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	0.7851	3.67***
αARCH	0.2136	7.55***
βGARCH	0.7846	28.28***

γ₁	-3.8529	-1.77*
γ₂	6.1136	1.50
γ₃	2.8706	0.54
γ₄	-10.3867	-1.66*
γ₅	6.3695	1.68*
γ₆	-2.9493	-1.24
γ₇	3.4587	1.26
γ₈	-1.4190	-0.62
γ₉	-0.9425	-0.74
γ₁₀	1.8876	0.97

Parameter	Value	t-statistic
ω const Unconditional variance weight	0.7851	3.67***
α ARCH Response to squared shocks	0.2136	7.55***
β GARCH Volatility persistence	0.7846	28.28***

γ₁	-3.8529	-1.77*
γ₂	6.1136	1.50
γ₃	2.8706	0.54
γ₄	-10.3867	-1.66*
γ₅	6.3695	1.68*
γ₆	-2.9493	-1.24
γ₇	3.4587	1.26
γ₈	-1.4190	-0.62
γ₉	-0.9425	-0.74
γ₁₀	1.8876	0.97

V-Lab

Cynca Nordic AB Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other Cynca Nordic AB Analyses