Internal and External Credit Enhancements

Because most mortgage-backed securities have higher credit quality collateral than non-mortgage ABS, many non-mortgage ABS offer credit enhancements to make their risk level more tolerable to investors.

Credit enhancement can be internal or external.

Internal Credit Enhancements

  • Cash Reserve Accounts: ABS issuer sets aside a portion of the fees earned in the security underwriting process, so this cash is available to draw upon if necessary.
  • Excess Servicing Spread Accounts: This spread creates an additional basis point buffer beyond the servicing fee between the gross weighted average coupon of the assets and weighted average coupon paid to ABS investors.  This excess spread is deposited into an account and is available to draw upon if necessary.
  • Overcollateralization: The value of the assets supporting an ABS is greater than the outstanding principal owed to bond investors.  Therefore, in the event of a default, excess assets can be used to pay ABS investors.
  • Senior/Subordinate Structure: More than one tranche is issued for the asset pool and in the event of a default; the subordinate tranches absorb losses before the senior tranches do.

External Credit Enhancements

Commonly, external credit enhancements are third party employed measures to back up the internal credit enhancements.

  • For example, bond insurance could be purchased for the asset pool from an insurance company.
  • Keep in mind that if the credit quality of the third party insurer or guarantor declines, then the credit quality of the ABS will decline as well.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.