CBOE Volatility Index

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How volatility evolves over time

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{name} Model Equation

How returns affect tomorrow's volatility

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Value at Risk (VaR) estimates the maximum loss at a given confidence level. Expected Shortfall (ES) estimates the average loss beyond VaR.

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Param	Value	t-stat
mwindow	66
αARCH	0.3402	4.30***
βGARCH	0.1131	1.77*
γleverage	0.0138	0.12
λ₁tau intercept	0.6355	0.84
λ₂forecast adj.	0.0216	1.52
λ₃tau persistence	0.9683	43.50***

Parameter	Value	t-statistic
m window Rolling window length	66
α ARCH Response to squared shocks	0.3402	4.30***
β GARCH Volatility persistence	0.1131	1.77*
γ leverage Additional response to negative shocks	0.0138	0.12
λ₁ tau intercept Baseline long-term coefficient	0.6355	0.84
λ₂ forecast adj. Forecast performance sensitivity	0.0216	1.52
λ₃ tau persistence Long-term factor persistence	0.9683	43.50***

V-Lab

CBOE Google Volatility Index MF2-GARCH Volatility Analysis

Date Range:

News Impact Curve

Volatility Forecast

Parameter Estimates

Other CBOE Google Volatility Index Analyses