Do We Often Find ARCH Because Of Neglected Outliers?
January 1997
Research Paper
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In this paper we test for (Generalized) AutoRegressive Conditional Heteroskedasticity [(G)ARCH] in daily and weekly data on 22 exchange rates and 13 stock market indices using the standard Lagrange Multiplier [LM] test for GARCH and a new LM test that is resistant to additive outliers. The data span two samples of 5 years ranging from 1986 to 1995. Our main result is that we find spurious GARCH in over 50% of the cases. Using Monte Carlo simulations, in which we evaluate our empirical method, we show that this general finding indeed appears to be due to outliers. We discuss some of the implications of our findings for empirical financial modeling.
Keywords
Automatically Extracted Terms
- lm test
- series
- outlier
- result
- table
- lm tests
- sample
- stock
- model
- additive outliers
- evidence
- statistic
- exchange
- section
- sample size t
- observation
- test statistics
- market
- level
- garch
