Daichi Hiraki, Siddhartha Chib, Yasuhiro Omori
arXiv 22 Apr 2024 · Econometrics · publishedJournal of Econometrics (2025) · 1 citations (OpenAlex)
arXiv:2404.13986 · PDF · DOI · OpenAlex · Extracted main text
In this paper we consider the simulation-based Bayesian analysis of stochastic volatility in mean (SVM) models. Extending the highly efficient Markov chain Monte Carlo mixture sampler for the SV model proposed in Kim et al. (1998) and Omori et al. (2007), we develop an accurate approximation of the non-central chi-squared distribution as a mixture of thirty normal distributions. Under this mixture representation, we sample the parameters and latent volatilities in one block. We also detail a correction of the small approximation error by using additional Metropolis-Hastings steps. The proposed method is extended to the SVM model with leverage. The methodology and models are applied to excess holding yields and S&P500 returns in empirical studies, and the SVM models are shown to outperform other volatility models based on marginal likelihoods.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Omori, Y., S. Chib, N. Shephard, and J. Nakajima (2007) Stochastic volatility with leverage: Fast and efficient likelihood inference self | 0.956 | 8 | 5 | 88% |
| 2 | Kim, S., N. Shephard, and S. Chib (1998) Stochastic volatility: likelihood inference and comparison with arch models | 0.928 | 5 | 4 | 80% |
| 3 | Chan, J. C (2017) The stochastic volatility in mean model with time-varying parameters: An application to inflation modeling | 0.737 | 3 | 2 | 100% |
| 4 | Chib, S (1995) Marginal likelihood from the gibbs output self | 0.644 | 2 | 2 | 100% |
| 5 | Chib, S. and E. Greenberg (1995) Understanding the metropolis-hastings algorithm self | 0.644 | 2 | 2 | 100% |
| 6 | Chib, S. and I. Jeliazkov (2001) Marginal likelihood from the metropolis–hastings output self | 0.644 | 2 | 2 | 100% |
| 7 | Chib, S., F. Nardari, and N. Shephard (2002) Markov chain monte carlo methods for stochastic volatility models self | 0.644 | 2 | 2 | 100% |
| 8 | Engle, R. F., D. M. Lilien, and R. P. Robins (1987) Estimating time varying risk premia in the term structure: The arch-m model | 0.644 | 2 | 2 | 100% |
| 9 | de Jong, P. and N. Shephard (1995) The simulation smoother for time series models | 0.585 | 3 | 3 | 33% |
| 10 | Durbin, J. and S. J. Koopman (2002) A simple and efficient simulation smoother for state space time series analysis | 0.585 | 3 | 3 | 33% |
Showing the top 10 of 31 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Dynamic Factor Stochastic Volatility-in-Mean VAR for Large Macroeconomic Panels | 0.843 | 4 | 4 |