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Moving
Average (Variable)
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. |
