Cash Flow Yield, Nominal Spread, and Zero Volatility Spread for ABS/MBS

Cash Flow Yield

Unlike bonds, cash flows for MBS/ABS investments are commonly monthly.  The cash flows will be made up of interest, scheduled principal repayments, and principal prepayments.  In order for the cash flow expectations to approximate reality, prepayment assumptions must be made.

r bond equiv. yield = 2[(1 + i monthly)6 – 1]

An investor actually realizing the cash flow yield return depends upon the following taking place:

  • Cash flows are re-invested every month at the cash flow yield;
  • Prepayments equal the assumed prepayment schedule;
  • The investor holds the ABS/MBS until it is retired.

Nominal Spread

Compares the cash flow yield of the ABS/MBS to the yield to maturity of a Treasury security with a maturity equal to the average life of the ABS/MBS.

Nominal Spread = CF Yield – YTM of comparable Treasury

The nominal spread may be a flawed measure of comparison as the spread may not reflect risk premiums unique to the ABS/MBS, such as prepayment risk.

Zero-Volatility Spread (aka Z-spread or static spread)

The Z-spread is the spread that an ABS/MBS investor will receive over the complete Treasury spot rate curve, if the security is held to maturity.

The Z-spread will be closer to the nominal spread when: the ABS/MBS has a short average life and/or when the yield curve is relatively flat.

The Z-spread assumes that the timing and amount of principal payments are known, regardless of future interest rate path changes.  In other words, the Z-spread assumes interest rates are constant, so prepayments are known.

Given the material prepayment risk inherent in ABS/MBS, the Z-spread may not be appropriate for relative valuation analysis.

Option Adjusted Spread (OAS)

Since the OAS adjusts for option risk, it is a good measure of relative risk after adjusting for the impact of prepayments.

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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.