Beware the Ides of GARCH?

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Published on March 5, 2018

| 2 min read

Kevin Armstrong, CFA, Managing Director, Consultant Relations


Julius Caesar didn’t heed his warning to stay home in Shakespeare’s tragedy only to be violently murdered. Can GARCH methods be the soothsayer of market volatility? Can they tell us when to stay home?

Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) methodologies are a more sophisticated approach to monitoring volatility. Popular among academics, GARCH methods attempt to model not just the returns, but also the evolution of the underlying volatility that characterizes them. They can provide some insight to market volatility, but industry practitioners should be aware of a couple of issues when using GARCH methods to monitor volatility.

First, even though GARCH methods are a reasonable starting point for understanding aftershocks after an initial market dislocation, they do not help to determine when a major shock will be more likely to occur.

Second, GARCH models are not well-suited for identifying or modeling regime changes, because the underlying model is challenged during the transition period between any two risk regimes.

Like GARCH models, most conventional approaches to understanding market risk have more value in establishing the rate at which prices fluctuate at the present or during the recent past, rather than when a major market shock is likely to occur.

That’s why we’ve introduced the Intech Equity Market Stress Monitor®.  The monitor is a collection of five metrics we believe are reliable indicators of equity market stress based on our 30-year history of studying stock price volatility. We believe these indicators are better suited to identifying signs of market stress. You can use the monitor to gain insight to market risk regimes, contextualize beta risk management and complement your conventional risk metrics.

Learn more about the Intech Equity Market Stress Monitor®. Download an eBook that serves as a guide to the monitor or watch our EMSM webinar.

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