Giorgio Calzolari, Roxana Halbleib, Christian Mücher
arXiv 14 Feb 2023 · Econometrics · publishedAStA Advances in Statistical Analysis (2025)
arXiv:2302.07052 · PDF · DOI · OpenAlex · Extracted main text
We provide a simple method to estimate the parameters of multivariate stochastic volatility models with latent factor structures. These models are very useful as they alleviate the standard curse of dimensionality, allowing the number of parameters to increase only linearly with the number of the return series. Although theoretically very appealing, these models have only found limited practical application due to huge computational burdens. Our estimation method is simple in implementation as it consists of two steps: first, we estimate the loadings and the unconditional variances by maximum likelihood, and then we use the efficient method of moments to estimate the parameters of the stochastic volatility structure with GARCH as an auxiliary model. In a comprehensive Monte Carlo study we show the good performance of our method to estimate the parameters of interest accurately. The simulation study and an application to real vectors of daily returns of dimensions up to 148 show the method's computation advantage over the existing estimation procedures.
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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 | Bai, J. and Li, K (2016) Maximum likelihood estimation and inference for approximate factor models of high dimension | 1.000 | 7 | 4 | 100% |
| 2 | Kastner, G., Frühwirth-Schnatter, S., and Lopes, H. F (2017) Efficient bayesian inference for multivariate factor stochastic volatility models | 0.971 | 12 | 4 | 92% |
| 3 | Calzolari, G., Halbleib, R., and Zagidullina, A (2021) A latent factor model for forecasting realized variances self | 0.928 | 4 | 3 | 100% |
| 4 | Bansal, R., Gallant, A. R., Hussey, R., and Tauchen, G (1994) Computational Aspects of Nonparametric Simulation Estimation, pages 3–22 | 0.843 | 3 | 3 | 100% |
| 5 | Gallant, A. R. and Tauchen, G (1996) Which moments to match? | 0.843 | 3 | 3 | 100% |
| 6 | Gordon, N. J., Salmond, D. J., and Smith, A. F (1993) Novel approach to nonlinear/non-gaussian bayesian state estimation | 0.843 | 3 | 3 | 100% |
| 7 | Nardari, F. and Scruggs, J. T (2007) Bayesian analysis of linear factor models with latent factors, multivariate stochastic volatility, and apt pricing restrictions | 0.843 | 3 | 3 | 100% |
| 8 | Bai, J. and Li, K (2012) Statistical analysis of factor models of high dimension | 0.822 | 18 | 6 | 56% |
| 9 | Chib, S., Nardari, F., and Shephard, N (2006) Analysis of high dimensional mulitivariate stochastic volatility models | 0.811 | 4 | 2 | 100% |
| 10 | Pitt, M. K. and Shephard, N (1999) Time-VaryingCovariances: A Factor Stochastic Volatility Approach | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 33 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Factor Multivariate Stochastic Volatility Models of High Dimension | 0.511 | 2 | 1 |