• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer
Finance Train

Finance Train

High Quality tutorials for finance, risk, data science

  • Home
  • Data Science
  • CFA® Exam
  • PRM Exam
  • Tutorials
  • Careers
  • Products
  • Login

Understanding Recovery Rates

Risk Management

This lesson is part 4 of 6 in the course Foundations of Credit Risk Modelling

The recovery rates are a crucial element for calculating credit risk.

The loss given default of an asset or a portfolio is calculated as 1 minus the recovery rate.

LGD = 1 – R

The problem with recovery rates is that they are not easy to estimate and the data available for recovery rate is very fragmented and inconsistent.

The recovery rates are closely linked to the bankruptcy procedures which happens after an obligor has defaulted. Based on the procedures, all the creditors of the defaulter are paid a part of their claims based on the priority. For example, the secured creditors will have first claim on the collateral and other assets of the defaulting firm. The unsecured creditors will come after them, and the shareholders will get the last preference.

Another important issue is whether the payment received as recovery should be discounted to the time of default or not. In case of large obligors the payments will generally be discounted back.

The recovery rates can be generally defined in two ways:

  1. Market Value Recovery: This is the market value per unit of legal claim amount, short time (1 to 3m) after the default
  2. Settlement Value Recovery: This refers to the value of the default settlement per unit of legal claim amount, discounted back to the default date and after subtracting legal costs

 

Previous Lesson

‹ Expected Loss, Unexpected Loss, and Loss Distribution

Next Lesson

Factors Affecting Recovery Rates ›

Join Our Facebook Group - Finance, Risk and Data Science

Posts You May Like

How to Improve your Financial Health

CFA® Exam Overview and Guidelines (Updated for 2021)

Changing Themes (Look and Feel) in ggplot2 in R

Coordinates in ggplot2 in R

Facets for ggplot2 Charts in R (Faceting Layer)

Reader Interactions

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Primary Sidebar

In this Course

  • What is Default Risk?
  • Exposure, Default and Recovery Rates
  • Expected Loss, Unexpected Loss, and Loss Distribution
  • Understanding Recovery Rates
  • Factors Affecting Recovery Rates
  • Using Beta Distribution for Estimating Recovery Rates

Latest Tutorials

    • Data Visualization with R
    • Derivatives with R
    • Machine Learning in Finance Using Python
    • Credit Risk Modelling in R
    • Quantitative Trading Strategies in R
    • Financial Time Series Analysis in R
    • VaR Mapping
    • Option Valuation
    • Financial Reporting Standards
    • Fraud
Facebook Group

Membership

Unlock full access to Finance Train and see the entire library of member-only content and resources.

Subscribe

Footer

Recent Posts

  • How to Improve your Financial Health
  • CFA® Exam Overview and Guidelines (Updated for 2021)
  • Changing Themes (Look and Feel) in ggplot2 in R
  • Coordinates in ggplot2 in R
  • Facets for ggplot2 Charts in R (Faceting Layer)

Products

  • Level I Authority for CFA® Exam
  • CFA Level I Practice Questions
  • CFA Level I Mock Exam
  • Level II Question Bank for CFA® Exam
  • PRM Exam 1 Practice Question Bank
  • All Products

Quick Links

  • Privacy Policy
  • Contact Us

CFA Institute does not endorse, promote or warrant the accuracy or quality of Finance Train. CFA® and Chartered Financial Analyst® are registered trademarks owned by CFA Institute.

Copyright © 2021 Finance Train. All rights reserved.

  • About Us
  • Privacy Policy
  • Contact Us