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
mwindow	76
αARCH	0.1047	10.07***
βGARCH	0.8996	87.08***
γleverage	-0.0453	-6.07***
λ₁tau intercept	0.1265	0.12
λ₂forecast adj.	0.0000	0.00
λ₃tau persistence	0.9986	491.46***

Parameter	Value	t-statistic
m window Rolling window length	76
α ARCH Response to squared shocks	0.1047	10.07***
β GARCH Volatility persistence	0.8996	87.08***
γ leverage Additional response to negative shocks	-0.0453	-6.07***
λ₁ tau intercept Baseline long-term coefficient	0.1265	0.12
λ₂ forecast adj. Forecast performance sensitivity	0.0000	0.00
λ₃ tau persistence Long-term factor persistence	0.9986	491.46***

V-Lab

Log-In Logistica Intermodal ILLIQ-MFMEM Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other Log-In Logistica Intermodal Analyses