The Asian Financial Crisis

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The Asian Financial Crisis
Pitfalls and Possibilities of Predictive Models
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Prepared by
Arjay Jensen Chip Krotee Hilde Larssen John
Mack Gerald Nessmann
February 24, 2000
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Mapping the Crisis
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Objectives and Hypotheses

• Goals
• Build predictive models using data up to the
  onset of the Asian crisis
• Analyze model performance out-of-sample during
  the crisis
• Expectations
• ( - ) Models would likely not accurately predict
  crisis
• ( ) Exercise would be useful in generating
  insight into alternative means of predicting such
  economic events

Question What might have worked better, and why?
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Analysis of Pre-crisis Predictive Models
In-sample
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Performance of Predictive Models
In-sample
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Performance of Predictive Models
Out-of-sample
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Increasing Predictive Accuracy

• Method
• Build models including out-of-sample data
• compare these models to models based on
  in-sample data
• use observations to construct a thought framework

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Pre- vs. Post-Crisis Models
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Increasing Predictive Accuracy

• Hypotheses
• There exist variables which can and do
  accurately predict
• economic crisis
• Data underlying these variables are usually hard
  to obtain due to
• government disclosure restrictions
• scale issues with data aggregation
• disincentive to disclose proprietary information
  (intellectual property)
• George Soros is not the typical investor.
• His models arent the typical models.
• He has data that you dont have.
• His data consists of key driver variables,
  highly correlated with
• macro-economic events

If you have an excellent model, dont give it
away!
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Conclusions
Market forecasts using standard
methodologies generally failed to predict the
Asian financial crisis
Predictive models using key driver variables
can have far better success at predicting such
crises
It is generally difficult to obtain these
variables, but it may be possible to derive them
indirectly