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	0.6226	3.66***
αARCH	0.2106	2.06**
βGARCH	0.3706	1.74*

γ₁	-30.7660	-1.36
γ₂	42.5192	1.10
γ₃	-29.3324	-1.09
γ₄	39.7554	2.05**
γ₅	-44.4854	-3.00***
γ₆	32.5548	3.68***
γ₇	-10.8158	-0.97
γ₈	6.2520	0.63
γ₉	-7.8463	-0.87
γ₁₀	-4.5862	-0.34

Parameter	Value	t-statistic
ω const Unconditional variance weight	0.6226	3.66***
α ARCH Response to squared shocks	0.2106	2.06**
β GARCH Volatility persistence	0.3706	1.74*

γ₁	-30.7660	-1.36
γ₂	42.5192	1.10
γ₃	-29.3324	-1.09
γ₄	39.7554	2.05**
γ₅	-44.4854	-3.00***
γ₆	32.5548	3.68***
γ₇	-10.8158	-0.97
γ₈	6.2520	0.63
γ₉	-7.8463	-0.87
γ₁₀	-4.5862	-0.34

V-Lab

Hedge Recebiveis FII Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other Hedge Recebiveis FII Analyses