Correlation Between DAX Index and China Coal

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Can any of the company-specific risk be diversified away by investing in both DAX Index and China Coal 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 China Coal into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between DAX Index and China Coal Energy, you can compare the effects of market volatilities on DAX Index and China Coal 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 China Coal. Check out your portfolio center. Please also check ongoing floating volatility patterns of DAX Index and China Coal.

Diversification Opportunities for DAX Index and China Coal

0.29
  Correlation Coefficient

Modest diversification

The 3 months correlation between DAX and China is 0.29. Overlapping area represents the amount of risk that can be diversified away by holding DAX Index and China Coal Energy in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on China Coal Energy 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 China Coal. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of China Coal Energy has no effect on the direction of DAX Index i.e., DAX Index and China Coal go up and down completely randomly.
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Pair Corralation between DAX Index and China Coal

Assuming the 90 days trading horizon DAX Index is expected to generate 2.86 times less return on investment than China Coal. But when comparing it to its historical volatility, DAX Index is 3.94 times less risky than China Coal. It trades about 0.09 of its potential returns per unit of risk. China Coal Energy is currently generating about 0.06 of returns per unit of risk over similar time horizon. If you would invest  50.00  in China Coal Energy on September 24, 2024 and sell it today you would earn a total of  59.00  from holding China Coal Energy or generate 118.0% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthVery Weak
Accuracy100.0%
ValuesDaily Returns

DAX Index  vs.  China Coal Energy

 Performance 
       Timeline  

DAX Index and China Coal Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with DAX Index and China Coal

The main advantage of trading using opposite DAX Index and China Coal positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if DAX Index position performs unexpectedly, China Coal 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 China Coal will offset losses from the drop in China Coal's long position.
The idea behind DAX Index and China Coal Energy 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 Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.

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