銀行保險(xiǎn)基金的資本化外文翻譯

1、銀行保險(xiǎn)基金的資本化【作者】凱文P希恩, 金融經(jīng)濟(jì)學(xué)家【來源】聯(lián)邦存款保險(xiǎn)公司研究和統(tǒng)計(jì)部，F(xiàn)DIC的工作文件98-1摘要：雙態(tài)的馬科夫轉(zhuǎn)換模型能夠預(yù)估銀行保險(xiǎn)基金按時(shí)間先后順序的支出行為的特點(diǎn)。預(yù)估出來的模型可以用于預(yù)測(cè)未來支出走向，而這些預(yù)測(cè)的支出走向又可以用于評(píng)估銀行保險(xiǎn)基金是否有破產(chǎn)的可能或者是否可能降至低于兩個(gè)不同的最低準(zhǔn)備金值。模擬結(jié)果證實(shí)，如果假設(shè)之前的虧損歷史就是一個(gè)對(duì)未來虧損的良好指標(biāo)，那么目前的資金安排（約有23個(gè)基點(diǎn)的估價(jià)率，1.25的法定準(zhǔn)備金比率）足以維持該基金的償付能力。在這方面，巴里克萊特、史蒂文西利格、詹姆斯馬里諾、林恩史布特以及丹尼爾駑瑟爾的意見和建議是得到普

2、遍認(rèn)可的。文中的觀點(diǎn)正是這些作者的觀點(diǎn)而不一定是聯(lián)邦存款保險(xiǎn)公司的意見。1.介紹聯(lián)邦存款保險(xiǎn)公司須保持銀行保險(xiǎn)基金（BIF）至少有1.25的存款保險(xiǎn)儲(chǔ)量，截至1996年底其實(shí)際存款準(zhǔn)備金率為1.34。鑒于銀行保險(xiǎn)基金在20世紀(jì)80年代末和90年代初的損失經(jīng)驗(yàn)，這些比率的水平是否充足是值得懷疑的。然而，與此同時(shí)，由于目前銀行業(yè)的財(cái)務(wù)狀況較為穩(wěn)健，因此這些層次的需求是否有必要也是一個(gè)問題。這項(xiàng)研究對(duì)類似于過去經(jīng)驗(yàn)的虧損進(jìn)行了假設(shè)，并且模擬了銀行保險(xiǎn)基金的未來的儲(chǔ)備水平，以及探討不同的評(píng)估利率和存款準(zhǔn)備金率的對(duì)應(yīng)意義。結(jié)果表明，從未來的銀行業(yè)危機(jī)的角度來看，當(dāng)聯(lián)邦存款保險(xiǎn)公司做出一個(gè)將要影響其資金損

3、失的維持的決定時(shí)，它在某種程度上必須要平衡兩個(gè)可能互相沖突的目標(biāo)。兩個(gè)目標(biāo)的其中之一是盡量減少評(píng)估率，而另一個(gè)則是盡量避免違背指定的最低資金水平。在某種程度上來說，目前的評(píng)估率不管在什么時(shí)候都是一個(gè)介于銀行保險(xiǎn)基金儲(chǔ)備和存款保險(xiǎn)之間的比率函數(shù)。如果這個(gè)比率降低至125個(gè)基點(diǎn)，那么聯(lián)邦存款保險(xiǎn)公司要么需要通過法律對(duì)該行業(yè)進(jìn)行評(píng)估以使其回升到最低水平值，要么就要采取一個(gè)恢復(fù)計(jì)劃，而其年度評(píng)估率最少不能低于23個(gè)基點(diǎn)（聯(lián)邦存款保險(xiǎn)公司可以要求國會(huì)放寬這項(xiàng)規(guī)定，并在設(shè)置不低于23個(gè)基點(diǎn)的溢價(jià)水平方面給予機(jī)構(gòu)更大的靈活性）。然而，聯(lián)邦存款保險(xiǎn)公司必須要使基金至少不低于125個(gè)基點(diǎn)，任何危機(jī)都可能導(dǎo)致基點(diǎn)

4、降至這個(gè)水平之下。這一點(diǎn)非常重要，因?yàn)槁?lián)邦存款保險(xiǎn)公司不僅僅需要避免任何破產(chǎn)的可能性，而且還要維持公眾的信心。為了幫助確保公眾的信心，聯(lián)邦存款保險(xiǎn)公司可能還需要保持其存款儲(chǔ)備金率高于某個(gè)值，如50或75個(gè)基點(diǎn)。我們將構(gòu)建一個(gè)模型（馬爾可夫切換模型）對(duì)這些問題進(jìn)行研究，這個(gè)模型可以在一定條件之下預(yù)測(cè)未來的支出。這些預(yù)測(cè)的支出走向又可以用于評(píng)估銀行保險(xiǎn)基金是否有破產(chǎn)的可能或者是否可能降至低于兩個(gè)不同的最低準(zhǔn)備金值。這些概率是在不同法定存款準(zhǔn)備金率的評(píng)估率值范圍內(nèi)進(jìn)行評(píng)估的（請(qǐng)注意這種分析并未明確指出近期銀行合并會(huì)對(duì)聯(lián)邦存款保險(xiǎn)公司的償付能力有何影響）。2方法論概述本文使用蒙特卡羅模擬分析銀行保險(xiǎn)基

5、金的充足性。我列出了未來支出的序列，然后將這些預(yù)測(cè)出的未來支出與回收率和評(píng)估一覽表結(jié)合起來研究隨時(shí)間推移的銀行基金水平。通過使用這些模擬的支出序列，我可將替代資助計(jì)劃的破產(chǎn)概率進(jìn)行量化。具體來說，我模擬出上千的支出序列以及序列的比例，這些詳盡敘述了銀行保險(xiǎn)基金可以決定破產(chǎn)的可能性。當(dāng)銀行倒閉時(shí)，銀行必須現(xiàn)金支付保險(xiǎn)基金以補(bǔ)償存款保險(xiǎn)的損失。然而，這種基金的流失可以從銀行的資產(chǎn)出售和銀行積累的保費(fèi)收入而得到彌補(bǔ)。從歷史上看，年度基金支付范圍從0個(gè)基點(diǎn)（1962年）上升至105個(gè)基點(diǎn)（1991年），而破產(chǎn)銀行資產(chǎn)出售的回收率平均在65左右。例如，假設(shè)大型的年度資金支出為100個(gè)基點(diǎn)，而銀行的回收率

6、在65的話，該銀行倒閉后的資產(chǎn)出手就可將總支出減少至35個(gè)基點(diǎn)，而保費(fèi)收入將進(jìn)一步抵消了總支出。事實(shí)上，如果銀行被估為23個(gè)基點(diǎn)的平均溢價(jià)，基金的流失將減少到12個(gè)基點(diǎn)。模擬方法可以借鑒上面的例子進(jìn)行闡述。如果銀行基金的差額在 t 階段的初期是100個(gè)基點(diǎn)的話，我們就假設(shè)模擬一個(gè)100基點(diǎn)的基金支出和65的回收率以及23個(gè)基點(diǎn)的評(píng)估。到了這個(gè)階段的最后，銀行的基金差額將會(huì)降至12個(gè)基點(diǎn)。鑒于此基金的流失，基金t +1階段的初期差額則會(huì)變?yōu)?8基點(diǎn)。然后再為t +1階段模擬一個(gè)新階段的資金支出，銀行就可以同樣的方法來決定基金在t +2階段的初期差額。通過跨序列模擬支出的模擬算法來跟進(jìn)保險(xiǎn)基金水平

7、，這一進(jìn)程將不斷繼續(xù)下去。通過使用這種方法，我可以根據(jù)目前的資金安排（23個(gè)基點(diǎn)的估價(jià)率和1.25%的準(zhǔn)備金率）以及替代基金計(jì)劃表來量化破產(chǎn)概率（低于各種最小準(zhǔn)備金率的可能性）。這些可能性讓我以一種有意義的方式處理不同的資金安排的充足性。我將會(huì)列出未來基金支出的序列，并使用這些預(yù)計(jì)支出以及回收率和評(píng)估計(jì)劃來跟進(jìn)隨著時(shí)間的推移的銀行基金水平。我認(rèn)為，銀行倒閉的概率在銀行危機(jī)期間要高于普通時(shí)期，我們通過使用一個(gè)模型來捕捉這種差異。這個(gè)模型考慮到了隨著時(shí)間而推移變化的數(shù)據(jù)產(chǎn)生過程。為了模擬未來支出的序列，我使用到了馬可夫轉(zhuǎn)換模型，下面我將解釋其結(jié)構(gòu)。這個(gè)模型對(duì)于未來支出的預(yù)測(cè)是建立于兩個(gè)不同的歷史數(shù)

8、據(jù)之上的，一個(gè)是從1934年至1996年這63年期間的數(shù)據(jù)，另外一個(gè)則是源于1972年至1996年這個(gè)更動(dòng)蕩的25年。通過在這兩個(gè)不同的歷史數(shù)據(jù)集的基礎(chǔ)上來預(yù)測(cè)支出，我能夠分析在兩種非常不同的假設(shè)下的資金安排的充足性。第一個(gè)模擬基于1934年至1996年這63年期間的支出數(shù)據(jù)，它表明未來的損失較低。相比之下，第二次的模擬基于1972年至1996年這25年間的支出，它則顯示出未來虧損較高。事實(shí)上，第二次模擬的平均支出增加超過100。Capitalization of the Bank Insurance FundKevin P. SheehanFinancial EconomistFederal

9、 Deposit Insurance CorporationDivision of Research and StatisticsFDIC Working Paper98-1Abstract: A two-state Markov-switching model is estimated to characterize the time series behavior of disbursements from the Bank Insurance Fund (BIF). The estimatedmodel is used to project future disbursements, a

10、nd these projected disbursements are used to estimate the likelihood of insolvency as well as the likelihood of the BIF falling below two different minimum reserve ratios. The simulation results confirm that the current funding arrangementan assessment rate of 23 basis points with a 1.25 percent req

11、uired reserve ratiois sufficient to maintain BIF solvency if one assumes that the prior history of losses is a good indicator of future losses. The comments and suggestions of Barry Kolatch, Steven Seelig, James Marino, Lynn Shibut, and Daniel Nuxoll are gratefully acknowledged. The views expressed

12、are those of the author and not necessarily those of the Federal Deposit Insurance Corporation.1 Introduction The FDIC is required to maintain Bank Insurance Fund (BIF) reserves of at least 1.25 percent of insured deposits; as of year-end 1996 the actual reserve ratio was 1.34 percent. In light of t

13、he BIFs loss experience in the late 1980s and early 1990s, questions have been raised about the adequacy of these levels. At the same time, however, given the currently robust financial state of the banking industry, questions have also been raised about the need for these levels. This study, assumi

14、ng losses similar to those experienced in the past, simulates the BIFs future reserve levels and examines the implications of different assessment rates and required reserve ratios. The results indicate the extent to which the FDIC may have to balance two possibly conflicting objectives when it make

15、s decisions affecting its ability to sustain funding losses from a future banking crisis. One of the two objectives is the desire to minimize assessment rates. The other is the desire to avoid breaching a specified minimum fund level. Currently assessment rates are to some extent a function of the r

16、atio between BIFreserves and insured deposits at any given time. If the ratio drops below 125 basis points, the FDIC is required by law either to assess the industry to bring the fund back to this minimum level or to adopt a restoration plan whereby the annual assessment rate is at least 23 basis po

17、ints. (The FDIC could ask Congress to relax this requirement and afford the agency greater flexibility in setting premium levels of less than 23 basis points.) But while the FDIC is required to maintain a fund of at least 125 basis points, acrisis may cause the fund to fall below this level. This ma

18、tters because the FDIC would like not only to avoid the possibility of insolvency but also to retain public confidence. To help ensure public confidence, it may be desirable for the FDIC to keep the reserve ratio above a certain minimum ratiofor example, 50 or 75 basis points. These issues are exami

19、ned by constructing a model (the Markov-switching model) that projects future disbursements under certain conditions. The projected disbursements are used to estimate the BIFs likelihood of insolvency as well as the likelihood of the BIF falling below two different minimum reserve ratios. These prob

20、abilities are estimated across a range of assessment rates for a number of different required reserve ratios. (Note that the analysis does not explicitly address what effect recent bank consolidation will have on the solvency of the BIF.)2 Overview of Methodology This paper uses a Monte-Carlo simula

21、tion to analyze the adequacy of the Bank Insurance Fund. I generate sequences of future disbursements and then combine these projected disbursements with a recovery rate and an assessment schedule to track the level of the bank fund over time. Using these simulated disbursement sequences, I quantify

22、 the probability of insolvency for alternative funding schemes. More specifically, I simulate a thousand sequences of disbursements, and the proportion of sequences that exhaust the Bank Insurance Fund determines the probability of insolvency. When a bank fails, cash is paid out of the insurance fun

23、d to cover insured-deposit losses. However, this drain on the fund is offset by the sale of failed-bank assets and by premium income collected from banks. Historically, annual fund disbursements have ranged from 0 basis points (in 1962) to 105 basis points (in 1991), and the recovery rates from the

24、sale of the failed-bank assets have averaged about 65 percent. Assume, for example, a large annual fund disbursement of 100 basis points. With a recovery rate of 65 percent, the sale of failed-bank assets would reduce this large gross disbursement to 35 basis points. Premium income would further off

25、set the gross disbursement. In fact, if banks were assessed an average premium of 23 basis points, the drain on the fund would be reduced to 12 basis points. The simulation approach can be illustrated by drawing on the example above. Consider a bank fund balance at the beginning of period t of 100 b

26、asis points and assume a simulated fund disbursement of 100 basis points, a recovery rate of 65 percent, and an assessment of 23 basis points. At the end of the period, the fund balance would be reduced by 12 basis points. Given this drain on the fund, the fund balance at the beginning of period t +

27、1 would be 88 basis points. After simulating a new fund disbursement for period t +1, one would determine the fund balance for the beginning of period t + 2 in the same way. This process would continue, with the simulation algorithm tracking the level of the insurance fund across a sequence of simul

28、ated disbursements. Using this approach, I can quantify the probability of insolvency (and the probabilities of falling below different minimum reserve ratios) under the current funding arrangementan assessment rate of 23 basis points with a 1.25 percent required reserve ratioas well as under altern

29、ative funding schemes. These probabilities allow me to address in a meaningful way the adequacy of different funding arrangements. I generate sequences of future fund disbursements and use these projected disbursements, along with a recovery rate and an assessment schedule, to track the level of the bank fund over time. I argue that the probability of bank failure is higher during a