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This paper is concerned with the Bayesian analysis of stochastic volatility (SV) models with leverage. Specifically, the paper shows how the often used Kim et al. (1998) method that was developed for SV models without leverage can be extended to models with leverage. The approach relies on the novel idea of approximating the joint distribution of the… (More)

- Shyjan Mahamud, Martial Hebert, Yasuhiro Omori, Jean Ponce
- CVPR
- 2001

Astract: The estimation of the projective structure of a scene from image correspondences can be formulated as the minimization of the mean-squared distance between predicted and observed image points with respect to the projection matrices, the scene point positions, and their depths. Since these unknowns are not independent, constraints must be chosen to… (More)

We provide a detailed summary of the large and vibrant emerging literature that deals with the multivariate modeling of conditional volatility of financial time series within the framework of stochastic volatility. The developments and achievements in this area represent one of the great success stories of financial econometrics. Three broad classes of… (More)

Kim, Shephard, and Chib (1998) provided a Bayesian analysis of stochastic volatility models based on a fast and reliable Markov chain Monte Carlo (MCMC) algorithm. Their method ruled out the leverage effect, which is known to be important in applications. Despite this, their basic method has been extensively used in the financial economics literature and… (More)

- Jouchi Nakajima, Yasuhiro Omori
- Computational Statistics & Data Analysis
- 2009

This paper proposes the efficient and fast Markov chain Monte Carlo estimation methods for the stochastic volatility model with leverage effects, heavy-tailed errors and jump components, and for the stochastic volatility model with correlated jumps. We illustrate our method using simulated data and analyze daily stock returns data on S&P500 index and TOPIX.… (More)

- Makoto Takahashi, Yasuhiro Omori, Toshiaki Watanabe
- Computational Statistics & Data Analysis
- 2009

- Yasuhiro Omori, Toshiaki Watanabe
- Computational Statistics & Data Analysis
- 2008

This article introduces a new efficient simulation smoother and disturbance smoother for asymmetric stochastic volatility models where there exists a correlation between today’s return and tomorrow’s volatility. The state vector is divided into several blocks where each block consists of many state variables. For each block, corresponding disturbances are… (More)

- Yasuhiro Omori, Koji Miyawaki
- Computational Statistics & Data Analysis
- 2010

Tobit models are extended to allow threshold values which depend on individuals’ characteristics. In such models, the parameters are subject to as many inequality constraints as the number of observations, and the maximum likelihood estimation which requires the numerical maximisation of the likelihood is often difficult to be implemented. Using a Bayesian… (More)

- Jouchi Nakajima, Yasuhiro Omori
- Computational Statistics & Data Analysis
- 2012

Bayesian analysis of a stochastic volatility model with a generalized hyperbolic (GH) skew Student’s t-error distribution is described where we first consider an asymmetric heavy-tailness as well as leverage effects. An efficient Markov chain Monte Carlo estimation method is described exploiting a normal variance-mean mixture representation of the error… (More)

- Shinichiro Shirota, Takayuki Hizu, Yasuhiro Omori
- Computational Statistics & Data Analysis
- 2014

! ! The daily return and the realized volatility are simultaneously modeled in the stochastic volatility model with leverage and long memory. In addition to the stochastic volatility model with leverage for the daily returns, ARFIMA process is jointly considered for the realized volatilities. Using a state space representation of the model, we estimate… (More)