30 Year Correlations

ZBUSD Commodity

117.38 0.34 0.29%

The current 90-days correlation between 30 Year Treasury and Five Year Treasury Note is 0.91 (i.e., Almost no diversification). The correlation of 30 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.

30 Year Correlation With Market

Average diversification

The correlation between 30 Year Treasury and DJI is 0.15 (i.e., Average diversification) for selected investment horizon. Overlapping area represents the amount of risk that can be diversified away by holding 30 Year Treasury and DJI in the same portfolio, assuming nothing else is changed.

The ability to find closely correlated positions to 30 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 30 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 30 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 30 Year Treasury to buy it.

Moving together with ZBUSD Commodity

0.85	BRK-A	Berkshire Hathaway	Pair Corr
0.68	SKFOF	Sika AG	Pair Corr
0.84	WM	Waste Management	Pair Corr
0.81	254687FM3	WALT DISNEY CO	Pair Corr
0.65	AIMDW	Ainos Inc	Pair Corr
0.82	64110LAT3	NetFlix 5875 percent	Pair Corr
0.7	WVVI	Willamette Valley Earnings Call Tomorrow	Pair Corr
0.62	TAIL	Cambria Tail Risk	Pair Corr
0.62	BBEM	JP Morgan Exchange	Pair Corr
0.89	SPIB	SPDR Barclays Interm	Pair Corr
0.87	LIAE	Stone Ridge 2050	Pair Corr
0.66	EEMX	SPDR MSCI Emerging	Pair Corr
0.8	GHMS	Goose Hollow Multi	Pair Corr
0.83	ACWV	iShares MSCI Global	Pair Corr
0.78	JMST	JPMorgan Ultra Short	Pair Corr
0.83	VICSX	Vanguard Intermediate-ter	Pair Corr
0.75	DBIWX	Dws Global Macro	Pair Corr
0.73	HTD	John Hancock Tax	Pair Corr
0.67	SGDLX	Sprott Gold Equity	Pair Corr
0.81	GIOIX	Guggenheim Macro Opp	Pair Corr

Moving against ZBUSD Commodity

0.74	GOOG	Alphabet Class C	Pair Corr
0.71	NVDA	NVIDIA	Pair Corr
0.68	MSFT	Microsoft	Pair Corr
0.58	AMZN	Amazon Inc	Pair Corr
0.44	38141E4D8	GOLDMAN SACHS GROUP	Pair Corr
0.35	AAPL	Apple Inc	Pair Corr
0.35	ISPOW	Thayer Ventures Acqu	Pair Corr

Related Correlations Analysis

	ZFUSD	NGUSD	RTYUSD	LEUSX	SIUSD	CLUSD	OJUSX
ZFUSD		0.48	-0.84	0.28	0.66	-0.81	-0.81	ZFUSD
NGUSD	0.48		-0.63	-0.04	0.32	-0.34	-0.57	NGUSD
RTYUSD	-0.84	-0.63		-0.23	-0.56	0.72	0.78	RTYUSD
LEUSX	0.28	-0.04	-0.23		0.41	-0.17	-0.21	LEUSX
SIUSD	0.66	0.32	-0.56	0.41		-0.55	-0.78	SIUSD
CLUSD	-0.81	-0.34	0.72	-0.17	-0.55		0.76	CLUSD
OJUSX	-0.81	-0.57	0.78	-0.21	-0.78	0.76		OJUSX
	ZFUSD	NGUSD	RTYUSD	LEUSX	SIUSD	CLUSD	OJUSX

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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

OJUSX	+ 0.78	RTYUSD
OJUSX	+ 0.76	CLUSD
CLUSD	+ 0.72	RTYUSD
SIUSD	+ 0.66	ZFUSD
NGUSD	+ 0.48	ZFUSD
SIUSD	+ 0.41	LEUSX

High negative correlations

RTYUSD	- 0.84	ZFUSD
OJUSX	- 0.81	ZFUSD
CLUSD	- 0.81	ZFUSD
OJUSX	- 0.78	SIUSD
RTYUSD	- 0.63	NGUSD
OJUSX	- 0.57	NGUSD

Risk-Adjusted Indicators

There is a big difference between ZBUSD Commodity performing well and 30 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 30 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
ZFUSD	0.16	0.02	0.44	0.75	0.00	(0.19)	0.37	(0.25)	1.00
NGUSD	4.03	0.21	0.06	(0.45)	4.87	(4.36)	7.92	(8.31)	25.21
RTYUSD	0.87	(0.15)	0.00	0.53	0.00	0.00	1.95	(1.98)	4.94
LEUSX	0.70	0.12	0.19	5.67	0.80	(0.76)	1.45	(1.23)	4.26
SIUSD	1.15	0.15	0.16	(1.20)	1.24	(1.23)	2.32	(1.90)	6.39
CLUSD	1.16	(0.05)	0.00	0.05	0.00	0.00	2.52	(2.50)	6.58
OJUSX	2.23	(0.95)	0.00	2.33	0.00	0.00	3.06	(5.03)	9.45

30 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 30 Year, pairing it with other commodities or financial instruments to create a balanced, market-neutral setup.

Five Year Treasury Note Natural Gas Micro E mini Russell Live Cattle Futures Silver Futures Crude Oil Orange Juice

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