Volatility measures the degree of variation in an asset's price over time. It is the most fundamental concept in risk management because it quantifies uncertainty. A stock that moves 5% per day is more volatile than one that moves 0.5% per day, and this difference has profound implications for position sizing, strategy design, and portfolio construction.
Types of volatility
Historical volatility (also called realized volatility) is calculated from past price data. It is typically expressed as the annualized standard deviation of returns. A stock with 20% annualized volatility means that, assuming normally distributed returns, its price is expected to stay within plus or minus 20% of its current value roughly 68% of the time over one year.
Implied volatility is derived from options prices and represents the market's expectation of future volatility. When implied volatility is high, options are expensive because the market expects large price moves. The VIX index, often called the fear gauge, measures implied volatility of S&P 500 options and is widely used as a market sentiment indicator.
Intraday volatility refers to price variation within a single trading session. It is particularly relevant for day trading strategies, where the magnitude and timing of price swings within the day determine profitability.
Why volatility matters for traders
Volatility directly affects position sizing. A common approach is to size positions inversely to volatility so that each trade carries approximately the same dollar risk. If a stock is twice as volatile, the position is half the size. This normalization prevents a single volatile instrument from dominating portfolio risk.
Volatility also determines whether certain strategies are viable. Mean reversion strategies tend to perform better in high-volatility environments where prices deviate further from their average. Trend-following strategies often struggle in low-volatility, range-bound markets where trends fail to develop.
Volatility clustering
Financial markets exhibit a well-documented phenomenon called volatility clustering: periods of high volatility tend to be followed by more high volatility, and periods of low volatility tend to persist. This has practical implications for strategy design. A strategy might reduce position sizes when recent volatility is elevated and increase them during calm periods. GARCH models are commonly used to forecast near-term volatility based on this clustering behavior.
Practical example
A trader manages a portfolio of four stocks with annualized volatilities of 15%, 25%, 40%, and 60%. Using volatility-adjusted position sizing with a target risk of 1% per trade, the position sizes would be inversely proportional to volatility. The 15% volatility stock gets the largest position, while the 60% volatility stock gets the smallest. This ensures that each position contributes roughly equally to portfolio risk.
How Tektii helps
Tektii calculates realized volatility metrics for every backtest, enabling traders to understand how their strategy performs across different volatility regimes. The platform's tick-level data allows precise measurement of intraday volatility, which is critical for strategies that operate at sub-daily frequencies. By providing volatility analytics alongside performance metrics, Tektii helps traders design strategies that account for changing market conditions rather than assuming volatility is constant.