Correlation Between IPath Bloomberg and PIMCO ETF
Can any of the company-specific risk be diversified away by investing in both IPath Bloomberg and PIMCO ETF 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 IPath Bloomberg and PIMCO ETF into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between iPath Bloomberg Commodity and PIMCO ETF Trust, you can compare the effects of market volatilities on IPath Bloomberg and PIMCO ETF 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 IPath Bloomberg with a short position of PIMCO ETF. Check out your portfolio center. Please also check ongoing floating volatility patterns of IPath Bloomberg and PIMCO ETF.
Diversification Opportunities for IPath Bloomberg and PIMCO ETF
0.87 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between IPath and PIMCO is 0.87. Overlapping area represents the amount of risk that can be diversified away by holding iPath Bloomberg Commodity and PIMCO ETF Trust in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on PIMCO ETF Trust and IPath Bloomberg 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 iPath Bloomberg Commodity are associated (or correlated) with PIMCO ETF. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of PIMCO ETF Trust has no effect on the direction of IPath Bloomberg i.e., IPath Bloomberg and PIMCO ETF go up and down completely randomly.
Pair Corralation between IPath Bloomberg and PIMCO ETF
Considering the 90-day investment horizon iPath Bloomberg Commodity is expected to generate 1.17 times more return on investment than PIMCO ETF. However, IPath Bloomberg is 1.17 times more volatile than PIMCO ETF Trust. It trades about 0.05 of its potential returns per unit of risk. PIMCO ETF Trust is currently generating about 0.06 per unit of risk. If you would invest 3,061 in iPath Bloomberg Commodity on August 30, 2024 and sell it today you would earn a total of 95.00 from holding iPath Bloomberg Commodity or generate 3.1% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
iPath Bloomberg Commodity vs. PIMCO ETF Trust
Performance |
Timeline |
iPath Bloomberg Commodity |
PIMCO ETF Trust |
IPath Bloomberg and PIMCO ETF Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with IPath Bloomberg and PIMCO ETF
The main advantage of trading using opposite IPath Bloomberg and PIMCO ETF positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if IPath Bloomberg position performs unexpectedly, PIMCO ETF 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 PIMCO ETF will offset losses from the drop in PIMCO ETF's long position.IPath Bloomberg vs. iShares SP GSCI | IPath Bloomberg vs. Invesco DB Commodity | IPath Bloomberg vs. Invesco DB Base |
PIMCO ETF vs. ProShares VIX Mid Term | PIMCO ETF vs. ProShares VIX Short Term | PIMCO ETF vs. Dynamic Short Short Term | PIMCO ETF vs. Advisors Inner Circle |
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 Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.
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