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題名：探討台灣保險之三篇論文
作者：賴丞坡 引用關係
作者(外文)：Cheng-Po Lai
校院名稱：國立中正大學
系所名稱：企業管理研究所
指導教授：劉亞秋
林文昌
學位類別：博士
出版日期：2004
主題關鍵詞：核保循環；傅立葉轉換；風險值；核保系統風險；理性預期模型；現金流量核保；Underwriting cycle；Discrete Fourier Transform；VaR；Insurance CAPM；Information and competitive price systems theory；Cash Flow Underwriting
原始連結：連回原系統網址new window
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本研究共有三篇論文，分別討論臺灣保險相關的理論和實證議題。第一篇論文檢視臺灣商業健康保險的核保循環。本文使用離散傅立葉轉換技術和月資料以證明商業健康保險核保循環的存在。其核保循環之週期長度大約為5.33年，且實證顯示外在因素與內在因素皆對核保循環有所影響。其次，全民健康保險在商業健康保險的核保循環中扮演相當重要的角色，全民健康保險寬鬆的理賠政策可能會導致商業健康保險理賠增加，進而造成商業健康保險保費的提高。此外，在核保循環中，當損失率高低不同時，外在因素對於保險公司訂定保費也有不同的影響。在損失率高時，外在因素對於核保循環並無顯著影響；在損失率低時，外在因素中的失業率及醫療費用，則對核保循環有顯著影響。
第二篇論文是關於保險公司的風險管理。以往產險業在風險值的應用上，核保風險因受限於較高頻率的資料不易統計，而大幅影響風險值估計的精確度。相對於過去文獻係以年資料為計算基礎，本文以佔產險業務最大宗的車險，利用月資料來計算風險值，結果顯示若使用年資料或季資料估算核保風險值時，相較於月資料，風險值將被顯著低估。此外，本文以橫斷面迴歸分析公司差異，結果發現各公司之核保風險值明顯受到再保險比率、公司規模和自由現金流量等因素影響，代表監理機構在公司特性上有考量必要。另外，從CAPM模型當中可以發現，使用月資料可以觀察出產險公司的核保利潤明顯有系統風險存在，此種現象值得保險監理機關注意。再者，從橫斷面迴歸分析公司特性對系統風險影響，結果發現各公司之系統風險值明顯受到槓桿比率、再保比率以及公司規模等因素影響。
最後，第三篇論文檢視保費和損失之關係。本文運用訊息和競爭價格的理性預期理論，建構二個均衡模型以解釋保費和損失之線性關係存在，實證結果亦支持理論模型。藉由使用月資料，本文發現大公司傾向提供汽車保險的折價方式以促成現金流量核保，此種現象，則無法由年資料觀察。
綜言之，本研究實證結果發現，保險公司的管理或監理，使用月資料有較佳的效果。最後，建議主管機關日後在保險監理上，能研擬以月資料作為衡量保險公司風險之基礎，有效評估保險公司之清償能力，以確保保戶權益。
There are three essays in this dissertation to examine the theoretical and empirical issues on the Taiwan insurance market. The first essay examines the underwriting cycle in the business health insurance in Taiwan. I use Discrete Fourier Transform techniques and monthly data to examine the presence of the underwriting cycles. The cycle length of the business health insurance based on Fourier analysis is around 5.33 years. For the factors of cycle behavior, the evidence supports that no single hypothesis dominates in explaining the insurance cycle. The NHI plays an important role in business health insurance. The loose claim policy for the NHI would cause the increase of business health insurance claims and thus increase the business health insurance premiums. The acts of insurers are different in the hard market and the soft market of the underwriting cycle. In the hard market the insurers may set premiums according to their past experience and premiums are not affected by exogenous factors. In the soft market, the exogenous factors, such as unemployment rate and medical expense, may affect health premiums.
The second essay is about the risk management of insurers. Underwriting risk is critical in the property-liability insurance industry which can be characterized by automobile insurance. I employ monthly auto insurance loss ratio data to calculate underwriting VaR. The result supports that using annual data, which is applied by previous researches, may significantly underestimate the true VaR. In addition, the evidence from cross-sectional regression indicates that some firm characteristics, e.g., reinsurance rate, firm size and free cash flow, significantly affect the VaR for a specific property and casualty insurer. On the other hand, from the insurance CAPM model, the systematic risks are not significant based on annual data analysis. However, the systematic risks exist while using monthly data. Moreover, the evidence from cross-sectional regression indicates that some firm characteristics, e.g., leverage ratio, reinsurance rate and firm size, significantly affect the systematic risks for insurers. Regulators should take them into account in the capital or solvency surveillance.
Finally, the relationship between premium and loss is examined in the third essay. I adopt information and competitive price systems theory and construct two equilibrium models to explain the linear relationship between premiums and losses. The empirical results support the theoretical models. By using monthly data, I find that big companies may use cash flow underwriting by offering price discount to accommodate the rate regulation, and that can not be observed by using annual data before.
From empirical tests in this dissertation, I find that monthly data can be more successful in managing the risks of insurers. In this regard, the regulators could consider taking monthly data in the formula in auditing the insolvency of insurers.
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