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	1.1258	2.97***
αARCH	0.1689	0.98
βGARCH	0.8310	4.85***

γ₁	-2.9649	-0.76
γ₂	6.1468	1.13
γ₃	-5.5780	-2.23**
γ₄	3.4584	2.65***
γ₅	-1.4086	-1.94*
γ₆	-0.1688	-0.23
γ₇	0.9878	1.27
γ₈	-0.4004	-0.43
γ₉	-0.3550	-0.29

Parameter	Value	t-statistic
ω const Unconditional variance weight	1.1258	2.97***
α ARCH Response to squared shocks	0.1689	0.98
β GARCH Volatility persistence	0.8310	4.85***

γ₁	-2.9649	-0.76
γ₂	6.1468	1.13
γ₃	-5.5780	-2.23**
γ₄	3.4584	2.65***
γ₅	-1.4086	-1.94*
γ₆	-0.1688	-0.23
γ₇	0.9878	1.27
γ₈	-0.4004	-0.43
γ₉	-0.3550	-0.29

V-Lab

Post And Telecommunication Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other Post And Telecommunication Analyses