A Flexible State-Space Model with Application to Stochastic Volatility
نویسندگان
چکیده
منابع مشابه
Multivariate stochastic volatility using state space models
A Bayesian procedure is developed for multivariate stochastic volatility, using state space models. An autoregressive model for the log-returns is employed. We generalize the inverted Wishart distribution to allow for different correlation structure between the observation and state innovation vectors and we extend the convolution between the Wishart and the multivariate singular beta distribut...
متن کاملA Threshold Stochastic Volatility Model with Realized Volatility
Rapid development in the computer technology has made the financial transaction data visible at an ultimate limit level. The realized volatility, as a proxy for the ”true” volatility, can be constructed using the high frequency data. This paper extends a threshold stochastic volatility specification proposed in So, Li and Lam (2002) by incorporating the high frequency volatility measures. Due t...
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 کاملA Multivariate Stochastic Volatility Model
Anastasios Plataniotis and Petros Dellaportas [email protected] [email protected] Department of Statistics, Athens University of Economics and Business, Greece Summary: We introduce a broad class of multivariate stochastic volatility models where transformed eigenvalues and Givens rotation angles are assumed to be AR(1) processes. This decomposition retains the required positive definite structure of...
متن کاملMoving average stochastic volatility models with application to inflation forecast
We introduce a new class of models that has both stochastic volatility and moving average errors, where the conditional mean has a state space representation. Having a moving average component, however, means that the errors in the measurement equation are no longer serially independent, and estimation becomes more difficult. We develop a posterior simulator that builds upon recent advances in ...
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: SSRN Electronic Journal
سال: 2016
ISSN: 1556-5068
DOI: 10.2139/ssrn.2873512