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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	21
αARCH	0.0150	0.77
βGARCH	0.9881	4,411.33***
γleverage	-0.0062	-0.16
λ₁tau intercept	0.0140	1.24
λ₂forecast adj.	0.2385	0.47
λ₃tau persistence	0.0001	0.00

Parameter	Value	t-statistic
m window Rolling window length	21
α ARCH Response to squared shocks	0.0150	0.77
β GARCH Volatility persistence	0.9881	4,411.33***
γ leverage Additional response to negative shocks	-0.0062	-0.16
λ₁ tau intercept Baseline long-term coefficient	0.0140	1.24
λ₂ forecast adj. Forecast performance sensitivity	0.2385	0.47
λ₃ tau persistence Long-term factor persistence	0.0001	0.00

V-Lab

US Dollar to Chinese Renminbi MF2-GARCH Volatility Analysis

Date Range:

News Impact Curve

Volatility Forecast

Parameter Estimates

Other US Dollar to Chinese Renminbi Analyses