On September 18, 2007 the US Federal Reserve cut the Federal Funds rate by half a percent in response to the looming sub-prime loan scandal. The markets had lost confidence and Banks were holding debt they could not sell. Write offs ensued, and the market forecast looked questionable at best.
At the time, this rate cut was seen as a dramatic response to worsening market conditions and proof that the Fed would act aggressively to protect the economy from the housing bubble. In the next two months, the Fed intervened again to cut rates .25% in October and .25% again in December. Each rate cut was seen as a prudent response to market conditions.
In January 2008, just a few weeks after the last rate cut, the Fed had to intervene again with a very sudden 1.25% cumulative rate cut to stem an asian-driven equity market sell-off following more sub-prime write offs and loss disclosures. In just five months, the Federal Reserve had to intervene five times with a combined interest rate cut of 2.25% following 17 quarter point rate increases in as many months.
This was an incredible see-saw of macro-economic policy - gradual rate increases were followed immediately by sudden rate cuts. In hindsight, the half-point cut in September 2007 was not very dramatic in comparison to the 1.75% in cuts that followed in the next four months. No one then could have foreseen the volatility in the markets that was to come, or could they have?
Why is it that the US Federal Reserve rate policy was reactive to market volatility? Why didn't their monetary policy, which had run up rates from 1% in June 2003 to 5.25% in June 2006, anticipate the looming housing bubble and bank losses that would surely ensue? Hadn't Alan Greenspan warned of this outcome in 2005? Didn't we all know the housing joyride would end at some point?
Today, we can see banking and financial market data that shows the risk trends in our rear view mirror. Unfortunately, no one has a mirror that forecasts the future, but they could if capitalized risk data were collected on a systemic basis by banks and shared with the Federal Reserve. The Federal Reserve does an excellent job of studying catastrophic risks and running sophisticated macroeconomic loss models on everything from terrorist attacks to coastal hurricanes. The Fed uses this catastrophic loss data to provide capitalize insurance loss reserves for the US economy - ie, they print more money when very bad things happen.
The insurance reserves got tapped after 9/11 and hurricane Katrina, when the Fed injected huge amounts of liquidity into the economy to stabilize markets and restore confidence. Of course, the timing of catastrophic events can't be forecasted, but the monetary response can be estimated based on a variety of risk factors. the fed constantly analyzes and wargames these risk factors and the success of Fed liquidity and monetary responses to 9/11 and Katrina attest to the diligence of their planning and the value of risk-based forecasting models.
What does this have to do with the sub-prime loan meltdown you ask? Well, if the Fed had non-catastrophic risk-data forecasting models they could possibly pre-empt loss events with macroeconomic policy tools that could even out some of the worst aspects of the business cycle. Unfortunately, that kind of non-catastrophic risk-data has to come from banks, who until recently were totally incapable of providing that kind of data, let alone using it themselves for their own risk-based policy-making.
That's changing. In the last two years banks around the world have been working to assess and collateralize market, credit, and operational risks as part of the Basel II compliance process. That data isn't normalized across banks, and there are wide disparities in how risks are assessed, calculated, and capitalized from bank to bank, country to country. But the raw data, and the beginnings of the knowhow are, for the first time in history, there. And that data and knowhow can be leveraged to provide new macroeconomic tools for Central Bank policymakers around the world.
What's needed are standards in risk assessment, classification, calculation, and the reporting of capitalized risk data from US banks to the Federal Reserve. This may take some years yet to accomplish but the time is right to begin discussing these issues. As US Banks reach Basel II compliance they will be in a position to leverage risk-data for their own self-insurance against non-catastrophic losses, and if they would be willing to share their capitalize risk data they could help the Federal Reserve to reduce market volatility and improve macroeconomic performance for everyone.
Here's a case where regulatory compliance really can improve business performance.[Read More]
Adler on Data Governance
DataGovernor 120000GKJR 1,187 Visits