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This paper contains a forecasting exercise on 30 time series, ranging on several fields, from economy to ecology. The …
Persistent link: https://www.econbiz.de/10010281250
This paper contains a forecasting exercise on 30 time series, ranging on several fields, from economy to ecology. The …
Persistent link: https://www.econbiz.de/10005190861
Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to …
Persistent link: https://www.econbiz.de/10010291802
We propose a new semi-parametric model for the implied volatility surface, which incorporates machine learning algorithms. Given a starting model, a tree-boosting algorithm sequentially minimizes the residuals of observed and estimated implied volatility. To overcome the poor predicting power of...
Persistent link: https://www.econbiz.de/10005453978
Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to …
Persistent link: https://www.econbiz.de/10010580995
In time series analysis, latent factors are often introduced to model the heterogeneous time evolution of the observed processes. The presence of unobserved components makes the maximum likelihood estimation method more difficult to apply. A Bayesian approach can sometimes be preferable since it...
Persistent link: https://www.econbiz.de/10012712875
In time series analysis, latent factors are often introduced to model the heterogeneous time evolution of the observed processes. The presence of unobserved components makes the maximum likelihood estimation method more difficult to apply. A Bayesian approach can sometimes be preferable since it...
Persistent link: https://www.econbiz.de/10014067403
In this paper, a Bayesian version of the exponential smoothing method of forecasting is proposed. The approach is based …
Persistent link: https://www.econbiz.de/10005125279
We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model is calibrated at a quarterly frequency for ten European countries. We also use maximum-likelihood estimates and Bayesian estimates to account for parameter uncertainty. We find...
Persistent link: https://www.econbiz.de/10008797745
In this paper, we propose a Markov Chain Quasi-Monte Carlo (MCQMC) approach for Bayesian estimation of a discrete-time version of the stochastic volatility (SV) model. The Bayesian approach represents a feasible way to estimate SV models. Under the conventional Bayesian estimation method for SV...
Persistent link: https://www.econbiz.de/10013116422