保險公司清償能力一直是保險監理的重心，在所有現行的制度中風險基礎資本是最重要的，但此項制度仍有其缺點，因此其他動態分析模型被許多學者所提出，如涉險值及情境分析。雖然這些動態分析模型被學者所偏好，但監理機關仍須對這些模型的精確程度加以了解，這也是本篇論文所要研究的目的。

基於此，本篇論文以模擬方式及經濟模型加以分析風險基礎資本、情境分析及涉險值等方法的破產預測的相對精確性。其中風險基礎資本完全採用現有NAIC的年報資料，情境分析及涉險值則採用我們所建立的模型，基於此也可以確認現有監理制度是否有缺失。

我們的結果發現風險基礎資本的預測能力很低，動態模型-情境分析及涉險值皆優於風險基礎資本，且在不同動態模型中涉險值的預測能力較好。因此可知被學者所偏好的動態分析模型應是未來保險監理的方向希望藉由本篇提供監理機關一個參考的依據。

Solvency prediction of insurers has been the focus of insurance regulation. Among the solvency regulation systems, risked-based capital (RBC) is the most important but RBC still has some drawbacks. Thus, the dynamic financial analyses-scenario analysis and Value at Risk have been developed to be the regulation tool. Although, the scholars prefer the dynamic financial analysis, the regulators still want to make sure the accuracy of dynamic financial analysis. That is the purpose of our paper.

Therefore, we use the simulation result and the econometric model to analyze the relative effectiveness of RBC, scenario and Value at Risk (VaR). The RBC is from the annual statement and the scenario and VaR come from our simulation model.

Our result shows that the RBC has very low explanatory power, the dynamic financial analysis is better than RBC, and VaR outperform scenario analysis. Thus, we conclude that VaR is the way to go for property-casualty insurance regulators.

Reference
1. Ahlgrim, Kevin C. (1999), Investigating the use of value at risk in insurance, Working Paper, University of Illinois at Urbana-Champaign Department of Finance.
2. Altman, Edward I., 1992, Revisiting the High Yield Bond Market, Financial Management, 21: 78-92.
3. Ambrose, Jan M. and Anne M. Carroll, 1994, Using Best's Ratings in Life Insurer Insolvency Prediction, The Journal of Risk and Insurance, 61:317-327.
4. Ambrose, Jan Mills and J. Allen Seward, 1988, Best's Ratings, Financial Ratios and Prior Probabilities in Insolvency Prediction, The Journal of Risk and Insurance, 55:229-244.
5. Baraanoff, Etti G., Thomas W. Sager, and Robert C. Witt, 1999, Industry Segmentation and Predictor Motifs for Solvency Analysis of the Life/Health Insurance Industry, Journal of Risk and Insurance, 66:99-123.
6. BarNiv, Ran and Adi Raveh, 1989, Identifying Financial Distress: A New Nonparametric Approach, The Journal of Business Finance and Accounting, 16:361-381.
7. BarNiv, Ran and James B. McDonald, 1992, Identifying Financial Distress in the Insurance Industry: A Synthesis of Methodological and Empirical Issues, The Journal of Risk and Insurance, 543-574.
8. BarNiv, Ran and Robert A. Hershbarger, 1990, Classifying Financial Distress in the Life Insurance Industry, The Journal of Risk and Insurance, 57:110-136.
9. Brockett, Patrick L., William W. Cooper, Linda L. Golden, and Utai Pitaktong, 1994, A Neural Network Method for Obtaining an Early Warning of Insurer Insolvency, The Journal of Risk and Insurance, 61:402-424.
10. Carson, James M. and Robert E. Hoyt, 1995, Life Insurer Financial Distress: Classification Models and Empirical Evidence, The Journal of Risk and Insurance, 62:764-775.
11. Cummins, J, David, Martin F. Grace, and Richard D. Phillips, 1999, Regulatory Solvency Prediction in Property-Liability Insurance: Risk-Based Capital, Audit Ratios, and Cash Flow Simulation, Journal of Risk and Insurance, 66:417-458.
12. Cummins, J. David, Scott E. Harrington, and Robert Klein, 1995, Insolvency Experience, Risk-Based Capital, and Prompt Corrective Action in Property-Liability Insurance, Journal of Banking and Finance, 19:511-527.
13. Dimson, Elroy and Paul Marsh, 1997, Stress tests of capital requirements, Journal of Banking & Finance, 21:1515-1546.
14. Grace, Martin F., Scott E. Harrington, and Robert W. Klein, 1998, Risk-Based Capital and Solvency Screening in Property-Liability Insurance: Hypotheses and Empirical Tests, The Journal of Risk and Insurance, 65:213-243.
15. Harrington, Scott E. and Jack M. Nelson, 1986, A Regression-Based Methodology for Solvency Surveillance in the Property-Liability Insurance Industry, The Journal of Risk and Insurance, 53:583-602.
16. Huang, Chin-Sheng, Robert E. Dorsey, and Mary Ann Boose, 1994, Life Insurer Financial Distress Prediction: A Neural Network Model, Journal of Insurance Regulation, 13:131-167.
17. Ibbotson Associates, Stocks, Bonds, Bills and Inflation: Yearbook (Chicago, IL: Ibbotson Associates).
18. Jackson, Patricia, David J. Maude, and William Perraudin, 1997, Bank Capital and Value at Risk, The Journal of Derivatives, 73-89.
19. Jorion, Philippe, 1997, Value at Risk: The New Benchmark for Controlling derivatives Risk, The McGraw-Hill companies.
20. Klein, Robert W., 1995, Insurance Regulation in Transition, The Journal of Risk and Insurance, 62:363-404.
21. Munch, Patricia and Dennis E. Smallwood, 1980, Solvency regulation in the property-liability insurance industry: empirical evidence, Bell Journal of Economics, 11:261-279.
22. Panning, William H., 1999, The Strategic Uses of Value at Risk: Long-Term Capital Management for Property/Casualty Insurers, North American Actuarial Journal, 2:84-105.
23. Pottier, Steven W., 1998, Life Insurer Financial Distress, Best's Ratings and Financial Ratios, The Journal of Risk and Insurance, 65:275-288.
24. Pottier, Steven W. and David W. Sommer, 1999, Property-Liability Insurer Financial Strength Ratings: Differences Across Rating Agencies, The Journal of Risk and Insurance, 66:621-642.
25. Pottier, Steven W. and David W. Sommer, 1999, Capital Ratios and Property-Liability Insurer Insolvencies, Working Paper, University of Georgia.
26. West, Robert Craig, 1985, A Factor-Analytic Approach to Bank Condition., Journal of Banking and Finance, 9:253-266.
27. Wiener, Zvi, 1997, Introduction to VaR (Value-at-Risk), Working Paper, The Hebrew University of Jerusalem.