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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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Spline Coefficients

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	126
αARCH	0.0196	4.73***
βGARCH	0.9621	278.46***
γleverage	0.0362	5.22***
λ₁tau intercept	0.0036	0.36
λ₂forecast adj.	0.0000	0.00
λ₃tau persistence	0.9999	183.20***

Parameter	Value	t-statistic
m window Rolling window length	126
α ARCH Response to squared shocks	0.0196	4.73***
β GARCH Volatility persistence	0.9621	278.46***
γ leverage Additional response to negative shocks	0.0362	5.22***
λ₁ tau intercept Baseline long-term coefficient	0.0036	0.36
λ₂ forecast adj. Forecast performance sensitivity	0.0000	0.00
λ₃ tau persistence Long-term factor persistence	0.9999	183.20***

V-Lab

7Y US Treasury 7 Year Zero Coupon Yield Continuously Compounded MF2-GARCH Volatility Analysis

Date Range:

News Impact Curve

Volatility Forecast

Parameter Estimates

Other 7Y US Treasury 7 Year Zero Coupon Yield Continuously Compounded Analyses