A semiparametric stochastic volatility model
نویسندگان
چکیده
منابع مشابه
Semiparametric Asymmetric Stochastic Volatility∗
This paper extends the stochastic volatility with leverage model, where returns are correlated with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically. The novelty of the paper is in modeling the unknown distribution with an infinite ordered mixture of bivariate normals with mean zero, but whose mixture probabilities and covari...
متن کاملBayesian semiparametric stochastic volatility modeling
This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovations, nonparametric Bayesian methods are used to flexibly model the distribution’s skewness and kurtosis while volatility dynamics follow a parametric structure. Our Bayesian approach p...
متن کاملA New Semiparametric Volatility Model
We propose a new semiparametric observation-driven volatility model where the form of the error density directly influences the volatility dynamics. This feature distinguishes our model from standard semiparametric GARCH models. The link between the estimated error density and the volatility dynamics follows from the application of the generalized autoregressive score framework of Creal, Koopma...
متن کاملEstimating a Semiparametric Asymmetric Stochastic Volatility Model with a Dirichlet Process Mixture
In this paper we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically. Its novelty is in modeling the joint, conditional, return-volatility, distribution with a infinite mixture of bivariate Normal distributions with mean zero ...
متن کاملA Neural Stochastic Volatility Model
In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance. The model comprises a pair of complementary stochastic recurrent neural networks: the generative network models the j...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Econometrics
سال: 2012
ISSN: 0304-4076
DOI: 10.1016/j.jeconom.2011.09.029