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	0.9072	3.96***
αARCH	0.2166	7.60***
βGARCH	0.7824	27.81***

γ₁	-4.2679	-1.80*
γ₂	6.7471	1.53
γ₃	2.8661	0.52
γ₄	-10.8012	-1.67*
γ₅	6.5648	1.69*
γ₆	-2.8576	-1.24
γ₇	3.1813	1.23
γ₈	-1.0379	-0.48
γ₉	-1.7047	-1.10
γ₁₀	3.9135	1.10

Parameter	Value	t-statistic
ω const Unconditional variance weight	0.9072	3.96***
α ARCH Response to squared shocks	0.2166	7.60***
β GARCH Volatility persistence	0.7824	27.81***

γ₁	-4.2679	-1.80*
γ₂	6.7471	1.53
γ₃	2.8661	0.52
γ₄	-10.8012	-1.67*
γ₅	6.5648	1.69*
γ₆	-2.8576	-1.24
γ₇	3.1813	1.23
γ₈	-1.0379	-0.48
γ₉	-1.7047	-1.10
γ₁₀	3.9135	1.10

V-Lab

Cynca Nordic AB Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other Cynca Nordic AB Analyses