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	2.0215	3.66***
αARCH	0.1397	8.05***
βGARCH	0.8585	47.59***

γ₁	0.3891	0.25
γ₂	-1.1817	-0.53
γ₃	1.5072	1.09
γ₄	-1.8451	-1.64
γ₅	1.4178	1.51
γ₆	0.8091	0.76
γ₇	-2.3881	-1.50
γ₈	2.0770	1.03
γ₉	-2.0800	-0.90
γ₁₀	4.9367	1.77*

Parameter	Value	t-statistic
ω const Unconditional variance weight	2.0215	3.66***
α ARCH Response to squared shocks	0.1397	8.05***
β GARCH Volatility persistence	0.8585	47.59***

γ₁	0.3891	0.25
γ₂	-1.1817	-0.53
γ₃	1.5072	1.09
γ₄	-1.8451	-1.64
γ₅	1.4178	1.51
γ₆	0.8091	0.76
γ₇	-2.3881	-1.50
γ₈	2.0770	1.03
γ₉	-2.0800	-0.90
γ₁₀	4.9367	1.77*

V-Lab

Maskapai Reasuransi Spline ILLIQ Liquidity Analysis

Date Range:

News Impact Curve

Illiquidity Forecast

Parameter Estimates

Other Maskapai Reasuransi Analyses