How to predict a stock market bubble in real-time

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The method, co-created by Robert JarrowRonald P. & Susan E. Lynch Professor of Investment Management at Cornell University, diverges from traditional ways of predicting bubbles that entail sensitivity to model choices and require time series data of asset prices. Instead, it relies on cross-sectional option price data.

“It’s simple,” Dr. Kwok said. “First, get the sale prices of the stock option contracts at various strike prices. They are useful for uncovering the distribution of discounted future stock prices. Then calculate the fundamental stock value as the average of discounted future stock prices. Subtract this from the observed spot (current) stock prices to obtain bubble estimates.”

Together with Professor Robert Jarrow from Cornell University, he examined daily prices of European call and put options written on the S&P 500 index spanning 1996 to 2015 and found strong evidence of the existence of bubbles. Their sizes were economically significant, taking up more than three percent of the index during 2006-2007, just before the start of the 2008 Global Financial Crisis.

“Big financial bubbles are a cause of concern because they are unsustainable and, once they burst, can lead to catastrophic loss of wealth. This is evident from the 2000 dot com bubble and 2007 housing bubble,” Dr Kwok said.

The method is applicable to any market indices or stocks that have liquidly traded options. The ASX 100 index and blue-chip stocks such as those of the big four banks fall into this category.

A pre-print of the paper is available online and is forthcoming in print in the Journal of Applied Econometrics.

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