Correlation Between Siemens Energy and Pampa Energa
Can any of the company-specific risk be diversified away by investing in both Siemens Energy and Pampa Energa 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 Siemens Energy and Pampa Energa into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Siemens Energy AG and Pampa Energa SA, you can compare the effects of market volatilities on Siemens Energy and Pampa Energa 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 Siemens Energy with a short position of Pampa Energa. Check out your portfolio center. Please also check ongoing floating volatility patterns of Siemens Energy and Pampa Energa.
Diversification Opportunities for Siemens Energy and Pampa Energa
0.97 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Siemens and Pampa is 0.97. Overlapping area represents the amount of risk that can be diversified away by holding Siemens Energy AG and Pampa Energa SA in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Pampa Energa SA and Siemens Energy 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 Siemens Energy AG are associated (or correlated) with Pampa Energa. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Pampa Energa SA has no effect on the direction of Siemens Energy i.e., Siemens Energy and Pampa Energa go up and down completely randomly.
Pair Corralation between Siemens Energy and Pampa Energa
Assuming the 90 days horizon Siemens Energy is expected to generate 2.04 times less return on investment than Pampa Energa. In addition to that, Siemens Energy is 1.08 times more volatile than Pampa Energa SA. It trades about 0.18 of its total potential returns per unit of risk. Pampa Energa SA is currently generating about 0.39 per unit of volatility. If you would invest 7,200 in Pampa Energa SA on September 19, 2024 and sell it today you would earn a total of 1,600 from holding Pampa Energa SA or generate 22.22% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Siemens Energy AG vs. Pampa Energa SA
Performance |
Timeline |
Siemens Energy AG |
Pampa Energa SA |
Siemens Energy and Pampa Energa Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Siemens Energy and Pampa Energa
The main advantage of trading using opposite Siemens Energy and Pampa Energa positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Siemens Energy position performs unexpectedly, Pampa Energa 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 Pampa Energa will offset losses from the drop in Pampa Energa's long position.Siemens Energy vs. CN YANGTPWR GDR | Siemens Energy vs. SIEMENS ENERGY AG | Siemens Energy vs. Vistra Corp | Siemens Energy vs. CENTRICA ADR NEW |
Pampa Energa vs. CN YANGTPWR GDR | Pampa Energa vs. SIEMENS ENERGY AG | Pampa Energa vs. Siemens Energy AG | Pampa Energa vs. Vistra Corp |
Check out your portfolio center.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.
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