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1992issue C041-2

Building intermarket context with linear correlation

Treat the signed correlation-coefficient as a construction gauge, not a story. Before a single position sits inside a two-market or spread frame, confirm that the scatter is straight, size the context by how close the intermarket-pair sits to plus or minus one, and refuse to promote a very small observation-count into a weeks-to-months regime label.

  • The archive presents the correlation-coefficient as a bounded index of how tightly two series move together on a straight-line basis, from perfect inverse alignment through no linear alignment to perfect positive alignment.
  • Editorial reading: apply the linear-restriction first and confirm that a straight line, not a curve, describes the intermarket-pair before a position is placed inside a two-market or spread frame.
  • A large reading is association-not-causation: it describes co-movement and is not proof that one series makes the other large.
  • Editorial reading: the worked pair uses only 10 paired closes and is flagged as a very small observation-count, too thin to promote into a weeks-to-months regime label.
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Current trackCorrelation analysis52 readings

What the coefficient measures

Correlation analysis is presented as a measure of the degree of relationship between two variables. The correlation-coefficient is a bounded index of how tightly two series move together on a straight-line basis. It spans perfect inverse alignment at minus one, no linear alignment at zero, and perfect positive alignment at plus one.

Computation of the coefficient is defined from the observation-count, the two series, their means, and the summed products and squares of those series.

The linear-restriction comes first

The coefficient is restricted to relationships that can be treated as linear rather than curved. The linear-restriction is the rule that the coefficient is only a fair construction input when a straight line, not a curve, describes the pair.

An intermarket-pair is two closing-price series chosen so one market can be read against another when assembling spread or portfolio context. If the scatter is curved, the signed reading is not a fair gauge for that frame.

Association is not causation

A large positive coefficient is not treated as proof that one variable causes the other to be large. Association-not-causation is the limit that a large reading describes co-movement and does not establish that one series makes the other large.

Do not promote a tiny paired sample

A worked intermarket-pair uses bond-futures closes against a commodity-index close with an observation-count of 10, flagged as very small. Observation-count is the number of paired prints used to compute the coefficient; a very small count weakens any intermarket frame built from it.

Editorial reading: refuse to promote that tiny paired sample into a weeks-to-months regime label. Size the context by how close the pair sits to plus or minus one, then stop short of treating a 10-print reading as a durable regime.

Bond futures and CRB closes behind r = -0.83

Over the ten sessions from 2 January through 15 January 1991, bond futures ease from 95.875 to 93.062 while the CRB index firms from 218.46 to 221.60. That inverse pairing is the worksheet behind the sidebar's r of -0.83. The closes are taken from the published calculation table, not from a redrawn curve. Ten paired observations are too few to treat the reading as a lasting regime.
Over the ten sessions from 2 January through 15 January 1991, bond futures ease from 95.875 to 93.062 while the CRB index firms from 218.46 to 221.60. That inverse pairing is the worksheet behind the sidebar's r of -0.83. The closes are taken from the published calculation table, not from a redrawn curve. Ten paired observations are too few to treat the reading as a lasting regime.Bond futures and CRB index · daily · 1991-01-02T00:00:00.000Z to 1991-01-15T00:00:00.000Z

The sidebar fixes n at 10 because of space and applies the linear-r formula only. The published r is association, not cause and effect.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
8 of 37 in the Correlation analysis track
19931-4 pp.Next on Correlation analysisInverse-scale overlays as a gold-equity regime filterAn inverse-scale-overlay inverts daily gold against the industrial average so gold-equity-inversion can be read as paired travel.
All readings on this track · 37 readings
  1. 1988Constructing a lead-aware correlation coefficient
  2. 1989A precious-metal price as a changing intermarket equation
  3. 1990Two clocks for copper: a factor regime, a regression baseline, and leftover moving-average timing
  4. 1990Earnings yield, rate correlation and regression for equity value
  5. 1991Name the window, then combine leaders
  6. 1991Constructing a two-market linear correlation check
  7. 1991Constructing a commodity-bond correlation regime filter
  8. 1992Building intermarket context with linear correlation
  9. 1993Inverse-scale overlays as a gold-equity regime filter
  10. 1994Constructing seasonal slots from windows, analog years, and implied volatility
  11. 1995Pin one reference close and roll companion correlations as an overlay
  12. 1995Rolling correlation windows for shifting intermarket regimes
  13. 1998Gold as a cross-market regime barometer
  14. 1999The gold-bond inverse is a regime, not a cause
  15. 1999A nested lag test of gold leading bond yields
  16. 1999Constructing spreads from stock and intermarket correlation
  17. 2000Evaluating headline versus food-and-energy-excluded CPI as bond-yield context
  18. 2005A late EUR/USD fifth wave tested by the Bund-Treasury gap
  19. 2006Intermarket dislocation as context for short-horizon momentum
  20. 2008Map ordinary 12-month outcomes before stacking valuation, rates, and seasonality
  21. 2008A clean-energy theme inside the oil-and-energy regime
  22. 2014Quantitative-easing overlays as fragile belief regimes
  23. 2015Three intermarket checks from the late-2014 crude decline
  24. 2015Basket construction via rank, correlation, and locked rules
  25. 2015Construct a CAD-oil pair from percent-of-range Bollinger maps
  26. 2015CAD/USD and crude: first the correlation, then the band gap
  27. 2017Correlation regime versus moving-average crossover for S&P 500 exposure
  28. 2017Updating intermarket systems after correlation shifts
  29. 2017Constructing a correlation-divergence regime filter for yen and Nikkei context
  30. 2018Clustered negative troughs in an energy-index pairwise correlation
  31. 2018Filter pairwise-correlation before reading an intermarket regime
  32. 2018Moving-average supports in the March 2018 correlation shock
  33. 2020Bond spreads as an equity regime lens
  34. 2020Crash-protection folklore as a correlation regime question
  35. 2020Constructing a bounded correlation-trend-filter
  36. 2020Constructing a correlation-to-line trend filter
  37. 2020Bitcoin correlation regimes across equities and gold
By fieldIntermarket and spreads148 readingsBy year1992115 readings
All 52 readings tagged Correlation analysis
Also on Correlation analysis5 readings
1988Building consensus indicators with correlation and the chi-square test1988Constructing a lead-aware correlation coefficient1989A precious-metal price as a changing intermarket equation1990Two clocks for copper: a factor regime, a regression baseline, and leftover moving-average timing1990Earnings yield, rate correlation and regression for equity value