How volatility evolves over time

How illiquidity evolves over time

{name} Model Equation

How returns affect tomorrow's volatility

How returns affect tomorrow's illiquidity

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
ωconst	5.2442	2.68***
αARCH	0.1165	4.78***
βGARCH	0.7796	14.87***

γ₁	-1.6863	-1.32
γ₂	4.0023	1.98**
γ₃	-3.3407	-2.91***
γ₄	1.3820	2.20**
γ₅	-0.1808	-0.42
γ₆	-0.3905	-1.18
γ₇	0.3356	1.06
γ₈	-0.4409	-1.29
γ₉	0.7071	1.90*
γ₁₀	-0.9741	-1.77*

Parameter	Value	t-statistic
ω const Unconditional variance weight	5.2442	2.68***
α ARCH Response to squared shocks	0.1165	4.78***
β GARCH Volatility persistence	0.7796	14.87***

γ₁	-1.6863	-1.32
γ₂	4.0023	1.98**
γ₃	-3.3407	-2.91***
γ₄	1.3820	2.20**
γ₅	-0.1808	-0.42
γ₆	-0.3905	-1.18
γ₇	0.3356	1.06
γ₈	-0.4409	-1.29
γ₉	0.7071	1.90*
γ₁₀	-0.9741	-1.77*

V-Lab

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Date Range:

News Impact Curve

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Parameter Estimates

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