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	0.9481	1.02
αARCH	0.2300	0.04
βGARCH	0.7699	0.15

γ₁	-0.1899	0.00
γ₂	1.1579	0.01
γ₃	-1.2746	-0.01
γ₄	-6.0548	-0.05
γ₅	17.3320	0.06
γ₆	-16.0012	-0.06
γ₇	4.7781	0.05
γ₈	0.9479	0.17
γ₉	-1.6669	-1.56
γ₁₀	1.3943	0.29

Parameter	Value	t-statistic
ω const Unconditional variance weight	0.9481	1.02
α ARCH Response to squared shocks	0.2300	0.04
β GARCH Volatility persistence	0.7699	0.15

γ₁	-0.1899	0.00
γ₂	1.1579	0.01
γ₃	-1.2746	-0.01
γ₄	-6.0548	-0.05
γ₅	17.3320	0.06
γ₆	-16.0012	-0.06
γ₇	4.7781	0.05
γ₈	0.9479	0.17
γ₉	-1.6669	-1.56
γ₁₀	1.3943	0.29

V-Lab

Cherat Papersack Ltd Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other Cherat Papersack Ltd Analyses