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	1.1173	2.74***
αARCH	0.1287	1.81*
βGARCH	0.0968	0.34

γ₁	14.2635	1.40
γ₂	-15.4880	-0.95
γ₃	-1.5749	-0.14
γ₄	-6.4838	-0.77
γ₅	22.7401	2.89***
γ₆	-16.5706	-2.72***
γ₇	-2.8891	-0.50
γ₈	12.4493	1.31
γ₉	-25.8580	-1.25

Parameter	Value	t-statistic
ω const Unconditional variance weight	1.1173	2.74***
α ARCH Response to squared shocks	0.1287	1.81*
β GARCH Volatility persistence	0.0968	0.34

γ₁	14.2635	1.40
γ₂	-15.4880	-0.95
γ₃	-1.5749	-0.14
γ₄	-6.4838	-0.77
γ₅	22.7401	2.89***
γ₆	-16.5706	-2.72***
γ₇	-2.8891	-0.50
γ₈	12.4493	1.31
γ₉	-25.8580	-1.25

V-Lab

CEPS PLC Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other CEPS PLC Analyses