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	0.6302	2.78***
αARCH	0.1508	9.77***
βGARCH	0.8378	55.06***

γ₁	-0.2949	-1.28
γ₂	0.4217	1.09
γ₃	-0.3132	-0.80
γ₄	-0.3529	-0.46
γ₅	1.7316	1.47
γ₆	-1.8561	-1.96*
γ₇	0.8364	2.46**
γ₈	-0.2317	-2.12**
γ₉	0.0657	0.68
γ₁₀	-0.1160	-0.81

Parameter	Value	t-statistic
ω const Unconditional variance weight	0.6302	2.78***
α ARCH Response to squared shocks	0.1508	9.77***
β GARCH Volatility persistence	0.8378	55.06***

γ₁	-0.2949	-1.28
γ₂	0.4217	1.09
γ₃	-0.3132	-0.80
γ₄	-0.3529	-0.46
γ₅	1.7316	1.47
γ₆	-1.8561	-1.96*
γ₇	0.8364	2.46**
γ₈	-0.2317	-2.12**
γ₉	0.0657	0.68
γ₁₀	-0.1160	-0.81

V-Lab

Vienna Insurance Group AG Wiener Versicherung Gruppe Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other Vienna Insurance Group AG Wiener Versicherung Gruppe Analyses