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	1.6819	1.17
αARCH	0.3396	6.30***
βGARCH	0.6471	15.62***

γ₁	-2.3264	-3.22***
γ₂	4.6438	4.38***
γ₃	-4.4999	-4.85***
γ₄	3.2606	2.26**
γ₅	-1.2480	-0.75
γ₆	-1.4589	-0.87
γ₇	4.0225	2.35**
γ₈	-3.6518	-2.87***
γ₉	1.8878	2.67***
γ₁₀	-0.8043	-1.17

Parameter	Value	t-statistic
ω const Unconditional variance weight	1.6819	1.17
α ARCH Response to squared shocks	0.3396	6.30***
β GARCH Volatility persistence	0.6471	15.62***

γ₁	-2.3264	-3.22***
γ₂	4.6438	4.38***
γ₃	-4.4999	-4.85***
γ₄	3.2606	2.26**
γ₅	-1.2480	-0.75
γ₆	-1.4589	-0.87
γ₇	4.0225	2.35**
γ₈	-3.6518	-2.87***
γ₉	1.8878	2.67***
γ₁₀	-0.8043	-1.17

V-Lab

Qatari German Co for Medical Devices QPSC Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other Qatari German Co for Medical Devices QPSC Analyses