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Thoughts on Systematic Risk in Financial Systems

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August Jackson
August Jackson

These slides are prepared for a brainstorming session on application of strategic analysis to evaluating systematic risks such as those evident in the financial crisis of 2008.

1 of 7
Surplus Capital Demand for Capital Quest for Liquidity Desire to Hedge Asian Savings Western Real Estate Collateralized Debt Obligations Credit Default Swaps Counter-Party Risk Loose Standards Reliance on Models
Finance Competitive Intelligence and Strategic Analysis Models based on history Models to cope with uncertainty Exclusively quantitative What if? Largely qualitative Models incorporate uncertainty Largely qualitative
Value at Risk (VAR) models fall apart at tail risk. Negatively correlated assets move into correlation.
If the Dow Jones Industrial Average followed a normal distribution, it should have moved by more than 3.4% on 58 days between 1916 and 2003; in fact it did so 1,001 times. It should have moved by more than 4.5% on six days; it did so on 366. It should have moved by more than 7% only once in every 300,000 years; in the 20th century it did so 48 times. Benoit Mandelbrot findings referenced in The Economist , January 22, 2009 Dow is between 1,726% and 144,000% more volatile than normally distributed models would predict.
Boom times create a Cassandra Syndrome though cognitive biases: Loss Aversion and Pseudocertainty : Decision-makers afraid to miss upside Bandwagon effect: Everybody else is doing it Confirmation Bias : Seek out evidence that supports boomtime mood Contrast Effect : Minimize older historic data that contradicts near-term observations Framing and Anchoring : Favoring quantitative models over qualitative analysis Executives chose to filter the thoughtful for the faithful. Contrarians in financial services firms were pushed to self-censorship . The market can stay irrational longer than you can stay in your job. Paraphrasing How to Play Chicken and Lose. The Economist , January 22, 2009.
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Thoughts on Systematic Risk in Financial Systems

  • 1. Surplus Capital Demand for Capital Quest for Liquidity Desire to Hedge Asian Savings Western Real Estate Collateralized Debt Obligations Credit Default Swaps Counter-Party Risk Loose Standards Reliance on Models
  • 2. Finance Competitive Intelligence and Strategic Analysis Models based on history Models to cope with uncertainty Exclusively quantitative What if? Largely qualitative Models incorporate uncertainty Largely qualitative
  • 3. Value at Risk (VAR) models fall apart at tail risk. Negatively correlated assets move into correlation.
  • 4. If the Dow Jones Industrial Average followed a normal distribution, it should have moved by more than 3.4% on 58 days between 1916 and 2003; in fact it did so 1,001 times. It should have moved by more than 4.5% on six days; it did so on 366. It should have moved by more than 7% only once in every 300,000 years; in the 20th century it did so 48 times. Benoit Mandelbrot findings referenced in The Economist , January 22, 2009 Dow is between 1,726% and 144,000% more volatile than normally distributed models would predict.
  • 5. Boom times create a Cassandra Syndrome though cognitive biases: Loss Aversion and Pseudocertainty : Decision-makers afraid to miss upside Bandwagon effect: Everybody else is doing it Confirmation Bias : Seek out evidence that supports boomtime mood Contrast Effect : Minimize older historic data that contradicts near-term observations Framing and Anchoring : Favoring quantitative models over qualitative analysis Executives chose to filter the thoughtful for the faithful. Contrarians in financial services firms were pushed to self-censorship . The market can stay irrational longer than you can stay in your job. Paraphrasing How to Play Chicken and Lose. The Economist , January 22, 2009.
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