臺灣博碩士論文加值系統

確保保險公司的清償能力一直是保險監理的重心。在所有施行的保險清償監理工具中，風險基礎資本（Risk-Based Capital, RBC）是目前為止最先進的代表。然銀行監理機關已經推薦涉險值（Value at Risk, VaR）系統為資本適足要求的工具，因此涉險值有很大的潛力成為下一代的保險資本適足要求工具，雖然尚未施行。由於保險監理的重要性以及RBC和VaR在其中扮演重要的角色，兩者相對上的精確性是我們所感興趣的。
本篇論文的目的是實際去比較RBC及VaR在破產預測上的相對精確性。我們以美國1995到1998年產險公司的實際清償記錄，用型1及型2錯誤檢視RBC及VaR的破產預測能力。RBC的數據直接從產險公司報給NAIC的年報上就可取得，而VaR的數據來自於我們所建立的現金流量模擬模型。既然RBC的數據是實際的數據，而VaR的估計值也是基於公司實際的財務數據而來，我們能以實例展現VaR相較於RBC的財務預警能力。
我們的結果顯示RBC沒有任何財務預警能力，換句話說，沒有一個破產公司的RBC值小於0.7（監理機關可以根據這個值關掉公司）。另一方面，VaR有較好的財務預警能力，但是它同時也會使許多財務健全的公司必須接受許多沒有必要的檢查。我們VaR模型的整體正確分類能力只比隨意分類稍微好一些。
雖然結果並不如原先預期的好，我們仍然對VaR成為保險監理工具抱持樂觀的態度，因為它是目前為止最嚴密也最先進的風險管理工具。我們認為這些結果可以藉由修正不適當的假設後獲得改善，未來研究可以先朝這個方向努力。

Assuring insurance company solvency has always been the focal point of insurance regulation. Among the employed solvency regulation methods, RBC represents the currently state-of-the-art capital adequacy requirement. Bank regulators already advocated the use of VaR systems in capital adequacy requirements. Value at risk thus has great potential to be the next-generation capital adequacy regulation, although not implemented yet. Because of the importance of solvency regulation as well as the key role played in that regulation by RBC and VaR, the relative accuracy of RBC and VaR is of great interest.
The purpose of this research is to empirically compare the relative effectiveness of RBC and VaR in predicting insolvency. Through the solvency record of property-casualty insurers in the United States from 1995 to 1998, we examine the Type I and Type II error of VaR and RBC in predicting insolvency. The RBC figures are readily available from the annual statement since 1994 and the VaR values come from a simulation model that we build up. Since the RBC figures are the “real” numbers and the VaR estimates also base on the companies’ real financial positions, our research will demonstrate how VaR is compared to RBC in early warning for real cases.
Our result shows that RBC doesn’t have any prediction power. In other words, none of the bankrupt insurers has a RBC ratio lesser than 0.7, the threshold according to which the regulator can seize the company. On the other hand, VaR has good early warning ability, but also leads the regulator to take too much unnecessary actions on solvent companies. The overall ability of correct classification of our model is just a little stronger than arbitrary classification.
Although our results are not as good as we expect, we are still optimistic about the use of VaR, the currently most comprehensive and advanced approach of risk management, as an insurance solvency regulation tool. We attribute the unsatisfactory outcome to some assumptions that may be inappropriate. Further researches can move toward this aspect.

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