Add to ORKGIn this paper we propose to use Monte Carlo Markov Chain methods to estimate the parameters of Stochastic Volatility Models with several factors varying at differ-ent time scales. The originality of our approach, in contrast with classical one-factor models is the identification of well-separated time scales and the number of these. This is tested with simulated data as well as foreign exchange data. Key words and phrases. Time scales in volatility, Bayesian estimation, MCMC, Foreign exchange. 1
In this paper we model the Gaussian errors in the standard Gaussian linear state space model as stoc...
This paper is concerned with the Bayesian estimation and comparison of flexible, high dimensional mu...
This paper is concerned with the Bayesian estimation and comparison of flexible, high di-mensional m...
[[abstract]]In this paper we propose to use Monte Carlo Markov Chain methods to estimate the paramet...
In the time series analysis of asset prices, the stochastic volatility models have recently attracte...
In the time series analysis of asset prices, the stochastic volatility models have recently attracte...
We address the problem of parameter estimation for diffusion driven sto-chastic volatility models th...
We address the problem of parameter estimation for diffusion driven stochastic volatility models thr...
The stochastic volatility (SV) model is an alternative to GARCH models to model time varying volatil...
This paper presents Markov chain Monte Carlo and importance sampling techniques for volatility estim...
In this paper we model the Gaussian errors in the standard Gaussian linear state space model as stoc...
Stochastic volatility models are important tools for studying the behavior of many financial markets...
This thesis introduces a generalization of the Threshold Stochastic Volatility (THSV) model proposed...
Stochastic volatility models are important tools for studying the behavior of many financial markets...
The impact of parameterisation on the simulation efficiency of Bayesian Markov chain Monte Carlo (MC...
In this paper we model the Gaussian errors in the standard Gaussian linear state space model as stoc...
This paper is concerned with the Bayesian estimation and comparison of flexible, high dimensional mu...
This paper is concerned with the Bayesian estimation and comparison of flexible, high di-mensional m...
[[abstract]]In this paper we propose to use Monte Carlo Markov Chain methods to estimate the paramet...
In the time series analysis of asset prices, the stochastic volatility models have recently attracte...
In the time series analysis of asset prices, the stochastic volatility models have recently attracte...
We address the problem of parameter estimation for diffusion driven sto-chastic volatility models th...
We address the problem of parameter estimation for diffusion driven stochastic volatility models thr...
The stochastic volatility (SV) model is an alternative to GARCH models to model time varying volatil...
This paper presents Markov chain Monte Carlo and importance sampling techniques for volatility estim...
In this paper we model the Gaussian errors in the standard Gaussian linear state space model as stoc...
Stochastic volatility models are important tools for studying the behavior of many financial markets...
This thesis introduces a generalization of the Threshold Stochastic Volatility (THSV) model proposed...
Stochastic volatility models are important tools for studying the behavior of many financial markets...
The impact of parameterisation on the simulation efficiency of Bayesian Markov chain Monte Carlo (MC...
In this paper we model the Gaussian errors in the standard Gaussian linear state space model as stoc...
This paper is concerned with the Bayesian estimation and comparison of flexible, high dimensional mu...
This paper is concerned with the Bayesian estimation and comparison of flexible, high di-mensional m...