Correlation Between OMX Copenhagen and Skjern Bank

Can any of the company-specific risk be diversified away by investing in both OMX Copenhagen and Skjern Bank 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 OMX Copenhagen and Skjern Bank into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between OMX Copenhagen All and Skjern Bank AS, you can compare the effects of market volatilities on OMX Copenhagen and Skjern Bank 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 OMX Copenhagen with a short position of Skjern Bank. Check out your portfolio center. Please also check ongoing floating volatility patterns of OMX Copenhagen and Skjern Bank.

Diversification Opportunities for OMX Copenhagen and Skjern Bank

0.34

Correlation Coefficient

OMX Copenhagen  Skjern Bank

Weak diversification

The 3 months correlation between OMX and Skjern is 0.34. Overlapping area represents the amount of risk that can be diversified away by holding OMX Copenhagen All and Skjern Bank AS in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Skjern Bank AS and OMX Copenhagen 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 OMX Copenhagen All are associated (or correlated) with Skjern Bank. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Skjern Bank AS has no effect on the direction of OMX Copenhagen i.e., OMX Copenhagen and Skjern Bank go up and down completely randomly.

Pair Corralation between OMX Copenhagen and Skjern Bank

Assuming the 90 days trading horizon OMX Copenhagen All is expected to under-perform the Skjern Bank. But the index apears to be less risky and, when comparing its historical volatility, OMX Copenhagen All is 1.13 times less risky than Skjern Bank. The index trades about -0.13 of its potential returns per unit of risk. The Skjern Bank AS is currently generating about -0.02 of returns per unit of risk over similar time horizon. If you would invest  20,650  in Skjern Bank AS on December 30, 2024 and sell it today you would lose (650.00) from holding Skjern Bank AS or give up 3.15% of portfolio value over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Very Weak
Accuracy	100.0%
Values	Daily Returns

OMX Copenhagen All  vs.  Skjern Bank AS

Pair Trading with OMX Copenhagen and Skjern Bank

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

The idea behind OMX Copenhagen All and Skjern Bank AS 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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.