Heating Oil Correlations
| HOUSD Commodity | 2.44 0.08 3.39% |
The current 90-days correlation between Heating Oil and Micro Silver Futures is 0.05 (i.e., Significant diversification). The correlation of Heating Oil 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.
Heating Oil Correlation With Market
Very good diversification
The correlation between Heating Oil and DJI is -0.29 (i.e., Very good diversification) for selected investment horizon. Overlapping area represents the amount of risk that can be diversified away by holding Heating Oil and DJI in the same portfolio, assuming nothing else is changed.
Heating |
The ability to find closely correlated positions to Heating Oil could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Heating Oil 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 Heating Oil - 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 Heating Oil to buy it.
Moving against Heating Commodity
| 0.67 | GLWLF | Glow Lifetech Corp | PairCorr |
| 0.6 | BMYMP | Bristol Myers Squibb Earnings Call Tomorrow | PairCorr |
| 0.57 | UNP | Union Pacific | PairCorr |
| 0.54 | PB | Prosperity Bancshares | PairCorr |
| 0.4 | RKLIF | Rentokil Initial plc | PairCorr |
| 0.36 | IGSB | iShares 1 5 | PairCorr |
| 0.64 | QLENX | Aqr Long Short | PairCorr |
| 0.53 | BUFTX | Buffalo Discovery | PairCorr |
| 0.47 | DMSZX | Destinations Multi | PairCorr |
| 0.41 | GRGUX | Goldman Sachs Growth | PairCorr |
| 0.39 | XBI | SPDR SP Biotech | PairCorr |
| 0.39 | QNTAX | Quantex Fund Adviser | PairCorr |
| 0.35 | FEUS | FlexShares ESG Climate | PairCorr |
| 0.31 | DIVB | iShares Dividend Low Volatility | PairCorr |
Related Correlations Analysis
| -0.86 | -0.7 | 0.69 | -0.87 | 0.81 | YMUSD | ||
| -0.86 | 0.68 | -0.77 | 0.84 | -0.87 | DCUSD | ||
| -0.7 | 0.68 | -0.56 | 0.68 | -0.76 | DXUSD | ||
| 0.69 | -0.77 | -0.56 | -0.73 | 0.8 | GFUSX | ||
| -0.87 | 0.84 | 0.68 | -0.73 | -0.74 | ZSUSX | ||
| 0.81 | -0.87 | -0.76 | 0.8 | -0.74 | SILUSD | ||
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
|
Risk-Adjusted Indicators
There is a big difference between Heating Commodity performing well and Heating Oil 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 Heating Oil'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 | ||
|---|---|---|---|---|---|---|---|---|---|---|
| YMUSD | 0.58 | 0.05 | 0.01 | 1.58 | 0.63 | 1.23 | 3.21 | |||
| DCUSD | 0.94 | (0.01) | 0.00 | 0.06 | 0.00 | 2.84 | 16.75 | |||
| DXUSD | 0.25 | (0.05) | 0.00 | (0.78) | 0.00 | 0.51 | 1.84 | |||
| GFUSX | 1.06 | (0.01) | (0.01) | 0.02 | 2.03 | 2.62 | 8.51 | |||
| ZSUSX | 0.87 | (0.07) | 0.00 | (0.23) | 0.00 | 1.70 | 5.86 | |||
| SILUSD | 3.86 | 1.03 | 0.14 | 1.13 | 6.07 | 7.94 | 45.36 |
Heating Oil 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 Heating Oil, pairing it with other commodities or financial instruments to create a balanced, market-neutral setup.
| Risk & Return | Correlation |