Extreme return correlation and volatility: a two-threshold approach

The purpose of this paper is to examine the relation between extreme return correlation and return volatility, in the context of US stock indexes, by detecting clusters of extreme returns using return and volatility thresholds based on an algorithm suggested in Laurini. The daily returns and conditional volatilities estimated using GARCH (1, 1) serve as inputs to the two threshold algorithm that detects extreme return clusters. The analysis of the relation between correlation and volatility is then based on the extent of overlapping extreme return clusters across DJIA, S&P 500 and NASDAQ composite. It is found that the correlation positive extreme returns within overlapping clusters significantly increases with volatility between DJIA and S&P 500. The authors did not find any significant change in the pair-wise correlation between the positive extreme returns within overlapping clusters in each of these indexes with those of NASDAQ composite. Prior researches examine extreme returns by using a return threshold and have found mixed results on the relation between correlation and volatility. This paper examines the relation between correlation and volatility between clusters of extreme returns and provides consistent results that are of vital interest to investors.