Bayesian Inference for a Periodic Stochastic Volatility Model of Intraday Electricity Prices

2009 ◽  
pp. 353-376 ◽  
Author(s):  
Michael Stanley Smith
Keyword(s):  
Bayesian Inference ◽  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Electricity Prices ◽  
Volatility Model

scholarly journals BAYESIAN INFERENCE OF STOCHASTIC VOLATILITY MODEL BY HYBRID MONTE CARLO

2009 ◽  
Vol 18 (08) ◽  
pp. 1381-1396 ◽  
Author(s):  
TETSUYA TAKAISHI
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Bayesian Inference ◽  
Stochastic Volatility ◽  
Metropolis Algorithm ◽  
Stochastic Volatility Model ◽  
Financial Data ◽  
Stock Index ◽  
Hybrid Monte Carlo ◽  
Volatility Model

The hybrid Monte Carlo (HMC) algorithm is applied for the Bayesian inference of the stochastic volatility (SV) model. We use the HMC algorithm for the Markov chain Monte Carlo updates of volatility variables of the SV model. First we compute parameters of the SV model by using the artificial financial data and compare the results from the HMC algorithm with those from the Metropolis algorithm. We find that the HMC algorithm decorrelates the volatility variables faster than the Metropolis algorithm. Second we make an empirical study for the time series of the Nikkei 225 stock index by the HMC algorithm. We find the similar correlation behavior for the sampled data to the results from the artificial financial data and obtain a ϕ value close to one (ϕ ≈ 0.977), which means that the time series has the strong persistency of the volatility shock.

scholarly journals “Exact” and Approximate Methods for Bayesian Inference: Stochastic Volatility Case Study

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 466
Author(s):  
Yuliya Shapovalova
Keyword(s):  
Bayesian Inference ◽  
Stochastic Volatility ◽  
Particle Filtering ◽  
Point Of View ◽  
Stochastic Volatility Model ◽  
Approximate Methods ◽  
Volatility Model ◽  
Multivariate Stochastic Volatility ◽  
Important Practical Application

We conduct a case study in which we empirically illustrate the performance of different classes of Bayesian inference methods to estimate stochastic volatility models. In particular, we consider how different particle filtering methods affect the variance of the estimated likelihood. We review and compare particle Markov Chain Monte Carlo (MCMC), RMHMC, fixed-form variational Bayes, and integrated nested Laplace approximation to estimate the posterior distribution of the parameters. Additionally, we conduct the review from the point of view of whether these methods are (1) easily adaptable to different model specifications; (2) adaptable to higher dimensions of the model in a straightforward way; (3) feasible in the multivariate case. We show that when using the stochastic volatility model for methods comparison, various data-generating processes have to be considered to make a fair assessment of the methods. Finally, we present a challenging specification of the multivariate stochastic volatility model, which is rarely used to illustrate the methods but constitutes an important practical application.

FUZZY UNCERTAINTY IN THE HESTON STOCHASTIC VOLATILITY MODEL

2011 ◽  
Vol 16 (02) ◽  
Author(s):  
G. Figà-Talamanca ◽  
M. L. Guerra ◽  
L. Stefanini
Keyword(s):  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Fuzzy Uncertainty ◽  
Volatility Model

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
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