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	2.9217	3.13***
αARCH	0.2017	6.27***
βGARCH	0.6786	15.37***

γ₁	-7.9064	-4.31***
γ₂	12.0038	4.39***
γ₃	-1.6461	-0.70
γ₄	-5.5750	-2.74***
γ₅	5.1095	2.85***
γ₆	-1.5781	-0.97
γ₇	-0.9471	-0.55
γ₈	1.9581	1.03
γ₉	-3.4071	-1.09

Parameter	Value	t-statistic
ω const Unconditional variance weight	2.9217	3.13***
α ARCH Response to squared shocks	0.2017	6.27***
β GARCH Volatility persistence	0.6786	15.37***

γ₁	-7.9064	-4.31***
γ₂	12.0038	4.39***
γ₃	-1.6461	-0.70
γ₄	-5.5750	-2.74***
γ₅	5.1095	2.85***
γ₆	-1.5781	-0.97
γ₇	-0.9471	-0.55
γ₈	1.9581	1.03
γ₉	-3.4071	-1.09

V-Lab

FII Vida Nova Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other FII Vida Nova Analyses