arXiv 25 Nov 2018 · Econometrics · publishedJournal of Econometrics (2020) · 41 citations (OpenAlex)
arXiv:1811.10045 · PDF · DOI · OpenAlex · Extracted main text
Volatilities, in high-dimensional panels of economic time series with a dynamic factor structure on the levels or returns, typically also admit a dynamic factor decomposition. We consider a two-stage dynamic factor model method recovering the common and idiosyncratic components of both levels and log-volatilities. Specifically, in a first estimation step, we extract the common and idiosyncratic shocks for the levels, from which a log-volatility proxy is computed. In a second step, we estimate a dynamic factor model, which is equivalent to a multiplicative factor structure for volatilities, for the log-volatility panel. By exploiting this two-stage factor approach, we build one-step-ahead conditional prediction intervals for large $n \times T$ panels of returns. Those intervals are based on empirical quantiles, not on conditional variances; they can be either equal- or unequal- tailed. We provide uniform consistency and consistency rates results for the proposed estimators as both $n$ and $T$ tend to infinity. We study the finite-sample properties of our estimators by means of Monte Carlo simulations. Finally, we apply our methodology to a panel of asset returns belonging to the S&P100 index in order to compute one-step-ahead conditional prediction intervals for the period 2006-2013. A comparison with the componentwise GARCH benchmark (which does not take advantage of cross-sectional information) demonstrates the superiority of our approach, which is genuinely multivariate (and high-dimensional), nonparametric, and model-free.
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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 | Barigozzi, M. and Hallin, M (2017) Networks, dynamic factors, and the volatility analysis of high-dimensional financial series self | 1.000 | 7 | 5 | 100% |
| 2 | Barigozzi, M. and Hallin, M (2016) General dynamic factors and volatilities: Recovering the market volatility shocks self | 1.000 | 6 | 4 | 100% |
| 3 | Barigozzi, M. and Hallin, M (2017) General dynamic factors and volatilities: Estimation and forecasting self | 1.000 | 6 | 4 | 100% |
| 4 | Christoffersen, P. F (1998) Evaluating interval forecasts | 0.874 | 5 | 2 | 100% |
| 5 | Vershynin, R (2012) Introduction to the non-asymptotic analysis of random matrices | 0.843 | 4 | 3 | 75% |
| 6 | Barigozzi, M., Hallin, M., and Soccorsi, S (2018) Identification of global and local shocks in international financial markets via general dynamic factor models self | 0.843 | 3 | 3 | 100% |
| 7 | Forni, M., Giovannelli, A., Lippi, M., and Soccorsi, S (2018) Dynamic factor model with infinite-dimensional factor space: Forecasting | 0.843 | 3 | 3 | 100% |
| 8 | Fan, J., Liao, Y., and Mincheva, M (2013) Large covariance estimation by thresholding principal orthogonal complements | 0.843 | 3 | 3 | 100% |
| 9 | Forni, M., Hallin, M., Lippi, M., and Zaffaroni, P (2017) Dynamic factor models with infinite dimensional factor space: Asymptotic analysis self | 0.794 | 28 | 5 | 50% |
| 10 | Hallin, M. and Li ska, R (2007) Determining the number of factors in the general dynamic factor model self | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 63 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review | 0.405 | 1 | 1 |
| 2 | The Dynamic, the Static, and the Weak factor models and the analysis of high-dimensional time series | 0.405 | 1 | 1 |