© 2015 Elsevier B.V. This paper develops a specification test for stochastic volatility models by comparing the nonparametric kernel deconvolution density estimator of an integrated volatility density with its parametric counterpart. L2 distance is used to measure the discrepancy. The asymptotic null distributions of the test statistics are established and the asymptotic power functions are computed. Through Monte Carlo simulations, the size and power properties of the test statistics are studied. The tests are applied to an empirical example
We consider a continuous time stochastic volatility model. The model contains a stationary volatilit...
In this paper we present two new tests for the parametric form of the variance function in diffusion...
This paper proposes a testing procedure in order to distinguish between the case where the volatilit...
This paper develops a specification test for stochastic volatility models by comparing the nonparame...
© 2016 Elsevier Ltd This paper develops nonparametric specification tests for stochastic volatility ...
We consider a continuous-time stochastic volatility model. The model contains a stationary volatilit...
In this paper we are concerned with non-parametric inference on the volatility of volatility proces...
We propose a nonparametric method to determine the functional form of the noise density in discrete...
Stochastic volatility modeling of financial processes has become increasingly popular. The proposed ...
© 2015 by the author; licensee MDPI, Basel, Switzerland. This paper studies the asymptotic normality...
Stochastic volatility modelling of financial processes has become popular and most models contain a ...
A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonp...
International audienceConsider discrete time observations (X_{\ell\delta})_{1\leq \ell \leq n+1}$ of...
We consider a continuous time stochastic volatility model. The model contains a stationary volatilit...
In this paper we present two new tests for the parametric form of the variance function in diffusion...
This paper proposes a testing procedure in order to distinguish between the case where the volatilit...
This paper develops a specification test for stochastic volatility models by comparing the nonparame...
© 2016 Elsevier Ltd This paper develops nonparametric specification tests for stochastic volatility ...
We consider a continuous-time stochastic volatility model. The model contains a stationary volatilit...
In this paper we are concerned with non-parametric inference on the volatility of volatility proces...
We propose a nonparametric method to determine the functional form of the noise density in discrete...
Stochastic volatility modeling of financial processes has become increasingly popular. The proposed ...
© 2015 by the author; licensee MDPI, Basel, Switzerland. This paper studies the asymptotic normality...
Stochastic volatility modelling of financial processes has become popular and most models contain a ...
A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonp...
International audienceConsider discrete time observations (X_{\ell\delta})_{1\leq \ell \leq n+1}$ of...
We consider a continuous time stochastic volatility model. The model contains a stationary volatilit...
In this paper we present two new tests for the parametric form of the variance function in diffusion...
This paper proposes a testing procedure in order to distinguish between the case where the volatilit...