Correlation Between AerCap Holdings and NYSE Composite

Can any of the company-specific risk be diversified away by investing in both AerCap Holdings and NYSE Composite at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining AerCap Holdings and NYSE Composite into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between AerCap Holdings NV and NYSE Composite, you can compare the effects of market volatilities on AerCap Holdings and NYSE Composite and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in AerCap Holdings with a short position of NYSE Composite. Check out your portfolio center. Please also check ongoing floating volatility patterns of AerCap Holdings and NYSE Composite.

Diversification Opportunities for AerCap Holdings and NYSE Composite

0.59

Correlation Coefficient

AerCap Holdings  NYSE Composite

Very weak diversification

The 3 months correlation between AerCap and NYSE is 0.59. Overlapping area represents the amount of risk that can be diversified away by holding AerCap Holdings NV and NYSE Composite in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on NYSE Composite and AerCap Holdings is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on AerCap Holdings NV are associated (or correlated) with NYSE Composite. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of NYSE Composite has no effect on the direction of AerCap Holdings i.e., AerCap Holdings and NYSE Composite go up and down completely randomly.

Pair Corralation between AerCap Holdings and NYSE Composite

Considering the 90-day investment horizon AerCap Holdings NV is expected to generate 1.97 times more return on investment than NYSE Composite. However, AerCap Holdings is 1.97 times more volatile than NYSE Composite. It trades about 0.11 of its potential returns per unit of risk. NYSE Composite is currently generating about 0.06 per unit of risk. If you would invest  3,781  in AerCap Holdings NV on March 7, 2024 and sell it today you would earn a total of  5,431  from holding AerCap Holdings NV or generate 143.64% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Weak
Accuracy	99.8%
Values	Daily Returns

AerCap Holdings NV  vs.  NYSE Composite

Pair Trading with AerCap Holdings and NYSE Composite

The main advantage of trading using opposite AerCap Holdings and NYSE Composite positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if AerCap Holdings position performs unexpectedly, NYSE Composite can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in NYSE Composite will offset losses from the drop in NYSE Composite's long position.

The idea behind AerCap Holdings NV and NYSE Composite pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.

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Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.