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	2.3071	3.06***
αARCH	0.2361	14.99***
βGARCH	0.7593	50.88***

γ₁	-0.1564	-0.83
γ₂	0.4484	1.84*
γ₃	-0.6049	-5.57***
γ₄	0.5549	5.34***
γ₅	-0.4086	-3.23***
γ₆	0.3436	1.85*
γ₇	-0.2777	-1.04
γ₈	0.1797	0.65
γ₉	-0.3679	-1.67*
γ₁₀	0.5937	3.04***

Parameter	Value	t-statistic
ω const Unconditional variance weight	2.3071	3.06***
α ARCH Response to squared shocks	0.2361	14.99***
β GARCH Volatility persistence	0.7593	50.88***

γ₁	-0.1564	-0.83
γ₂	0.4484	1.84*
γ₃	-0.6049	-5.57***
γ₄	0.5549	5.34***
γ₅	-0.4086	-3.23***
γ₆	0.3436	1.85*
γ₇	-0.2777	-1.04
γ₈	0.1797	0.65
γ₉	-0.3679	-1.67*
γ₁₀	0.5937	3.04***

V-Lab

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Date Range:

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Illiquidity Forecast

Parameter Estimates

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