetrics® and KMV models and the input variables in those models by estimating the Value at Risk (VaR) value of the target guaranty fund system for non-life insurance sector in Korea. We find that the most significant input variable for the models, the volatility of asset values, is not stable to provide sustainable level of Target Guaranty Fund for non-life insurers in Korea and propose that we need to estimate the Target Guaranty Fund level dynamically.
Abstract
Estimating the appropriate level of Target Guaranty Fund for financial institutions has been a challenging mission for the deposit insurance system because the financial safety of insurance policyholders will be at risk. The level of the Target Guaranty Fund should be sufficient to sustain financial distress of financial institutions but testing sustainability of the fund is not always available unless the financial market actually experienced the financial distress. The dramatic changes of market condition nowadays such as the uprising of stock market in 2007 and the collapse of global financial market in 2008 provide us the unique opportunity to test the sustainability. We examine Creditmetrics® and KMV models and the input variables in those models by estimating the Value at Risk (VaR) value of the target guaranty fund system for non-life insurance sector in Korea. We find that the most significant input variable for the models, the volatility of asset values, is not stable to provide sustainable level of Target Guaranty Fund for non-life insurers in Korea and propose that we need to estimate the Target Guaranty Fund level dynamically.
참고문헌 (15)
- Crouhy, M., "A Comparative Analysis of Current Credit Risk Models", Journal of Banking and Finance, 24(1), pp.59~117 (2000)
- Credit Suisse Financial Products, "Risk Management Framework", ~ (1997)
- Credit Risk Modeling: A Literature Survey, "D’Souza, D.", Working paper, ~ (2003)
- Hull, John, "Options, Futures, and Other Derivatives", Prentice Hall, ~ (2003)
- J.P.Morgan, "CreditMetrics-Technical Document", ~ (1997)
- Kealhofer, S., "Managing Default Risk in Portfolios of Derivatives, In Derivative Credit Risk" (1995), Risk Books
- Kealhofer, S., "Portfolio Management of Default Risk", Net Exposure 1, ~ (1998)
- Korea Money and Finance Association (KMFA), "Study on Korean Deposit Insurance System", ~ (2007)
- "A Generalized Framework for Credit Risk Portfolio Models, Oliver" (1998), Wyman & Company
- Methodology for Testing the Level of the EDF Credit Measure, "Matthew, K", KMV White Papers, ~ (2002)
- Merton, R.C., "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates", Journal of Finance, 28 (1974)
- Phillipe Jorion, Value at Risk, "New Benchmark for Managing Financial Risk" (2000), McGraw-Hill
- De Servigny,Arnaud, "Measuring and Managing Credit Risk" (2004), McGraw-Hill
- Svetlozar T. Rachev, "-Tailed and Skewed Asset Return Distributions: Implications for Risk Management, Portfolio Selection, and Option Pricing" (2005), Wiley & Sons, Inc
- Vasicek, O., "Credit Valuation", Net Exposure, ~ (1997)