1. The Asian Financial Crisis
Pitfalls and Possibilities of Predictive Models

2. Prepared by: February 24, 2000 Arjay Jensen Chip Krotee Hilde Larssen
John Mack
Gerald Nessmann
February 24, 2000

3. Mapping the Crisis

4. 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?

5. Analysis of Pre-crisis Predictive Models
In-sample

6. Performance of Predictive Models
In-sample

7. Performance of Predictive Models
Out-of-sample

8. 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

9. Pre- vs. Post-Crisis Models

10. 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 aren’t the typical models.
He has data that you don’t have.
His data consists of “key driver” variables, highly correlated with
macro-economic events
If you have an excellent model, don’t give it away!

11. 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