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	2.3715	2.75***
αARCH	0.2378	8.64***
βGARCH	0.7415	28.41***

γ₁	-5.3726	-1.44
γ₂	13.2354	1.68*
γ₃	-12.1752	-2.02**
γ₄	5.7482	2.80***
γ₅	-3.0154	-3.47***
γ₆	2.6948	3.64***
γ₇	-2.6084	-5.74***
γ₈	3.1879	2.61***
γ₉	-2.9018	-2.01**

Parameter	Value	t-statistic
ω const Unconditional variance weight	2.3715	2.75***
α ARCH Response to squared shocks	0.2378	8.64***
β GARCH Volatility persistence	0.7415	28.41***

γ₁	-5.3726	-1.44
γ₂	13.2354	1.68*
γ₃	-12.1752	-2.02**
γ₄	5.7482	2.80***
γ₅	-3.0154	-3.47***
γ₆	2.6948	3.64***
γ₇	-2.6084	-5.74***
γ₈	3.1879	2.61***
γ₉	-2.9018	-2.01**

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