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	0.5962	1.58
αARCH	0.1085	5.04***
βGARCH	0.8769	48.14***

γ₁	-2.6307	-1.47
γ₂	4.2010	1.48
γ₃	-1.9341	-1.20
γ₄	0.8402	0.89
γ₅	-2.4892	-4.09***
γ₆	3.3338	7.23***
γ₇	-0.9935	-2.49**
γ₈	-0.4722	-1.40
γ₉	0.0531	0.18
γ₁₀	0.3418	0.82

Parameter	Value	t-statistic
ω const Unconditional variance weight	0.5962	1.58
α ARCH Response to squared shocks	0.1085	5.04***
β GARCH Volatility persistence	0.8769	48.14***

γ₁	-2.6307	-1.47
γ₂	4.2010	1.48
γ₃	-1.9341	-1.20
γ₄	0.8402	0.89
γ₅	-2.4892	-4.09***
γ₆	3.3338	7.23***
γ₇	-0.9935	-2.49**
γ₈	-0.4722	-1.40
γ₉	0.0531	0.18
γ₁₀	0.3418	0.82

V-Lab

Trisul Sa Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other Trisul Sa Analyses