scholarly journals A latent‐factor‐driven endogenous regime‐switching non‐Gaussian model: Evidence from simulation and application

2020 ◽  
Author(s):  
Ruijun Bu ◽  
Jie Cheng ◽  
Fredj Jawadi
Keyword(s):  
Regime Switching ◽  
Gaussian Model ◽  
Latent Factor ◽  
Model Evidence ◽  
Non Gaussian

A non-Gaussian model in polymeric network

2006 ◽  
Vol 20 (4) ◽  
pp. 395-399 ◽  
Author(s):  
L. C. Malacarne ◽  
R. S. Mendes ◽  
E. K. Lenzi ◽  
S. Picoli ◽  
J. P. Dal Molin
Keyword(s):  
Gaussian Model ◽  
Polymeric Network ◽  
Non Gaussian

HISTORICAL VOLATILITY DISTRIBUTION IN GAUSSIAN AND GARCH(1,1) MODELS

2000 ◽  
Vol 03 (03) ◽  
pp. 417-417 ◽  
Author(s):  
LUTZ MOLGEDEY
Keyword(s):  
Experimental Data ◽  
Finite Time ◽  
Time Window ◽  
Gaussian Model ◽  
Local Variance ◽  
Non Gaussian

On experimental data the historical volatility is usually calculated by averaging the local variance (or its generalizations) over a finite time window. Already in the case of a constant volatility in the Gaussian model the resulting historical volatility is non-Gaussian distributed. We will calculate historical volatility distributions in the Gaussian and GARCH(1,1) model for different time window sizes and compare them with those obtained from the S&P500 data [1].

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