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A Bayesian Semiparametric Multiplicative Error Model With an Application to Realized Volatility
| Content Provider | Semantic Scholar |
|---|---|
| Author | Solgi, Reza Mira, A. |
| Copyright Year | 2013 |
| Abstract | A semiparametric multiplicative error model (MEM) is proposed. In traditional MEM, the innovations are typically assumed to be Gamma distributed (with one free parameter that ensures unit mean of the innovations and thus identifiability of the model), however empirical investigations unveil the inappropriateness of this choice. In the proposed approach, the conditional mean of the time series is modeled parametrically, while we model its conditional distribution nonparametrically by Dirichlet process mixture of Gamma distributions. Bayesian inference is performed using Markov chain Monte Carlo simulation. This model is applied to the time series of daily realized volatility of some indices, and is compared to similar parametric models available in the literature. Our simulations and empirical studies show better predictive performance, flexibility, and robustness to misspecification of our Bayesian semiparametric approach. Supplemental materials for this article are available online. |
| Starting Page | 558 |
| Ending Page | 583 |
| Page Count | 26 |
| File Format | PDF HTM / HTML |
| DOI | 10.1080/10618600.2013.810151 |
| Alternate Webpage(s) | https://ssl.lu.usi.ch/entityws/Allegati/pdf_pub6719.pdf |
| Alternate Webpage(s) | https://doi.org/10.1080/10618600.2013.810151 |
| Volume Number | 22 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
