Table of Contents
PRODUCT EVOLUTION – A PERIOD OF RAPID CHANGE
CHAPTER 01
MARKET FOUNDATIONS
Product overview: structure, functionality and usage
Significant recent events
THE CREDIT DEFAULT SWAP
Successor issues
Impact of LBO activity
A new form of cash settlement
Auction mechanism
Basis risk – when the market standard contract does not perform as advertised
Credit event basis risk
DEVELOPMENTS IN THE EUROPEAN INDEX MARKETS
Overview
Futures contract
Future pricing following a credit event
The reduced pool credit index future
User benefits
CHAPTER 04
TRANCHING CREDIT RISK: CDOs, TRANCHED INDICES AND OTHER BASKET PRODUCTS
Background
The evolution of the single tranche market
Delta hedging
Understanding correlation
Investor views of correlation
Tranched indices
Substitution mechanics on managed deals
Shorting
Rating agencies and tranche
investing
S&P and Moody’s divergent rating methods
Tranche thickness is key
The use of surveillance models
Surveillance via SROC
Trends in spreads and correlation
Market dislocation
Leveraged super senior
Product innovations
CDO developments
CPPI structures
CPDO structures
Future prospects
Widespread interest in the CDPC model
CDPC capital models
FRONT-OFFICE CHALLENGES AND OBSTACLES TO FUTURE CHANGE
Suitability of users, usage and products
The growing challenge facing the salesforce
Product robustness
Bank cultural and organisational challenges
WHAT HAS TECHNOLOGY DONE FOR CREDIT DERIVATIVES?
The distant past
A maturing market
New opportunities
The current situation
CHAPTER 07
OPERATIONS INFRASTRUCTURE
Regulatory concerns
A creaking infrastructure
Investment and control problems create confirmations bottleneck
08
DEALING WITH FUTURE CREDIT EVENTS
Background
The 2005 defaults
Creating a settlement protocol
The move to cash settlement
Initial use of net physical settlement
Outlook
TECHNOLOGYOBJECTIVES
Front-office expectations
The power of the spreadsheet
Development of dedicated tools
Bloomberg
Objectives for the middle and back office
Data management
Challenges to technology
Processing
CHAPTER 10
THE FUTURE POTENTIAL FOR TECHNOLOGY TO INFLUENCE CREDIT DERIVATIVES TRADING
The technology tools for credit derivatives growth
How will technology change credit derivatives trading?
Electronic trading will prevail
Credit events will no longer be a process bottleneck
Glossary of terms
Relevant links
Companies
Organisations
QUANTITATIVE CREDIT
CHAPTER 11
SINGLE NAME RISK AND BASIC CDO ISSUES
The model
Mathematical description
General description
Valuing a CDS or insurance contract
Calibration
Bootstrapping the hazard rate/survival probability curve
Interpolating between calibration dates
Missing elements
Spread volatility – the delta trap
CHAPTER 12
PORTFOLIO PRODUCTS AND MODELS
Single tranche CDO and cash flow CDO
The Normal Copula model and default time correlation
Additional modelling components for full capital structure products
Calculating default-time correlation
Correlation naming
Hedging the correlation book: avoiding the delta trap
Correlation and CDO2
Negative exposures
Stochastic recovery
The Normal Copula model and default time correlation revisited
CHAPTER 13
LOANS, MORTGAGES AND EXOTIC REFERENCE POOLS
Examples of reference assets
Life insurance contracts
MBS
Lease or rental contracts
Loans
Valuation of individual reference assets and data issues
Life insurance contracts
MBS
Lease and rental contracts
Loans
Covenants
Data risks and stresses
Distribution risk
Correlation
Aggregated pools – simulation and correlation impact
Other elements
Rating
Reserves/economic capital
T3.1: iTraxx credit index futures – contract specifications
T3.2: OTC versus Eurex
F3.1: Credit default swap future
T3.3: OTC versus Eurex – Example credit event
T4.1: Sample tranched index quotes
F4.1: Typical CPPI structure
F4.2: Typical CPDO structure
F6.1: Credit derivatives growth, 2001–06 (US$trn, %)
F8.1: Global corporate defaults, 2001–06 (US$bn)
T8.1: CDS (index) protocol adherence, June 2005–Nov 2006
T10.1: Key technology solutions in the credit derivatives market
F11.1: CDS premiums and implied hazard rates where piecewise constant and piecewise linear calibration is assumed, Feb 2006–July 2022
T12.1: Tranche value differences – without and with stochastic recovery
T12.2: Impact on the probabilities of zero loss and total loss
T13.1: Mortality rate per thousand, by age attained and select period (MNS)
T13.2: Value and time statistics for first gain piece and the balance of a 250 name 300m notional structured life
pool
F13.1: Correlation to produce the correct premium for a 1,600 name pool which is modelled as a bucketed pool
Please note: These contents were correct prior to publication of the report in July 2007 but are subject to change.
