liveBook · Manning

Volatility mean reversion

As explained in Build a Robo-Advisor with Python (From Scratch)

Volatility mean reversion is the tendency of volatility to move back toward a long-run or pre-shock level after a temporary deviation.

Volatility mean reversion describes volatility spikes that eventually move back toward pre-crisis levels. The 2008 VIX episode illustrates implied volatility falling from 80% to 21% over the following year. section 3.2.2

ARCH and GARCH models capture volatility clustering and mean reversion, allowing volatility forecasts after unusually turbulent periods. section 3.2.2

A practical GARCH forecasting workflow:

  • Compute AAPL percentage returns, multiply them by 100 for optimizer convergence, and remove the initial missing value. section 3.2.2
  • Fit a basic GARCH model, forecast 63 trading days, sum the one-day variances, annualize by 252 divided by the horizon, and take the square root. section 3.2.2
  • Interpret the illustrative output as a forecast declining from approximately 9.87 to 9.22 in residual variance, producing 48.10% annualized volatility. section 3.2.2
Figure 3.5 VIX implied volatility around the financial crisis of 2008
figure

View the VIX crisis-period figure showing the temporary volatility spike and subsequent reversion. section 3.2.2

How Build a Robo-Advisor with Python (From Scratch) covers it

How deep it goes
Explains crisis behavior, variance aggregation, and a basic GARCH implementation while deferring more complex model varieties.
Examples
Uses the 2008 VIX spike, AAPL returns, and a 63-trading-day GARCH forecast producing 48.10% annualized volatility.
What you can do after
Apply it via the arch package to fit a basic GARCH model and produce an annualized volatility forecast.
How it connects
Connects volatility mean reversion to GARCH models, ARCH models, volatility clustering, implied volatility, and variance forecasting.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Build a Robo-Advisor with Python (From Scratch). The section links point to the parts of the book it draws on.