Matias Quiroz, Laleh Tafakori, Hans Manner
arXiv 14 Dec 2024 · Econometrics
arXiv:2412.10791 · PDF · DOI · OpenAlex · Extracted main text
We investigate methods for forecasting multivariate realized covariances matrices applied to a set of 30 assets that were included in the DJ30 index at some point, including two novel methods that use existing (univariate) log of realized variance models that account for attenuation bias and time-varying parameters. We consider the implications of some modeling choices within the class of heterogeneous autoregressive models. The following are our key findings. First, modeling the logs of the marginal volatilities is strongly preferred over direct modeling of marginal volatility. Thus, our proposed model that accounts for attenuation bias (for the log-response) provides superior one-step-ahead forecasts over existing multivariate realized covariance approaches. Second, accounting for measurement errors in marginal realized variances generally improves multivariate forecasting performance, but to a lesser degree than previously found in the literature. Third, time-varying parameter models based on state-space models perform almost equally well. Fourth, statistical and economic criteria for comparing the forecasting performance lead to some differences in the models' rankings, which can partially be explained by the turbulent post-pandemic data in our out-of-sample validation dataset using sub-sample analyses.
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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 | Bollerslev, T., Patton, A. J., and Quaedvlieg, R (2018) Modeling and forecasting (un) reliable realized covariances for more reliable financial decisions | 1.000 | 14 | 4 | 100% |
| 2 | Bekierman, J. and Manner, H (2018) Forecasting realized variance measures using time-varying coefficient models self | 1.000 | 6 | 3 | 100% |
| 3 | Bollerslev, T., Patton, A. J., and Quaedvlieg, R (2016) Exploiting the errors: A simple approach for improved volatility forecasting | 1.000 | 5 | 4 | 100% |
| 4 | Wang, Y., Liang, F., Wang, T., and Huang, Z (2020) Does measurement error matter in volatility forecasting? Empirical evidence from the Chinese stock market | 0.843 | 3 | 3 | 100% |
| 5 | Corsi, F (2009) A simple approximate long-memory model of realized volatility | 0.737 | 3 | 2 | 100% |
| 6 | Barndorff-Nielsen, O. E., Hansen, P. R., Lunde, A., and Shephard, N (2011) Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and n… | 0.644 | 2 | 2 | 100% |
| 7 | Chiriac, R. and Voev, V (2011) Modelling and forecasting multivariate realized volatility | 0.644 | 2 | 2 | 100% |
| 8 | Bollerslev, T., Engle, R. F., and Nelson, D. B (1994) ARCH models | 0.511 | 2 | 1 | 100% |
| 9 | Oh, D. H. and Patton, A. J (2016) High-dimensional copula-based distributions with mixed frequency data | 0.511 | 2 | 1 | 100% |
| 10 | Golosnoy, V., Gribisch, B., and Liesenfeld, R (2012) The conditional autoregressive wishart model for multivariate stock market volatility | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 52 scored citations.
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| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Forecasting of volatility and risk premia in electricity markets | 0.737 | 3 | 2 |