Bad Environments Good Environments: A Non-Gaussian Asymmetric Volatility Model

2014 ◽  
Author(s):  
Geert Bekaert ◽  
Eric C. Engstrom ◽  
Andrey Ermolov
Keyword(s):  
Asymmetric Volatility ◽  
Volatility Model ◽  
Non Gaussian

Bad environments, good environments: A non-Gaussian asymmetric volatility model

2015 ◽  
Vol 186 (1) ◽  
pp. 258-275 ◽  
Author(s):  
Geert Bekaert ◽  
Eric Engstrom ◽  
Andrey Ermolov
Keyword(s):  
Asymmetric Volatility ◽  
Volatility Model ◽  
Non Gaussian

A non-Gaussian stochastic volatility model

1998 ◽  
Vol 2 (2) ◽  
pp. 33-47 ◽  
Author(s):  
Yuichi Nagahara ◽  
Genshiro Kitagawa
Keyword(s):  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Volatility Model ◽  
Non Gaussian

scholarly journals Pricing of Forwards and Options in a Multivariate Non-Gaussian Stochastic Volatility Model for Energy Markets

2013 ◽  
Vol 45 (2) ◽  
pp. 572-594 ◽  
Author(s):  
F. E. Benth ◽  
L. Vos
Keyword(s):  
Stochastic Volatility ◽  
Energy Markets ◽  
Stochastic Volatility Model ◽  
Spot Price ◽  
Price Model ◽  
Forward Price ◽  
Volatility Model ◽  
Characteristic Features ◽  
Hump Shape ◽  
Non Gaussian

In Benth and Vos (2013) we introduced a multivariate spot price model with stochastic volatility for energy markets which captures characteristic features, such as price spikes, mean reversion, stochastic volatility, and inverse leverage effect as well as dependencies between commodities. In this paper we derive the forward price dynamics based on our multivariate spot price model, providing a very flexible structure for the forward curves, including contango, backwardation, and hump shape. Moreover, a Fourier transform-based method to price options on the forward is described.

scholarly journals Exact Maximum Likelihood and Bayesian Estimation of the Stochastic Volatility Model

2003 ◽  
Vol 23 (2) ◽  
pp. 183
Author(s):  
Anderson C. O. Motta ◽  
Luiz K. Hotta
Keyword(s):  
Maximum Likelihood ◽  
State Space ◽  
Stochastic Volatility ◽  
Stock Exchange ◽  
Estimation Procedure ◽  
Stochastic Volatility Model ◽  
Gaussian State ◽  
Volatility Model ◽  
Non Gaussian ◽  
Exact Maximum Likelihood

This paper considers the classical and Bayesian approaches to the estimation of the stochastic volatility (SV) model. The estimation procedures rely heavily on the fact that SV model can be written in the State Space Form (SSF) with non-Ga ussian disturbances. The first widely employed estimation procedure to use this model was the quasi-maximum likelihood estimator proposed by Harvey et al. The Bayesian approach was proposed by Jacquier et al.(1994). Lately, many papers have appeared in the literature dealing with non-Gaussian state space models which directly influenced the estimation of the SV model. Some of these methods proposed to estimate the SV model are compared using the Sao Paulo stock exchange index (IBOVESPA) and simulated series. The influence of outliers is also considered.

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