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	3.5884	3.70***
αARCH	0.2291	8.36***
βGARCH	0.7452	28.70***

γ₁	-2.3248	-0.61
γ₂	7.0071	0.87
γ₃	-7.7357	-1.26
γ₄	4.6540	2.14**
γ₅	-3.3059	-4.33***
γ₆	2.9002	4.75***
γ₇	-2.6526	-5.68***
γ₈	2.9933	2.54**
γ₉	-2.4736	-1.78*

Parameter	Value	t-statistic
ω const Unconditional variance weight	3.5884	3.70***
α ARCH Response to squared shocks	0.2291	8.36***
β GARCH Volatility persistence	0.7452	28.70***

γ₁	-2.3248	-0.61
γ₂	7.0071	0.87
γ₃	-7.7357	-1.26
γ₄	4.6540	2.14**
γ₅	-3.3059	-4.33***
γ₆	2.9002	4.75***
γ₇	-2.6526	-5.68***
γ₈	2.9933	2.54**
γ₉	-2.4736	-1.78*

V-Lab

GPI for Urban Growth Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other GPI for Urban Growth Analyses