2006issue C011-2
Squared versus absolute deviation in envelope construction
Bollinger Bands set width from standard deviation. The commodity channel index scales from mean absolute deviation. With the same inputs and equalized constants, the two widths stay close when prices hug their average, then diverge at extremes. Changing constants cannot force the series to match.
- When the same inputs are used and the constants are equalized, the main construction difference between Bollinger Bands and the commodity channel index is standard deviation versus mean absolute deviation.
- The two dispersion calculations stay close when prices hug their average and diverge sharply once prices move away from that average.
- Squaring deviations accentuates large moves, so changing constants cannot close the nonlinear construction gap between the two widths.
- The percent-b oscillator taken from Bollinger Bands and the commodity channel index should not be treated as interchangeable tools.
Two widths from the same inputs
Bollinger Bands are a price envelope whose width is set from a standard-deviation measure of how far observations sit from a central average. The commodity channel index is a channel oscillator whose scale is built from mean absolute deviation rather than standard deviation.
When the same inputs are used and the constants are equalized, the main construction difference is that dispersion choice. Standard deviation squares deviations from the mean and therefore enlarges the influence of large moves. Mean absolute deviation averages unsigned deviations and does not magnify outliers by squaring.
Close near the average, apart at extremes
The two dispersion calculations stay close when prices hug their average and diverge sharply once prices move away from that average. Squaring deviations in the standard-deviation calculation accentuates large moves, while mean absolute deviation has no equivalent magnifying step.
The construction gap matters most at extremes, where a band-based reading of relatively high and low must stay aligned with a rapidly changing price structure.
A nonlinear construction gap
Changing constants cannot force the two series to match. The difference is a nonlinear construction gap: it comes from squaring deviations when standard deviation is computed, and it cannot be removed by changing constants because one path squares deviations and the other does not.
For a normally distributed series, mean absolute deviation is about 0.7979 of standard deviation. Because security prices are not normally distributed, the gap can be larger.
Percent-b is not a substitute channel oscillator
Percent-b is an oscillator that restates price as a position inside a Bollinger Band envelope. Because that envelope is a standard-deviation construction, the percent-b oscillator taken from Bollinger Bands and the commodity channel index should not be treated as interchangeable tools.
All readings on this track · 45 readings
- 1992Constructing volatility-scaled bands with relative strength index confirmation
- 1994Implied volatility as a band-defined regime filter for index options
- 1995Constructing projection bands from least-squares slopes
- 1995Constructing regression projection bands and range oscillators
- 1996Constructing Bollinger bands, percent-b, and stochastics
- 1996Constructing mechanical rules from Bollinger Bands and stochastics
- 1996Constructing a standard-error envelope around a linear regression
- 1996Dual-horizon ratio envelopes and regression error channels
- 1997Rational group structure with a trend screen, RSI, and bands
- 1997Asymmetric volatility band construction
- 1998Constructing three-state filters from Bollinger band envelopes
- 1999Combination filters with Bollinger Bands and the relative strength index
- 1999Constructing stochastic timed exits and band-RSI reversals
- 1999Evaluating Bollinger Bands against fixed-width and range-based envelopes
- 2000Constructing a Bollinger Band target as a forward price
- 2001Numeric candlestick encoding with local size bands
- 2001Ranked candlestick sentiment to band-cross entries
- 2002Combining Bollinger Bands, RSI, and a stop-loss
- 2002Bollinger Bands remain filters, not forecasts
- 2002Constructing a stochastic RSI with Bollinger bands
- 2002Constructing a StochRSI and Bollinger mechanical system
- 2003Constructing volatility-scaled Bollinger envelopes
- 2003Why tick breadth fails as a market personality
- 2005Constructing Bollinger bands versus fixed trading bands
- 2006Squared versus absolute deviation in envelope construction
- 2006Confirming yen crossovers with implied volatility and bands
- 2006A daily candle reversal is a hypothesis until shorter sessions fail at the same zone
- 2008Rebuild the Relative Strength Index as price-scale bands
- 2008Reading Relative Strength Index extremes on one price axis with Bollinger Bands and moving averages
- 2011Three-filter confirmation for short-swing futures
- 2011Constructing an inverse Fisher stochastic with bands and averages
- 2012Constructing a Bollinger Band indicator suite
- 2012Stacking price extremes, crossovers, bands, and MACD
- 2012Adaptive Bollinger band impulse, trend, and momentum filters
- 2013Rescaling stochastic, percent-B, and wave-count parameters
- 2014Industry-group quartile pivots as a Bollinger Bands case study
- 2014Bollinger Bands as adaptive price envelopes: a 2014 classroom case
- 2016Trend-channel entry rules from stacked moving averages
- 2016A permission stack for Bollinger, RSI, and the 50-period average
- 2017Constructing weighted Bollinger bands and volume averages
- 2017Four swing-entry rules that share a timed exit
- 2017Two-wave monthly cycles as a regime filter
- 2019Constructing exponential-deviation-bands from a midline-average
- 2020Critiquing exponential variants of Bollinger Bands
- 2020Constructing selectable volatility and moving-average bands