Issues in Estimating Cost of Debt

We discussed that the cost of debt for a company can be measured using the YTM or Debt-Rating approach.

An analyst faces certain issues while estimating the cost of debt. These issues are discussed below:

Floating/Fixed Rate Debt: In our previous article, our calculations assumed a fixed rate for debt. However, in reality, a firm may also issue floating rate debt. For such debt, the rate will be linked to a benchmark such as LIBOR, and will fluctuate periodically. This makes it difficult to estimate the cost of debt. The analyst may use the term structure while estimating the cost of debt.

Debt with Embedded Options:  Companies don’t always issue vanilla debt products.  There are also debt products that have features such as call option embedded in them. This makes their payoff non-linear. For example, the yield of a callable bond is higher than a non-callable bond. These features make estimating the cost of debt difficult for the analyst. If the company’s current debt already incorporates such features, then the analyst may use the current YTM. However, if the company is planning to introduce option-embedded debt, the analyst will have to adjust the YTM to reflect the value of these options.

Nonrated debt: Another problem is that the may not be able to use similarly rated debt securities. This could be because there is no existing debt rating for the company. In such a case an analyst may arrive at synthetic ratings using financial ratios, but this will not be accurate.

Leases: A lease, whether operating lease or capital, is considered as an alternative form of borrowing, even though only capital leases are shown on a company’s balance sheet. The cost of such lease should be included in the cost of debt. An analyst can use the company's long-term borrowing rate for estimating the cost of capital for leasing.

Related Downloads

Related Quizzes

Cost of Capital

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 includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $39 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

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.