Showing 1 - 10 of 2,490
We provide a formulation of stochastic volatility (SV) based on Gaussian process regression (GPR). Forecasting … reduces the error rate on one-year out-of-sample forecasting during the 2007-09 recession by 26% relative to a benchmark range …
Persistent link: https://www.econbiz.de/10014186681
We propose a new algorithm which allows easy estimation of Vector Autoregressions (VARs) featuring asymmetric priors and time varying volatilities, even when the cross sectional dimension of the system N is particularly large. The algorithm is based on a simple triangularisation which allows to...
Persistent link: https://www.econbiz.de/10011389735
COVID-19 observations and discusses their impact on prior calibration for inference and forecasting purposes. It shows that … volatility. For forecasting, the choice among outlier-robust error structures is less important, however, when a large cross …
Persistent link: https://www.econbiz.de/10013472790
Efficient posterior simulators for two GARCH models with generalized hyperbolic disturbances are presented. The first model, GHt-GARCH, is a threshold GARCH with a skewed and heavy-tailed error distribution; in this model, the latent variables that account for skewness and heavy tails are...
Persistent link: https://www.econbiz.de/10013105412
The estimation of large vector autoregressions with stochastic volatility using standard methods is computationally very demanding. In this paper we propose to model conditional volatilities as driven by a single common unobserved factor. This is justified by the observation that the pattern of...
Persistent link: https://www.econbiz.de/10013066409
Adding multivariate stochastic volatility of a flexible form to large Vector Autoregressions (VARs) involving over a hundred variables has proved challenging due to computational considerations and over-parameterization concerns. The existing literature either works with homoskedastic models or...
Persistent link: https://www.econbiz.de/10012917923
Recent research has shown that a reliable vector autoregressive model (VAR) for forecasting and structural analysis of … priors. This is important both for reduced form applications, such as forecasting, and for more structural applications, such …
Persistent link: https://www.econbiz.de/10012983057
We analyse the volatility structure of Asian currencies against the U.S. dollar (USD) for the Thai Baht THB, the Philippine Peso PHP, the Indonesian Rupiah IDR and the South Korean Won KRW. Our goal is to check if the characteristics of the volatility dynamics have changed in a K-state switching...
Persistent link: https://www.econbiz.de/10009733810
This article details a Bayesian analysis of the Nile river flow data, using a simple state space model. This allows the article to concentrate on implementation issues surrounding this model. For this data set, Metropolis-Hastings and Gibbs sampling algorithms are implemented in the programming...
Persistent link: https://www.econbiz.de/10013128945
This paper presents a method for Bayesian nonparametric analysis of the return distribution in a stochastic volatility model. The distribution of the logarithm of the squared return is flexibly modelled using an infinite mixture of Normal distributions. This allows efficient Markov chain Monte...
Persistent link: https://www.econbiz.de/10013133054