Add to ORKGinference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processe
Non-Gaussian processes of Ornstein-Uhlenbeck type, or OU processes for short, offer the possibility ...
Barndorff-Nielsen and Shephard (2001) proposed a class of stochastic volatility models in which the ...
This paper introduces the concept of stochastic volatility of volatility in continuous time and, hen...
This paper discusses Bayesian inference for stochastic volatility models based on continuous superpo...
This paper discusses Bayesian inference for stochastic volatility models based on continuous superpo...
Continuous-time stochastic volatility models are becoming an increasingly popular way to describe mo...
Continuous superpositions of Ornstein-Uhlenbeck processes are proposed as a model for asset return v...
Continuous-time stochastic volatility models are becoming an increasingly popular way to describe mo...
We develop Markov chain Monte Carlo methodology for Bayesian inference for non-Gaussian Ornstein-Uhl...
Non-Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important ...
Non-Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important ...
In this thesis we consider a stochastic volatility model based on non-Gaussian Ornstein-Uhlenbeck pr...
We develop Markov chain Monte Carlo methodology for Bayesian inference for non-Gaussian Ornstein–Uhl...
This paper aims to develop new methods for statistical inference in a class of stochastic volatility...
This paper aims to develop new methods for statistical inference in a class of stochastic volatility...
Non-Gaussian processes of Ornstein-Uhlenbeck type, or OU processes for short, offer the possibility ...
Barndorff-Nielsen and Shephard (2001) proposed a class of stochastic volatility models in which the ...
This paper introduces the concept of stochastic volatility of volatility in continuous time and, hen...
This paper discusses Bayesian inference for stochastic volatility models based on continuous superpo...
This paper discusses Bayesian inference for stochastic volatility models based on continuous superpo...
Continuous-time stochastic volatility models are becoming an increasingly popular way to describe mo...
Continuous superpositions of Ornstein-Uhlenbeck processes are proposed as a model for asset return v...
Continuous-time stochastic volatility models are becoming an increasingly popular way to describe mo...
We develop Markov chain Monte Carlo methodology for Bayesian inference for non-Gaussian Ornstein-Uhl...
Non-Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important ...
Non-Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important ...
In this thesis we consider a stochastic volatility model based on non-Gaussian Ornstein-Uhlenbeck pr...
We develop Markov chain Monte Carlo methodology for Bayesian inference for non-Gaussian Ornstein–Uhl...
This paper aims to develop new methods for statistical inference in a class of stochastic volatility...
This paper aims to develop new methods for statistical inference in a class of stochastic volatility...
Non-Gaussian processes of Ornstein-Uhlenbeck type, or OU processes for short, offer the possibility ...
Barndorff-Nielsen and Shephard (2001) proposed a class of stochastic volatility models in which the ...
This paper introduces the concept of stochastic volatility of volatility in continuous time and, hen...