The Complete Guide to Volatility Indicators
The volatility indicator is a technical tool that measures how far the stock moves away from its increasingly low average price. It calculates the dispersion of returns over time in a visual format that technicians use to assess whether that mathematical input is increasing or decreasing. Low volatility generally refers to calm price movements, with predictable short-term fluctuations, while high volatility refers to loud or dramatic price movements, with short-term fluctuations that are often unpredictable.
Volatility measures the degree to which the price moves over time, generating non-directional information unless the data is plotted in specific visual formats. This technical element has a big impact on option pricing and market sentiment, with high volatility generating extremes of greed and fear. Built as an indicator, volatility plots a history of price movement that complements trend, momentum and range analysis.
Volatile instruments are considered to be more risky than non-volatile instruments. Volatility regularly oscillates between highs and lows, providing a potential timing tool for traders and market timers. In particular, lower volatility over X periods is often a precursor to an impending move to high volatility which results in trend movement and trading signals. Market lore describes this classic dynamic, telling market participants to “buy in good times and sell in times of crisis”.
Bollinger bands are the most well-known volatility indicator in the financial market. Created by John Bollinger in the early 1980s, the indicator builds three lines around the price: a simple moving average acts as the middle band while the equally distant upper and lower bands expand and contract in response to changes in volatility. The 20 day or period SMA is the most common setting for the middle band, but this value is customizable in the advanced mapping program.
The calculation takes the standard deviation of the SMA, which is a way to calculate the distance from the SMA over time, and applies the result to the upper and lower bands. The bands expand and contract over time in response to changing levels of volatility. The tight bands “squeeze” the price action between narrow bounds, indicating low volatility while predicting a cycle change towards high volatility. The transition can trigger high odds entry and exit signals for many trading strategies.
Donchian chains build upper, lower and middle bands by looking at extreme prices over the chosen time period. The highest price over the chosen period marks the high band while the lowest price over the chosen period marks the low band. The middle band is constructed by subtracting the value of the low band from the value of the high band and dividing it by two. The indicator is then used to study the relationships between the current price and the trading ranges over the chosen period.
As with Bollinger Bands, 20 days or periods is the most common Donchian Channel setting. An upper band that goes up as price approaches (or a lower band that goes down) signals ease of movement that makes it easier to develop trends. Conversely, a band that remains horizontal as price approaches identifies support or resistance that increases the chances of a reversal and a return to the middle band. Bollinger Bands differ from Donchian channels, applying moving averages that reduce the impact of high and low outliers during analysis periods.
Keltner chains place bands around the developing price in order to gauge volatility and help with directional prediction. The upper and lower bands are calculated as a multiple of the True Average Range (ATR) and are plotted above and below an Exponential Moving Average (EMA). EMA and ATR multipliers can be customized, but 50 and 5 are common settings. Rising prices in the upper band denotes strength while falling prices in the lower band denotes weakness.
These bands represent support and resistance regardless of tilt, with the piercing through the bands generating overbought and oversold trading signals in addition to marking an acceleration in the trend. Horizontal bands exert more support or resistance than higher or lower bands. Price falling in a rising band generates bullish divergence while price rising in a falling band generates bearish divergence.
Ichimoku Clouds, developed by Goichi Hosada in the late 1960s, plots several moving averages above and below price as shaded areas called bullish or bearish “ clouds. ” Five calculations are applied to construct the indicator, generating a cloud that represents the difference between two of the lines. The price above a cloud signals an uptrend while the price below a cloud signals a downtrend. A bullish price swing in a cloud denotes resistance while a bearish price swing in a cloud denotes support.
Clouds also increase or decrease over time, which adds to the versatility of the indicator. Trend signals should be stronger and more reliable when price moves higher above a cloud or lower below a cloud. The two lines of clouds are called “Span A” and “Span B”. The cloud is green in color when Span A is above Span B and red in color when Span A is below Span B. The price above a red cloud signals bullish divergence while the price below d ‘a green cloud signals a bearish divergence.
Historical volatility is shown in a separate pane, unlike most volatility indicators. It measures the distance traveled by the price against a central average during the chosen period. The standard deviation is often used to calculate the indicator, but the variations use other measures. The risk increases when the indicator increases and decreases when it decreases. It is not directional, which means that upward or downward volatility does not specifically favor buy or sell strategies.
The original indicator applied a 10 period and 252 day parameter to measure volatility over a year (252 = average number of trading days in a year). The technician now customizes these entries as well as the standard deviation (SD). It is best to ‘tailor’ the calculation to a security, as the average volatility should differ between different types of instruments and markets. Interpreting historical volatility compares current levels to past levels, looking for highs and lows that can impact profits and losses. It can also be useful to compare the values of highly correlated instruments to discover the “typical” value and hidden discrepancies.
Additional volatility indicators
Beta – measures the volatility of a security in relation to the overall market or to another security.
Bollinger% b – translates the distance between the price and the Bollinger bands into an oscillator plot.
Bollinger bandwidth – calculate the percentage distance between the upper and lower Bollinger bands, seeking to identify high odds turning points.
Jerky clue – measure whether a market is engaged in a trend or a trading range.
Chaikin’s volatility – generates an oscillator that applies the moving average convergence divergence (MACD) to accumulation-distribution rather than to price. A cross above a zero line indicates strength and buildup while a cross below a zero line indicates weakness and distribution.
Donchian Canal – constructs upper, lower and middle ranges by examining extreme prices over a chosen period of time. The highest price over the chosen period marks the high band while the lowest price over the chosen period marks the low band.
Donchian width – measures the price difference between the high and low bands of the Donchian channel.
Fractal Chaos Groups – draw bands around the price action, the slope determining whether the stock is trending or flat.
Moving average spread – measures volatility by examining how the price of an asset has deviated from the selected moving average over time.
Moving average envelope – draw a band over the price, with the upper and lower extremes calculated as a pre-chosen percentage above and below a moving average.
Prime number bands – identifies the highest and lowest prime numbers in a trading range over a period of time and plots production as a band across price.
Relative volatility – is a change in the Relative Strength Index (RSI) which measures the direction of volatility over the specified time period, using standard deviation calculations.
Standard deviation – examines how far the price deviates from a central average price over time.
STARC bands – also known as the Stoller Average Range channel strips, are plotted above and below a simple moving average, highlighting extreme levels that can elicit strong buy or sell signals.
Index of ulcers – predicts the drawdown, depth and duration of asset declines by examining the ups and downs over time.
This article originally appeared on FX Empire