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	0.8127	5.17***
αARCH	0.2891	3.66***
βGARCH	0.7107	8.99***

γ₁	2.4661	1.67*
γ₂	-3.9915	-2.01**
γ₃	0.6823	0.93
γ₄	2.6156	4.23***
γ₅	-5.9648	-7.65***
γ₆	8.7082	13.34***
γ₇	-5.1008	-7.30***
γ₈	-1.3157	-1.53
γ₉	1.6424	0.59

Parameter	Value	t-statistic
ω const Unconditional variance weight	0.8127	5.17***
α ARCH Response to squared shocks	0.2891	3.66***
β GARCH Volatility persistence	0.7107	8.99***

γ₁	2.4661	1.67*
γ₂	-3.9915	-2.01**
γ₃	0.6823	0.93
γ₄	2.6156	4.23***
γ₅	-5.9648	-7.65***
γ₆	8.7082	13.34***
γ₇	-5.1008	-7.30***
γ₈	-1.3157	-1.53
γ₉	1.6424	0.59

V-Lab

Supra Boga Lestari Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other Supra Boga Lestari Analyses