Add to ORKGThe present work focuses on the inference in stochastic volatility models. More precisely, estimation of suitable functions of the mean vector of the increment stock price is performed without estimating in advance the parameters of the model. A moving block bootstrap (MBB) approach is then suggested in order to estimate the variance of those functions and properties of the involved estimators are discussed. Simulations on the model are also performe
This paper is concerned with simulation-based inference in generalized models of stochastic volatili...
The prediction-based estimating functions proposed by (Sørensen, 1999) are generalized to facilitate...
In this PhD. Thesis we focus on diffusion models. Diffusions are very attractive and widely applied ...
The present work focuses on the inference in stochastic volatility models. More precisely, estimatio...
The present work focuses on the inference in stochastic volatility models. More precisely, estimatio...
This paper focuses on the inference of suitable generally non linear functions in stochastic volatil...
This paper focuses on the inference of suitable generally non linear functions in stochastic volatil...
This paper focuses on the inference of suitable generally non linear functions in stochastic volatil...
This paper focuses on the inference of suitable generally non linear functions in stochastic volatil...
This paper focuses on the inference of suitable generally non linear functions in stochastic volatil...
In this paper a new method is proposed for estimation of parameters in diffusion processes from disc...
First draft: August 1997In this paper a new method is proposed for estimation of parameters in diffu...
We consider a Black-Scholes type model, but with volatility being a Markov Chain process. Assuming t...
This thesis consists of five papers (Paper A-E) on statistical modeling of diffusion processes. Two ...
We propose a simple, general and computationally efficient algorithm for maximum likelihood estima- ...
This paper is concerned with simulation-based inference in generalized models of stochastic volatili...
The prediction-based estimating functions proposed by (Sørensen, 1999) are generalized to facilitate...
In this PhD. Thesis we focus on diffusion models. Diffusions are very attractive and widely applied ...
The present work focuses on the inference in stochastic volatility models. More precisely, estimatio...
The present work focuses on the inference in stochastic volatility models. More precisely, estimatio...
This paper focuses on the inference of suitable generally non linear functions in stochastic volatil...
This paper focuses on the inference of suitable generally non linear functions in stochastic volatil...
This paper focuses on the inference of suitable generally non linear functions in stochastic volatil...
This paper focuses on the inference of suitable generally non linear functions in stochastic volatil...
This paper focuses on the inference of suitable generally non linear functions in stochastic volatil...
In this paper a new method is proposed for estimation of parameters in diffusion processes from disc...
First draft: August 1997In this paper a new method is proposed for estimation of parameters in diffu...
We consider a Black-Scholes type model, but with volatility being a Markov Chain process. Assuming t...
This thesis consists of five papers (Paper A-E) on statistical modeling of diffusion processes. Two ...
We propose a simple, general and computationally efficient algorithm for maximum likelihood estima- ...
This paper is concerned with simulation-based inference in generalized models of stochastic volatili...
The prediction-based estimating functions proposed by (Sørensen, 1999) are generalized to facilitate...
In this PhD. Thesis we focus on diffusion models. Diffusions are very attractive and widely applied ...