本文關(guān)鍵詞：基于Bayesian Bootstrap方法的準(zhǔn)備金風(fēng)險度量研究　出處：《天津財經(jīng)大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

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【摘要】：保險公司的風(fēng)險主要來自承保風(fēng)險、操作風(fēng)險、違約風(fēng)險等,其中承保風(fēng)險包括準(zhǔn)備金風(fēng)險和保費風(fēng)險。由于未來賠付的不確定性,準(zhǔn)備金的評估值在實際值附近波動,提取的準(zhǔn)備金與未來賠付可能并不相等,保險公司為了保證有充足的資本應(yīng)對非預(yù)期風(fēng)險,在技術(shù)準(zhǔn)備金的基礎(chǔ)上增加一部分作為償付能力資本要求中的組成成分,這一部分就是準(zhǔn)備金風(fēng)險。因此,準(zhǔn)備金風(fēng)險是用來抵御準(zhǔn)備金不充足的風(fēng)險,是準(zhǔn)備金與未來賠付之間差距的量化。常用的準(zhǔn)備金風(fēng)險為最終風(fēng)險,衡量的是保單在未來全部進(jìn)展年的不確定性,在隨機準(zhǔn)備金評估方法中,可以用均方根誤差來進(jìn)行度量。歐盟償付能力Ⅱ提出要基于一年期對準(zhǔn)備金風(fēng)險進(jìn)行度量,用來抵御保單在未來進(jìn)展12月準(zhǔn)備金不充足的風(fēng)險,時間范圍設(shè)定為1年。一年期準(zhǔn)備金風(fēng)險比最終準(zhǔn)備金風(fēng)險小,且更符合實際需要,更有利于風(fēng)險監(jiān)管。一年期準(zhǔn)備金風(fēng)險的度量方法可分為解析法和隨機模擬法。Wiithrich(2008)基于Mack模型,推導(dǎo)了一年期準(zhǔn)備金風(fēng)險的解析方法,解析方法理論性強,不足之處是推導(dǎo)復(fù)雜,且未能考慮尾部進(jìn)展因子。Ohlsson Lauzeningks(2009)提出了一種更加實用的一年期準(zhǔn)備金風(fēng)險度量方法——隨機模擬Re-reserving方法,該方法與解析法相比,簡便易行,且可以考慮尾部進(jìn)展因子的影響,隨機模擬Re-reserving方法主要應(yīng)用MCMC原理和Bootstrap方法。文中首先介紹了歐盟償付能力Ⅱ背景,進(jìn)而明確了一年期準(zhǔn)備金風(fēng)險的概念；并介紹了解析式方法和隨機模擬方法；然后在隨機模擬的框架下,將Bootstrap方法擴展為Bayesian Bootstrap方法,并將尾部因子考慮進(jìn)來,并可以獲得賠付進(jìn)展結(jié)果預(yù)測分布置信水平為99.5%的VaR值以量化一年期準(zhǔn)備金風(fēng)險；最后利用數(shù)據(jù)進(jìn)行了實證分析,并將Bayesian Bootstrap方法與Wiithrich Merz解析方法(MW方法)、Bootstrap方法(2008)進(jìn)行比較。結(jié)果表明：Bayesian Bootstrap方法與MW方法結(jié)果相近,且比Bootstrap方法標(biāo)準(zhǔn)差更低,過程誤差和估計誤差也更低。
[Abstract]:The risk of insurance companies from underwriting risk, operational risk, risk of default, which includes risk premium and underwriting risk reserve risk. Due to the uncertainty of future payment and reserve evaluation value in the actual value fluctuated around the reserve and future loss may not be equal to the insurance company, in order to ensure adequate capital to deal with unexpected the risk increase, as part of a solvency capital requirement in the composition on the basis of the technical reserves, which is part of the risk reserve. Therefore, the risk reserve is used to resist the risk reserve is not sufficient, is to quantify the gap between the reserve and future loss. The common reserve risk as the ultimate risk, is a measure of policy in the future all the progress of years of uncertainty in stochastic reserving method, can be used to measure the RMS error compensation of EU. The ability to pay one year to II proposed to reserve risk measurement based on the policy to resist the risk of future developments in December reserves are not sufficient, the time range is set for 1 years. One year reserve risk than the final reserve risk is small, and is consistent with the actual needs, more conducive to the risk supervision. Measure the one-year reserve the risk can be divided into analytical method and stochastic simulation method.Wiithrich (2008) based on the Mack model, the analytical method is the one-year risk reserve, the analytical method of strong theory, the deficiency is is complex, and failed to consider the tail factor in.Ohlsson Lauzeningks (2009) proposed a one-year risk reserve is more practical the measure method of stochastic simulation Re-reserving method, compared with analytical method, this method is simple and feasible, and can consider the influence factor in the tail, the stochastic simulation method Re-reserving The main application of MCMC principle and Bootstrap method. This paper firstly introduces the background of the EU Solvency II, and defines the concept of the one-year risk reserve; and introduces the analytical method and stochastic simulation method; and then in the framework of stochastic simulation, the Bootstrap method is extended to Bayesian Bootstrap method, and the tail factor into account, and can obtain compensation in distribution prediction results of 99.5% confidence level VaR values to quantify the one-year reserve risk; finally, using empirical analysis, and the Bayesian Bootstrap method and Wiithrich Merz analysis method (MW method), Bootstrap (2008) were compared. The results showed that the Bayesian Bootstrap method and MW method the results are similar, and the standard deviation is lower than the Bootstrap method, the process of error and error estimate is also lower.

【學(xué)位授予單位】：天津財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F840.31;F224

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