Technische Universität München Credit as an Asset Class - risklab

More documents

Recommendations

Info

LIST OF FIGURES 7.45 Results of score optimisation, 3 years investment horizon with Gaussian defaults (left-hand side) and NIG defaults (right-hand side) . . . . . . . . . 171 7.46 Results of score optimisation, 5 years investment horizon with Gaussian defaults (left-hand side) and NIG defaults (right-hand side) . . . . . . . . . 172 7.47 Results for risk-averse and risk-affine investor, 1 year investment horizon, Gaussian defaults (top) and NIG defaults (bottom) . . . . . . . . . . . . . . 173 7.48 Results for risk-averse and risk-affine investor, 3 years investment horizon, Gaussian defaults (top) and NIG defaults (bottom) . . . . . . . . . . . . . . 174 7.49 Results for risk-averse and risk-affine investor, 5 years investment horizon, Gaussian defaults (top) and NIG defaults (bottom) . . . . . . . . . . . . . . 174 ix
List of Tables 2.1 Risks in credit derivatives markets . . . . . . . . . . . . . . . . . . . . . . . 16 2.2 Market shares of main protection buyers and sellers . . . . . . . . . . . . . 20 2.3 Risks in securitisation markets . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.1 iTraxx Europe by industrial sectors . . . . . . . . . . . . . . . . . . . . . . . 52 3.2 Issuance of iTraxx Europe series . . . . . . . . . . . . . . . . . . . . . . . . 55 3.3 Fixed spreads and recovery rates of iTraxx Europe series 6 . . . . . . . . . . 55 3.4 DJ CDX Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.1 US Lehman aggregates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4.2 Jarque-Bera test for US Lehman aggregates . . . . . . . . . . . . . . . . . . 63 4.3 Jarque-Bera test for US Lehman aggregates (log returns) . . . . . . . . . . 63 4.4 Euro-Lehman aggregates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.5 Jarque-Bera test for Euro-Lehman aggregates . . . . . . . . . . . . . . . . . 65 4.6 Jarque-Bera test for Euro-Lehman aggregates (log returns) . . . . . . . . . 65 5.1 US average one-year probability of default, 1981 to 2005 . . . . . . . . . . . 77 5.2 US average one-year transition rates, 1981 to 2005 . . . . . . . . . . . . . . 77 5.3 Log-expansion of M . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 7.1 Parameter estimation for processes r and ω . . . . . . . . . . . . . . . . . . 115 7.2 Parameter estimation for processes s and u for rating classes AA, A2, BBB1116 7.3 Composition of US CDS index . . . . . . . . . . . . . . . . . . . . . . . . . 116 7.4 Parameter estimation for process i . . . . . . . . . . . . . . . . . . . . . . . 117 7.5 Parameters for US equity process . . . . . . . . . . . . . . . . . . . . . . . . 117 7.6 Nelson-Siegel parameters for US zero rates . . . . . . . . . . . . . . . . . . . 119 7.7 Parameters of NIG distribution for US economy . . . . . . . . . . . . . . . . 119 7.8 Characterisation of representative investors . . . . . . . . . . . . . . . . . . 144 B.1 Standard & Poor’s credit ratings . . . . . . . . . . . . . . . . . . . . . . . . 184 D.1 European Union average one-year transition rates, 1981 to 2005 . . . . . . . 187 x
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: LIST OF FIGURES 7.22 Results of sco
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 and 22: CHAPTER 2. CREDIT RISK TRANSFER •
Page 23 and 24: 2.3.2 Classification according to R
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 61 and 62:
CHAPTER 3. CREDIT RISK TRANSFER INS
Page 63 and 64:
Page 65 and 66:
Page 67 and 68:
Page 69 and 70:
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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?