2 Year Correlations
| ZTUSD Commodity | 103.53 0.10 0.1% |
The current 90-days correlation between 2 Year T and Micro Gold Futures is -0.06 (i.e., Good diversification). The correlation of 2 Year is a statistical measure of how it moves in relation to other instruments. This measure is expressed in what is known as the correlation coefficient, which ranges between -1 and +1. A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally considered weak.
2 Year Correlation With Market
Good diversification
The correlation between 2 Year T Note Futures and DJI is -0.18 (i.e., Good diversification) for selected investment horizon. Overlapping area represents the amount of risk that can be diversified away by holding 2 Year T Note Futures and DJI in the same portfolio, assuming nothing else is changed.
ZTUSD |
The ability to find closely correlated positions to 2 Year could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace 2 Year when you sell it. If you don't do this, your portfolio allocation will be skewed against your target asset allocation. So, investors can't just sell and buy back 2 Year - that would be a violation of the tax code under the "wash sale" rule, and this is why you need to find a similar enough asset and use the proceeds from selling 2 Year T Note Futures to buy it.
Moving together with ZTUSD Commodity
| 0.82 | BRK-A | Berkshire Hathaway | PairCorr |
| 0.81 | DNSKF | Danske Bank AS Normal Trading | PairCorr |
| 0.88 | TAIL | Cambria Tail Risk | PairCorr |
| 0.83 | SPIB | SPDR Barclays Interm Sell-off Trend | PairCorr |
| 0.75 | LIAE | Stone Ridge 2050 | PairCorr |
| 0.72 | GHMS | Goose Hollow Multi | PairCorr |
| 0.72 | ACWV | iShares MSCI Global | PairCorr |
| 0.78 | JMST | JPMorgan Ultra Short | PairCorr |
| 0.8 | VICSX | Vanguard Intermediate-ter | PairCorr |
| 0.74 | GIOIX | Guggenheim Macro Opp | PairCorr |
Moving against ZTUSD Commodity
| 0.82 | AMZN | Amazon Inc Aggressive Push | PairCorr |
| 0.82 | GOOG | Alphabet Class C Aggressive Push | PairCorr |
| 0.8 | NVDA | NVIDIA Aggressive Push | PairCorr |
| 0.77 | MSFT | Microsoft Aggressive Push | PairCorr |
| 0.53 | AAPL | Apple Inc Aggressive Push | PairCorr |
| 0.31 | ISPOW | Thayer Ventures Acqu | PairCorr |
Related Correlations Analysis
| 0.87 | -0.61 | -0.46 | -0.61 | MGCUSD | ||
| 0.87 | -0.58 | -0.7 | -0.82 | LBUSD | ||
| -0.61 | -0.58 | 0.51 | 0.51 | CTUSX | ||
| -0.46 | -0.7 | 0.51 | 0.77 | DCUSD | ||
| -0.61 | -0.82 | 0.51 | 0.77 | RTYUSD | ||
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Correlation Matchups
Over a given time period, the two securities move together when the Correlation Coefficient is positive. Conversely, the two assets move in opposite directions when the Correlation Coefficient is negative. Determining your positions' relationship to each other is valuable for analyzing and projecting your portfolio's future expected return and risk.High positive correlations
| High negative correlations
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Risk-Adjusted Indicators
There is a big difference between ZTUSD Commodity performing well and 2 Year Commodity doing well as a business compared to the competition. There are so many exceptions to the norm that investors cannot definitively determine what's good or bad unless they analyze 2 Year's multiple risk-adjusted performance indicators across the competitive landscape. These indicators are quantitative in nature and help investors forecast volatility and risk-adjusted expected returns across various positions.| Mean Deviation | Jensen Alpha | Sortino Ratio | Treynor Ratio | Semi Deviation | Expected Shortfall | Potential Upside | Value @Risk | Maximum Drawdown | ||
|---|---|---|---|---|---|---|---|---|---|---|
| MGCUSD | 0.65 | 0.19 | 0.29 | (0.88) | 0.50 | 1.62 | 4.36 | |||
| LBUSD | 1.38 | 0.36 | 0.26 | (1.65) | 1.33 | 3.59 | 11.20 | |||
| CTUSX | 0.79 | (0.06) | 0.00 | (0.62) | 0.00 | 1.51 | 6.01 | |||
| DCUSD | 1.02 | (0.13) | 0.00 | 1.48 | 0.00 | 1.54 | 15.17 | |||
| RTYUSD | 0.87 | (0.11) | 0.00 | (0.23) | 0.00 | 1.95 | 4.94 |
2 Year Related Commodities
One prevalent trading approach among algorithmic traders in the commodities sector involves employing market-neutral strategies, wherein each trade is designed to hedge away specific risks. Given that this approach necessitates two distinct transactions, if one position underperforms unexpectedly, the other can potentially offset some of the losses. This method can be applied to commodities such as 2 Year, pairing it with other commodities or financial instruments to create a balanced, market-neutral setup.
| Risk & Return | Correlation |