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	0.9749	5.08***
αARCH	0.1090	6.13***
βGARCH	0.8643	34.76***

γ₁	1.8360	4.71***
γ₂	-3.0922	-5.01***
γ₃	2.0314	5.58***
γ₄	-1.4286	-4.99***
γ₅	1.0869	2.83***
γ₆	-0.7069	-1.86*
γ₇	-0.0646	-0.16
γ₈	0.9625	1.97**
γ₉	-0.6587	-1.70*
γ₁₀	-0.6973	-1.38

Parameter	Value	t-statistic
ω const Unconditional variance weight	0.9749	5.08***
α ARCH Response to squared shocks	0.1090	6.13***
β GARCH Volatility persistence	0.8643	34.76***

γ₁	1.8360	4.71***
γ₂	-3.0922	-5.01***
γ₃	2.0314	5.58***
γ₄	-1.4286	-4.99***
γ₅	1.0869	2.83***
γ₆	-0.7069	-1.86*
γ₇	-0.0646	-0.16
γ₈	0.9625	1.97**
γ₉	-0.6587	-1.70*
γ₁₀	-0.6973	-1.38

V-Lab

Disa India Ltd Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

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