Correlation Between DAX Index and Bayerische Motoren

Can any of the company-specific risk be diversified away by investing in both DAX Index and Bayerische Motoren 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 DAX Index and Bayerische Motoren into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between DAX Index and Bayerische Motoren Werke, you can compare the effects of market volatilities on DAX Index and Bayerische Motoren 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 DAX Index with a short position of Bayerische Motoren. Check out your portfolio center. Please also check ongoing floating volatility patterns of DAX Index and Bayerische Motoren.

Diversification Opportunities for DAX Index and Bayerische Motoren

-0.14

Correlation Coefficient

DAX Index  Bayerische Motoren

Good diversification

The 3 months correlation between DAX and Bayerische is -0.14. Overlapping area represents the amount of risk that can be diversified away by holding DAX Index and Bayerische Motoren Werke in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bayerische Motoren Werke and DAX Index 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 DAX Index are associated (or correlated) with Bayerische Motoren. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bayerische Motoren Werke has no effect on the direction of DAX Index i.e., DAX Index and Bayerische Motoren go up and down completely randomly.

Pair Corralation between DAX Index and Bayerische Motoren

Assuming the 90 days trading horizon DAX Index is expected to generate 0.39 times more return on investment than Bayerische Motoren. However, DAX Index is 2.55 times less risky than Bayerische Motoren. It trades about 0.09 of its potential returns per unit of risk. Bayerische Motoren Werke is currently generating about -0.1 per unit of risk. If you would invest  1,874,711  in DAX Index on September 3, 2024 and sell it today you would earn a total of  87,934  from holding DAX Index or generate 4.69% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Against
Strength	Insignificant
Accuracy	100.0%
Values	Daily Returns

DAX Index  vs.  Bayerische Motoren Werke

Pair Trading with DAX Index and Bayerische Motoren

The main advantage of trading using opposite DAX Index and Bayerische Motoren positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if DAX Index position performs unexpectedly, Bayerische Motoren 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 Bayerische Motoren will offset losses from the drop in Bayerische Motoren's long position.

The idea behind DAX Index and Bayerische Motoren Werke 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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.