Early Warning Indicators in Icelandic Financial Crisis

Like in every financial crisis, even the Icelandic Financial Crisis provided many early warning indicators and cues, which if acted upon at the right time could have avoided or atleast reduced the extent of the losses.

Let’s look at the key early warning indicators:

1. Size of the balance sheets compared to the size of the economy.

The rate at which the balance sheets of the top banks in Iceland grew was actually worrying. In 2003, the banks assets were about 100% of GDP. However, by March 2008, the assets had grown to 100% of the country’s GDP.

The growth was attributed to organic growth as well as acquisitions. Both these sources can expose the institutions to different risks. In acquisition, the risk is that you over-pay for the assets. In organic growth, the risk is that asset quality suffers because of poor underwriting and lower lending standards.

Compared to its Nordic peers, the Icelandic banks were actually earning more than their overseas competitors.    Since the Icelandic banks relied heavily on more expensive wholesale funding, its high earnings were achieved at higher costs.

The two more related warning signals were: very low reported loan problems and a growing reliance on shares to collateralize their loans.

2. Private sector credit boom

There was a rapid expansion of credit to the private sector, which also contributed to the expansion of banks. This is an indication of deteriorating asset quality.

 3. Rise in Stock Prices and Housing Prices

With the expansion of credit to private sector, the funds were invested in the stock markets and real estate.

This leveraged stock purchase lead to the rise in stock markets. The benchmark stock index rose 500% from 2003 to 2007.

Similarly, the real estate prices rose by 80%.

The risk with such highly leveraged buying is then when the markets return to normal, the asset values will become negative, the value of collateral will fall because people will be forced to sell their assets in distress conditions.

4. Appreciation in real-exchange rate

Even though in the year 2008, the value of krona declined significantly, in the years leading to the crisis, the real exchange rate appreciated. As the real-exchange rate of krona appreciated, their domestic businesses became incompetent.

5. Persistent Current Account Deficit

The increased consumption and strong currency contributed to the increased demand for foreign goods.

The current account deficit for Iceland was about 20% of GDP. Their net external debt was the second highest in world.

For NIIP, the net international investment position, for Iceland was -131% of GDP. NIIP = total external assets - total external liabilities).

As per macroeconomic theory, if a country has large negative NIIP, it will experience currency depreciation, so that it can generate trade surplus to pay interest on external liabilities. For Iceland, if currency depreciated, it would increase the cost of servicing debt that held loans indexed to foreign currency.

6. High Short-term External Debt

Iceland had very high short-term external debt. It includes deposits in foreign branches and loans. Since the bank has to rollover the short-term debt, it exposes banks to funding risk.

7. Mini-crisis in early 2006

Iceland also faces a min-crisis in the early 2006, which was triggered by two reports in February, 2006 by Fitch. This should have been seen as an early warning signal about the stability of the banking system.

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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.