Modeling volatility dynamics using non-Gaussian stochastic volatility model based on band matrix routine

2018 ◽  
Vol 114 ◽  
pp. 193-201 ◽  
Author(s):  
Xiao-Li Gong ◽  
Xi-Hua Liu ◽  
Xiong Xiong ◽  
Xin-Tian Zhuang
Keyword(s):  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Band Matrix ◽  
Model Based ◽  
Volatility Model ◽  
Modeling Volatility ◽  
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 Dynamic Correlation Multivariate Stochastic Volatility Black-Litterman With Latent Factors

2021 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Mihnea S. Andrei ◽  
Sujit K. Ghosh ◽  
Jian Zou
Keyword(s):  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Mcmc Algorithm ◽  
Latent Factors ◽  
Dynamic Correlation ◽  
Confidence Levels ◽  
Volatility Models ◽  
Volatility Model ◽  
Multivariate Stochastic Volatility ◽  
Modeling Volatility

In finance, it is often of interest to study market volatility for portfolios that may consist of a large number of assets using multivariate stochastic volatility models. However, such models, though useful, do not usually incorporate investor views that might be available. In this paper we introduce a novel hierarchical Bayesian methodology of modeling volatility for a large portfolio of assets that incorporates investor’s personal views of the market via the Black-Litterman (BL) model. We extend the scope and use of BL models by using it within a multivariate stochastic volatility model based on latent factors for dimensionality reduction but allows for time varying correlations. Detailed derivations of MCMC algorithm are provided with an illustration with S&P500 asset returns. Moreover, sensitivity analysis for the confidence levels that the investor has in their personal views is also explored. Numerical results show that the proposed method provides flexible interpretation based on the investor’s uncertainty in personal beliefs, and converges to the empirical sample estimate when their confidence level of the market becomes weak.

scholarly journals Pricing Parisian Option under a Stochastic Volatility Model

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Min-Ku Lee ◽  
Kyu-Hwan Jang
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Barrier Options ◽  
Barrier Option ◽  
Partial Differential ◽  
Model Based ◽  
Volatility Model ◽  
Black Scholes

We study the pricing of a Parisian option under a stochastic volatility model. Based on the manipulation problem that barrier options might create near barriers, the Parisian option has been designed as an extended barrier option. A stochastic volatility correction to the Black-Scholes price of the Parisian option is obtained in a partial differential equation form and the solution is characterized numerically.

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