Historical volatility is based on statistical analysis of past data and is a statistical measure of how much the price of an underlying asset has deviated from its mean over time, quantifying the magnitude of price fluctuations by calculating the standard deviation of the underlying asset's return. Historical volatility is a fundamental and important concept in risk management and derivative pricing.
How is Historical Volatility calculated?
First from the market to obtain the price of the underlying asset in a fixed period of time, the continuous asset prices to take the logarithmic difference, to obtain a continuous series of returns; then, the standard deviation of these sample values, the obtained that is, historical volatility.
How can Historical Volatility be utilized in trading?
Risk management: Historical volatility is an important indicator of the investment risk of an underlying asset; the higher the historical volatility, the more volatile the price of the underlying asset and the higher the investment risk.
Option Pricing: Historical volatility is a very important input parameter in option pricing models.
Portfolio optimization: By analyzing the historical volatility of the underlying asset, the overall risk of the portfolio can be reduced by diversifying into different underlying assets.
The Flaws of Historical Volatility
Data lag: Historical volatility is calculated based on past data, which does not provide a complete picture of the future volatility of the underlying asset.
Limitations: When calculating historical volatility, the length of the sample period chosen and the different parameters entered can lead to different calculated historical volatility, and traders are unable to capture the impact of some emergencies and market sentiment on volatility through historical volatility.
Advancements in Historical Volatility
Weighted Historical Volatility:Gives more weight to recent data to better reflect current market conditions.
EMA:Weights historical data by exponential decay to capture time-varying characteristics of volatility.
GARCH model:This is a statistical model that models the time-varying and conditional heteroskedasticity of volatility.
