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Variable Moving Average
Description
A variable moving average is an exponential moving average that
automatically adjusts the smoothing constant based on the volatility
of the data series. The more volatile the data, the larger the
smoothing constant used in the moving average calculation. The
larger the smoothing constant, the more weight given to the current
data. The opposite is true for less volatile data.
Trader’s often associate high volatility with strongly trending
markets. However, this is a mistake. Strong trending markets are
often less volatile because of the consistency of day-to-day price
changes. Its when prices are erratic in their day-to-day movements
(i.e., down a lot, up a little, up a little, up a lot, up a little,
down a little, etc.), that volatility increases. This can occur in
uptrending, downtrending, or sideways markets.
Typical moving averages suffer from the inability to compensate for
changes in volatility. During volatile markets, you want a moving
average to increase its sensitivity, so that you will quickly be on
the correct side of any wild gyrations. By automatically adjusting
the smoothing constant, a variable moving average is able to adjust
its sensitivity, allowing it to perform better in both high and low
volatility markets.
VMA = (0.78*(volatility index) * close)
+ (1-0.078 * volatility index)*yesterday’s VMA
The absolute value of a 9-period Chande Momentum Oscillator is
used for the volatility index. The higher this index the more
volatile the market, thereby increasing the sensitivity of the
moving average.
This method of calculating a variable moving average was presented
by Tushar Chande in the March 1992 issue of Technical Analysis of
Stocks & Commodities magazine.
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