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	0.7420	2.84***
αARCH	0.2957	7.71***
βGARCH	0.6976	18.54***

γ₁	-3.5872	-1.58
γ₂	4.1667	1.28
γ₃	-0.2348	-0.11
γ₄	-1.6053	-0.76
γ₅	4.0106	1.79*
γ₆	-8.1740	-2.43**
γ₇	10.1425	1.95*
γ₈	-7.9949	-1.61
γ₉	8.7572	2.47**

Parameter	Value	t-statistic
ω const Unconditional variance weight	0.7420	2.84***
α ARCH Response to squared shocks	0.2957	7.71***
β GARCH Volatility persistence	0.6976	18.54***

γ₁	-3.5872	-1.58
γ₂	4.1667	1.28
γ₃	-0.2348	-0.11
γ₄	-1.6053	-0.76
γ₅	4.0106	1.79*
γ₆	-8.1740	-2.43**
γ₇	10.1425	1.95*
γ₈	-7.9949	-1.61
γ₉	8.7572	2.47**

V-Lab

Shanghai Dazhong Public Util Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

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