Correlation Between DAX Index and SPTSX Dividend

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

By analyzing existing cross correlation between DAX Index and SPTSX Dividend Aristocrats, you can compare the effects of market volatilities on DAX Index and SPTSX Dividend 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 SPTSX Dividend. Check out your portfolio center. Please also check ongoing floating volatility patterns of DAX Index and SPTSX Dividend.

Diversification Opportunities for DAX Index and SPTSX Dividend

0.8

Correlation Coefficient

DAX Index  SPTSX Dividend

Very poor diversification

The 3 months correlation between DAX and SPTSX is 0.8. Overlapping area represents the amount of risk that can be diversified away by holding DAX Index and SPTSX Dividend Aristocrats in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on SPTSX Dividend Arist 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 SPTSX Dividend. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of SPTSX Dividend Arist has no effect on the direction of DAX Index i.e., DAX Index and SPTSX Dividend go up and down completely randomly.

Pair Corralation between DAX Index and SPTSX Dividend

Assuming the 90 days trading horizon DAX Index is expected to under-perform the SPTSX Dividend. In addition to that, DAX Index is 2.06 times more volatile than SPTSX Dividend Aristocrats. It trades about -0.05 of its total potential returns per unit of risk. SPTSX Dividend Aristocrats is currently generating about 0.21 per unit of volatility. If you would invest  36,606  in SPTSX Dividend Aristocrats on August 30, 2024 and sell it today you would earn a total of  763.00  from holding SPTSX Dividend Aristocrats or generate 2.08% return on investment over 90 days.

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

DAX Index  vs.  SPTSX Dividend Aristocrats

Pair Trading with DAX Index and SPTSX Dividend

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

The idea behind DAX Index and SPTSX Dividend Aristocrats 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.