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	1.3668	0.59
αARCH	0.1494	0.09
βGARCH	0.8506	0.54

γ₁	0.3336	0.05
γ₂	-1.0721	-0.10
γ₃	0.0345	0.00
γ₄	1.9082	0.04
γ₅	-0.0121	0.00
γ₆	-2.8219	-0.04
γ₇	2.2425	0.07
γ₈	-0.8023	-0.70
γ₉	-0.1103	-0.06

Parameter	Value	t-statistic
ω const Unconditional variance weight	1.3668	0.59
α ARCH Response to squared shocks	0.1494	0.09
β GARCH Volatility persistence	0.8506	0.54

γ₁	0.3336	0.05
γ₂	-1.0721	-0.10
γ₃	0.0345	0.00
γ₄	1.9082	0.04
γ₅	-0.0121	0.00
γ₆	-2.8219	-0.04
γ₇	2.2425	0.07
γ₈	-0.8023	-0.70
γ₉	-0.1103	-0.06

V-Lab

Axactor Asa Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other Axactor Asa Analyses