Technische Universität München Credit as an Asset Class - risklab

More documents

Recommendations

Info

CHAPTER 2. CREDIT RISK TRANSFER of the underlying financial assets (or instruments) at time T (within the time interval [0, T ]).” 18 [Das06] defines credit derivatives as a class of financial instrument, whose value ”is derived from an underlying market value driven by the credit risk of private or government entities other than the counterparties to the credit derivative transaction itself.” 19 A credit derivative is a bilateral financial contract which allows to separate and isolate credit risk related to a credit-sensitive underlying asset, and thus facilitates the trading of credit risk. This leads to a greater efficiency in the pricing and distribution of credit risk among market participants, who are now able to better manage it by hedging, transferring or replicating it. Figure 2.1 shows the basic structure of a credit derivative. Figure 2.1: Basic structure of a credit derivative contract During the term of the credit derivative contract, the protection buyer or risk seller makes one or several premium payments to the protection seller or risk buyer. In return, the latter has to make a conditional payment to the protection buyer if a credit event occurs during the lifetime of the derivative. A credit event is characterised with respect to a reference entity and the reference obligations, which can be any financial instrument issued by the reference entity and exposed to credit risk, such as a loan, a bond or a portfolio of loans or bonds. The possible credit events might include bankruptcy, deterioration of credit quality or changes of the credit spread. The contract terminates either at maturity or if a credit event has occurred. Apart from the conditional payment, a credit derivative can oblige the protection seller to other payments. For simplicity reasons, we will use the terms reference entity and reference obligation synonymously, as most authors also do not differentiate between the terms. 18 See [BK04], p. 2. 19 See [Das06], p. 669. 11
2.3.2 Classification according to Risk Category CHAPTER 2. CREDIT RISK TRANSFER Credit derivative contracts can be classified according to different risks they cover. Figure 2.2 gives an overview. Figure 2.2: Risks covered by credit derivatives There are credit derivatives that are solely linked to default events. The default risk can be covered by default products, such as credit default swaps, credit default options, basket default swaps, n th -to-default swaps, etc.. Moreover, there are derivatives that are sensitive towards changes in credit quality. The credit spread risk can be covered by spread products, such as credit spread swaps or options. Finally, there are credit derivatives that transfer the total risk related to a bond. This can be done by means of total return derivatives like total return swaps. All these instruments will be explained in detail in Chapter 3. We want to remark that – due to the complexity and variety of credit derivative products – it is not possible to give a complete classification. One can also think of another classification scheme. Credit derivatives can be grouped into single-name and multi-name instruments. Single-name credit derivatives do only have one reference entity, while multi-name credit derivatives involve protection against credit events in a pool of reference entities, such as a portfolio of bonds or loans. 2.3.3 Reasons for the Utilisation of Credit Derivatives In this section, we will point out the reasons for the utilisation of credit derivatives. At first, we present the specific reasons for financial institutions to use credit derivatives, since these institutions build the foundation for their development and the enormous growth of the market. Then, we will explain general reasons for investors to participate in the credit derivatives market both as protection buyer and as protection seller. Specific motivations for Financial Institutions to operate in the Credit Deriv- atives Market As explained in Section 2.1.2, credit risk management is a vital task for banks. An active credit risk management has to be in line with an overall risk management strategy. It 12
Page 1 and 2: Technische Universität München Ze
Page 3 and 4: Danksagungen Zuallererst möchte ic
Page 5 and 6: CONTENTS 3.8.2 Synthetic CDO (Synth
Page 7 and 8: List of Figures 2.1 Basic structure
Page 9 and 10: LIST OF FIGURES 7.22 Results of sco
Page 11 and 12: List of Tables 2.1 Risks in credit
Page 13 and 14: CHAPTER 1. INTRODUCTION based simul
Page 15 and 16: Chapter 2 Credit Risk Transfer To o
Page 17 and 18: CHAPTER 2. CREDIT RISK TRANSFER ins
Page 19 and 20: CHAPTER 2. CREDIT RISK TRANSFER bon
Page 21: CHAPTER 2. CREDIT RISK TRANSFER •
Page 25 and 26: CHAPTER 2. CREDIT RISK TRANSFER •
Page 27 and 28: CHAPTER 2. CREDIT RISK TRANSFER Ris
Page 29 and 30: CHAPTER 2. CREDIT RISK TRANSFER exa
Page 31 and 32: CHAPTER 2. CREDIT RISK TRANSFER Eur
Page 33 and 34: CHAPTER 2. CREDIT RISK TRANSFER By
Page 35 and 36: CHAPTER 2. CREDIT RISK TRANSFER Mor
Page 37 and 38: CHAPTER 2. CREDIT RISK TRANSFER The
Page 43 and 44: CHAPTER 2. CREDIT RISK TRANSFER Fig
Page 45 and 46: in detail. CHAPTER 2. CREDIT RISK T
Page 47 and 48: CHAPTER 3. CREDIT RISK TRANSFER INS
Page 53 and 54: Product Variations CHAPTER 3. CREDI
Page 71 and 72: Definition 4.1 (Simple Return) CHAP
Page 73 and 74:
Figure 4.2: Histogram of US aggrega
Page 75 and 76:
CHAPTER 4. DATA ANALYSIS Aggregate
Page 77 and 78:
4.4 Summary CHAPTER 4. DATA ANALYSI
Page 79 and 80:
Definition 5.1 (Interest-Rate) CHAP
Page 81 and 82:
CHAPTER 5. SIMULATION AND PRICING F
Page 83 and 84:
Page 85 and 86:
with and Proof: A d (t, T ) = D d (
Page 87 and 88:
Page 89 and 90:
Theorem 5.12 (Log-Expansion) CHAPTE
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
Page 97 and 98:
and CHAPTER 5. SIMULATION AND PRICI
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
Page 107 and 108:
5.5.4 Floating Rate Notes CHAPTER 5
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
epeat the adjusted formulas here in
Page 115 and 116:
Page 117 and 118:
Chapter 6 Portfolio Selection After
Page 119 and 120:
CHAPTER 6. PORTFOLIO SELECTION µ(x
Page 121 and 122:
above definition of the VaR can als
Page 123 and 124:
L� −∞ FR(r)dr = L� −∞ C
Page 125 and 126:
Chapter 7 Simulation and Simulation
Page 127 and 128:
CHAPTER 7. SIMULATION AND SIMULATIO
Page 129 and 130:
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
7.1.6 Conditional Value at Risk CHA
Page 151 and 152:
Page 153 and 154:
Page 155 and 156:
7.1.7 Score CHAPTER 7. SIMULATION A
Page 157 and 158:
Page 159 and 160:
7.1.8 Comparison of Optimal Portfol
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
Page 171 and 172:
7.2.2 Mean-Variance Approach CHAPTE
Page 173 and 174:
Page 175 and 176:
Page 177 and 178:
Page 179 and 180:
Page 181 and 182:
7.2.4 Score CHAPTER 7. SIMULATION A
Page 183 and 184:
Page 185 and 186:
Page 187 and 188:
CHAPTER 8. SUMMARY AND OUTLOOK fact
Page 189 and 190:
Appendix A Mathematical Preliminari
Page 191 and 192:
APPENDIX A. MATHEMATICAL PRELIMINAR
Page 193 and 194:
APPENDIX A. MATHEMATICAL PRELIMINAR
Page 195 and 196:
Appendix B Ratings Rating Descripti
Page 197 and 198:
APPENDIX C. CALCULATION OF THE DYNA
Page 199 and 200:
Appendix E List of Abbreviations AB
Page 201 and 202:
BIBLIOGRAPHY [Das06] Das, S. (2006)
Page 203:
BIBLIOGRAPHY [NS87] Nelson, C.R., S
show all

Technische Universität München Credit as an Asset Class - risklab

Create successful ePaper yourself

Delete template?

Save as template?