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After a seven-year period of being stuck in the zero lower bound (ZLB) range, the target rate was raised by 25 basis points on December 16, 2015. Prior to the rate hike, the important issues that the Federal Reserve dealt with were the magnitude, timing, and the information conveyed by a first-time rate hike from the ZLB period. The purpose of this paper is to use the data from the ZLB period and simulate the impact of an increase in the proxies for the federal funds rate: effective federal funds rate and shadow rate, and measure the impact on the resulting changes in credit default swap (CDS) spreads across 11 industries. Increases in both proxies predict a significant decrease in CDS spreads which is indicative of an economic recovery. This prediction is confirmed by the announcement effect of the actual rate increase on December 16, 2015 and the three subsequent rate increases.

In the absence of target rate changes in the ZLB environment, the authors use a recursive vector autoregressive (VAR) model to simulate the rate increases in proxies for target federal rate and predict the impact on the economy by observing the reaction in CDS spreads and stock returns across 11 industries.

The impulse response indicates that an increase of one standard deviation in the effective rate (approximately 25 basis points) results in a statistically significant decrease in the spreads of CDS contracts in 8 of the 11 sectors studied in this research. Similar results obtain for an increase in shadow rate thus providing a robustness check. These results suggest a rate increase from the ZLB period and the resulting dynamics captured in the VAR system is indicative of an economic recovery.

Prior studies have used the event study methodology to evaluate the impact of rate changes on credit spreads. The ZLB environment does not contain data on target rate changes and renders the event study methodology as ineffective. This paper is the first to simulate the implications of a first-time rate increase from the ZLB environment in the context of a recursive VAR model. The results are very helpful to the Federal Reserve of countries experiencing a ZLB environment such as Japan and Europe.

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.