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研究生:傅信豪
研究生(外文):Fu, Hsin Hao
論文名稱:關於信用集中度風險的兩篇論述
論文名稱(外文):Two Essays on Credit Concentration Risk
指導教授:江彌修江彌修引用關係
指導教授(外文):Chiang, Mi Hsiu
學位類別:博士
校院名稱:國立政治大學
系所名稱:金融研究所
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:中文
論文頁數:127
中文關鍵詞:房屋抵押貸款證券集中度風險微粒化調整內部信用增強放款投資組合多角化相關性程度銀行績效
外文關鍵詞:mortgage-backed securitiesconcentration riskgranularity adjustmentsinternal credit enhancementsloan portfoliodiversificationdependence structurebank performance
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【第一篇論文中文摘要】
集中度風險於結構式商品的量化與分析:以房屋抵押貸款證券為例
"Martin and Wilde (2002)與Gordy (2003)" 針對巴塞爾協定(Basel Accords)中金融機構之投資組合所內藴之集中度風險提出了相對應的微粒化調整(Granularity Adjustment)風險量化準則,然而該模型僅止於單因子架構下探究單一信用標的集中度風險之量化。本文將其架構延用至結構式商品中,允許債權群組內之信用標的具不同區域別,我們採用Hull and White(2010)之跨池違約相關性描述,並結合Pykhtin (2004)中延拓單因子聯繫模型至多因子之方式,進而求取債權群組之單一資產集中度(Name Concentration)與區域類別集中度(Sector Concentration)風險的量化。本文以房屋抵押貸款證券(Mortgage Backed Securities, MBSs)為例,於集中度風險的考量下,藉由檢視不同風險情境下分券之損失起賠點,重新評估房屋抵押貸款證券AAA投資級分券信用評級之合理性。研究結果顯示,AAA評等之分券高度曝險於系統性風險,且於高風險情境下,標的房貸之區域集中現象擴大了違約相關性對債權群組損失分配的影響,致使AAA分券之損失起賠點得以超過其實際擔保額度(subordination)範圍。

【第二篇論文中文摘要】
美國銀行放款多角化對其報酬與風險之影響:相關性與傳染的觀點
本文目的在於分析銀行放款的多角化行為對其報酬與風險之影響。研究發現納入銀行放款投資組合相關性之考量,亦即標的資產之相關性結構以及資產間因契約關係所隱含跨投資組合之傳染途徑,將降低多角化之成效。文中透過因子模型(factor model)建構資產之報酬,同時決定其相關性結構,其中將資產間殘差項相關性作為傳染指標,進一步分析投資組合內標的資產間的平均相關係數、傳染與多角化程度間的關聯性。我們以美國銀行作為研究樣本,分別以赫芬達-赫希曼指數估算投資組合權重分配之集中度、使用組合內標的產業股票報酬資訊來計算投資組合內相關程度,接著利用標的產業與投資組合外產業間的殘差相關性來捕捉產業傳染效果,將此三項指標作為衡量多角化指標,分析其在1987年至2014年間聯貸投資組合多角化情形並試圖分析放款多角化對銀行績效之影響。透過契約關係的界定進而探討顧客傳染如何影響銀行績效。
研究發現於市場處於平穩期間(tranquil period),所有多角化指標銀行放款均呈現放款多角化程度越高越有助於提高銀行的報酬並降低其風險。然而於危機期間(turmoil period),銀行應將放款權重集中於部分產業、建構相關性較低之組合或選擇較低之傳染效果之產業作為放款的對象,用以提高銀行績效。隱含在危機期間銀行應該選擇適度之多角化策略,若僅以赫芬達-赫希曼指數作為多角化之衡量將顯示危機期間越集中越有助於銀行的表現,此舉將造成解釋上的偏誤。說明於投資組合多角化的衡量上,不該忽略由相關性結構所引發之集中度風險。

【Essay I】
Quantification and Analysis of Concentration Risk in Structured Products: the Case of Mortgage Backed Securities
Granularity adjustments, introduced by Martin and While (2002) and Gordy (2003), allow one to quantify the concentration exposures of credit portfolios due to imperfect diversification. However, they focus solely on name concentrations under an Asymptotic Single Risk Factor (ASRF) framework. In this study, by adapting the multi-pool correlation structure of Hull and White (2010) under the multi-factor setting of Pykhtin (2004), we derive quantitative measures of name and sector concentration that facilitate subsequent analysis of the risk profiles embedded in Mortgage Backed Securities (MBSs). Under different stress scenarios, we examine the impacts of concentration exposures on the internal credit enhancements, in particular, the AAA tranche attachment points. We show that, under severe market conditions, the presence of sector concentrations in the underlying mortgage pools can further amplify the effects of default correlation on the portfolio loss distributions. As a direct consequence, the predetermined subordination level determined by the assignment of tranche attachment points can be exceeded.

【Essay II】
How Loan Portfolio Diversification Affects U.S. Banks’ Return and Risk: Correlation and Contagion Perspectives.
In this paper we investigate how loan portfolio diversification affects the banks’ return and risk. We argue that, the dependence structure of bank loan portfolios, namely, the correlation structure among loan assets and the presence of contagion channels due to contractual relationships across the border of portfolio, contributes to the costs of diversification. Under the factor model framework, we derive a theoretical model to depict the asset returns and their dependence structure. Based on data of US bank loans collected from 1987-2014, our empirical study employs HHI, intra-portfolio correlation, and contagion as proxies for diversification to examine how loan portfolio diversification affects the banks’ profitability and riskiness. In addition, contractual relationships are identified and we investigate how customer contagion affects the bank’s performance. We find that all diversification measures exhibit a positive effect on the performance of U.S. banks during tranquil periods. However, for turmoil periods, banks with loan portfolios of more concentrated weight distributions, lower intra-portfolio correlation, or lower consumer contagion effects would have improved returns and reduced risk. In other words, during crisis, banks should choose an appropriate concentration strategy rather than focus on selected industries as determined solely by the HHI.

Introduction 1
【Essay I】
Quantification and Analysis of Concentration Risk in Structured Products: The Case of Mortgage Backed Securities 7
1 Introduction 7
2 Internal Credit Enhancement (Subordination) 12
2.1 Modelling the dependence among mortgages 12
2.2 Structured finance 14
2.3 Estimation of subordination level 16
3 Measuring Concentration Risk 17
3.1 The granularity assumption 17
3.2 Quantification of concentration risk 19
3.3 The asymptotic limit of attachment point 24
4 Numerical results and analytics 28
4.1 Assessing the risk characteristics of tranches of MBSs 28
4.1.1 Contents of commodity contract 28
4.1.2 Characterizing tranche risk profile 30
4.2 Assessing the concentration risk in tranches of MBSs 36
4.2.1 The analysis of concentration risk arise from exposure distribution across names and regions 37
4.2.2 The analysis of concentration risk arise from dependence structure 39
4.2.3 The analysis of region concentration risk 47
4.2.4 Comparing the concentration effect induced from distribution of exposures among names and regions and dependence structures 50
5 Conclusion 52

【Essay II】
How Loan Portfolio Diversification Affects U.S. Banks’ Return and Risk: Correlation and Contagion Perspectives. 61
1 Introduction 61
2 The Models 67
2.1 A factor model with sector-specific latent factor 67
2.2 The ingredient of the extent of diversification 69
2.2.1 Return dispersion of each underlying sector to the extent of diversification 70
2.2.2 Portfolio correlation to the extent of diversification 71
2.3 Research hypotheses 77
3 Empirical Examinations 79
3.1 Description of Variables 81
3.1.1. Measuring Loan Diversification 82
3.1.2. Measuring Outer Overlap Ratio ("Outer Overlap Ratio" ) 85
3.1.3. Control variables 86
3.2 Data Summary and Univariate Results 86
4 Empirical Result 90
4.1 The relationship between bank return and loan portfolio diversification 90
4.2 The relationship between bank return and the customer contagion effect 93
4.3 The role of outer overlap ratio on customer contagion effect on bank return 95
4.4 The relationship between bank risk and loan portfolio diversification 97
4.5 The relationship between bank risk and the customer contagion effect 99
4.6 The role of outer overlap ratio on customer contagion effect on bank risk 101
5 Conclusion 103

【Essay I】References
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