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	1.8495	3.21***
αARCH	0.1238	4.38***
βGARCH	0.8483	21.08***

γ₁	0.5012	0.86
γ₂	-1.0546	-0.87
γ₃	0.9442	0.80
γ₄	-0.4731	-0.53
γ₅	-0.6387	-0.99
γ₆	2.5914	3.79***
γ₇	-3.3753	-3.70***
γ₈	1.3870	1.62
γ₉	0.0898	0.14
γ₁₀	1.0211	1.82*

Parameter	Value	t-statistic
ω const Unconditional variance weight	1.8495	3.21***
α ARCH Response to squared shocks	0.1238	4.38***
β GARCH Volatility persistence	0.8483	21.08***

γ₁	0.5012	0.86
γ₂	-1.0546	-0.87
γ₃	0.9442	0.80
γ₄	-0.4731	-0.53
γ₅	-0.6387	-0.99
γ₆	2.5914	3.79***
γ₇	-3.3753	-3.70***
γ₈	1.3870	1.62
γ₉	0.0898	0.14
γ₁₀	1.0211	1.82*

V-Lab

Grimoldi SA Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other Grimoldi SA Analyses