Benjamin Poignard, Manabu Asai
arXiv 27 Jun 2024 · Econometrics · publishedEconometric Reviews (2026)
arXiv:2406.19033 · PDF · DOI · OpenAlex · Extracted main text
Building upon the pertinence of the factor decomposition to break the curse of dimensionality inherent to multivariate volatility processes, we develop a factor model-based multivariate stochastic volatility (fMSV) framework that relies on two viewpoints: sparse approximate factor model and sparse factor loading matrix. We propose a two-stage estimation procedure for the fMSV model: the first stage obtains the estimators of the factor model, and the second stage estimates the MSV part using the estimated common factor variables. We derive the asymptotic properties of the estimators. Simulated experiments are performed to assess the forecasting performances of the covariance matrices. The empirical analysis based on vectors of asset returns illustrates that the forecasting performances of the fMSV models outperforms competing conditional covariance models.
appendix boundary found by appendix_command · 44% of the source is main text. Read the extracted text to check this.
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 | Poignard, B. and M. Asai (2023) High-dimensional sparse multivariate stochastic volatility models self | 1.000 | 6 | 3 | 100% |
| 2 | Chib, S. and Nardari, F. and N. Shephard (2006) Analysis of high dimensional multivariate stochastic volatility models | 1.000 | 5 | 3 | 100% |
| 3 | Onatski, A (2010) Determining the number of factors from empirical distribution of eigenvalues | 1.000 | 5 | 3 | 100% |
| 4 | Bai, J. and K. Li (2012) Statistical analysis of factor models of high dimension | 0.920 | 18 | 6 | 78% |
| 5 | Engle, R.F (2002) Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models | 0.737 | 4 | 3 | 50% |
| 6 | Engle, R.F. and K.F. Kroner (1995) Multivariate simultaneous generalized arch | 0.737 | 3 | 3 | 67% |
| 7 | G. Kastner and S. Frühwirth-Schnatter and H.F. Lopes (2017) Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models | 0.737 | 3 | 3 | 67% |
| 8 | Bauwens, L. and Laurent, S. and J.V.K. Rombouts (2006) Multivariate GARCH models: A survey | 0.737 | 3 | 2 | 100% |
| 9 | Pitt, M. and N. Shephard (1999) Time varying covariances: a factor stochastic volatility approach (with discussion) | 0.737 | 3 | 2 | 100% |
| 10 | M. Barigozzi and M. Hallin (2017) Generalized dynamic factor models and volatilities: estimation and forecasting | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 61 scored citations.