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	1.1616	3.25***
αARCH	0.2156	7.08***
βGARCH	0.7819	25.93***

γ₁	-1.8746	-1.12
γ₂	1.8055	0.53
γ₃	6.8071	1.12
γ₄	-12.3810	-1.73*
γ₅	6.7236	1.65*
γ₆	-2.6649	-1.23
γ₇	2.7358	1.21
γ₈	-0.7957	-0.45
γ₉	-0.4904	-0.58
γ₁₀	-0.5607	-0.90

Parameter	Value	t-statistic
ω const Unconditional variance weight	1.1616	3.25***
α ARCH Response to squared shocks	0.2156	7.08***
β GARCH Volatility persistence	0.7819	25.93***

γ₁	-1.8746	-1.12
γ₂	1.8055	0.53
γ₃	6.8071	1.12
γ₄	-12.3810	-1.73*
γ₅	6.7236	1.65*
γ₆	-2.6649	-1.23
γ₇	2.7358	1.21
γ₈	-0.7957	-0.45
γ₉	-0.4904	-0.58
γ₁₀	-0.5607	-0.90

V-Lab

Cynca Nordic AB Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other Cynca Nordic AB Analyses