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	1.0646	3.17***
αARCH	0.2059	7.74***
βGARCH	0.7912	29.99***

γ₁	-1.6777	-1.10
γ₂	2.1545	0.71
γ₃	4.7391	0.97
γ₄	-10.0221	-1.72*
γ₅	5.9140	1.64
γ₆	-3.0845	-1.24
γ₇	3.9241	1.28
γ₈	-2.0143	-0.77
γ₉	-0.4248	-0.32
γ₁₀	1.1930	0.82

Parameter	Value	t-statistic
ω const Unconditional variance weight	1.0646	3.17***
α ARCH Response to squared shocks	0.2059	7.74***
β GARCH Volatility persistence	0.7912	29.99***

γ₁	-1.6777	-1.10
γ₂	2.1545	0.71
γ₃	4.7391	0.97
γ₄	-10.0221	-1.72*
γ₅	5.9140	1.64
γ₆	-3.0845	-1.24
γ₇	3.9241	1.28
γ₈	-2.0143	-0.77
γ₉	-0.4248	-0.32
γ₁₀	1.1930	0.82

V-Lab

Cynca Nordic AB Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other Cynca Nordic AB Analyses