How Icelandic Banks Underplayed their True Riskiness?

Before the financial crisis began, even though all the warning signals were there, the top three Icelandic banks were able to underplay their true riskiness. Moody's had assigned high credit ratings to all these banks.

Moody's ratings consider both quantitative and qualitative factors. Let's take a look at the reasoning behind Moody's ratings.

Quantitative Factors

Moody's quantitative indicators are based on five factors, namely, asset quality, profitability, total capital, liquidity, and efficiency.

When you look at the fundamentals of these Icelandic banks from the perspective of these factors, they look quit favorable. However, that was not really the case.

The asset quality factor was based on loan loss reserves and did not capture the deterioration in the asset quality. Also, the loan loss reserves were inadequate which inflated the profits. The total capital projected was way above the truth because their asset prices were overvalued. It also did not account for weak capital (purchase of their own stock by the bank).  Liquidity was based on the bank's own estimation. It did not even account for the liquidity dry up in the market during the crisis. Partly, there was problem with the rating methodology, and partly it was the over-reliance on the figures supplied by the banks themselves.

Qualitative Factors

Moody's qualitative assessment was based on four factors: franchise value, regulatory environment, operating environment, and risk positioning. The first three factors were assessed with moderate score, while the risk position was given a low score.

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.