Mika Meitz, Daniel Preve, Pentti Saikkonen
arXiv 10 May 2018 · Econometrics · publishedCommunication in Statistics-Theory and Methods (2021) · 14 citations (OpenAlex)
arXiv:1805.04010 · PDF · DOI · OpenAlex · Extracted main text
A new mixture autoregressive model based on Student's $t$-distribution is proposed. A key feature of our model is that the conditional $t$-distributions of the component models are based on autoregressions that have multivariate $t$-distributions as their (low-dimensional) stationary distributions. That autoregressions with such stationary distributions exist is not immediate. Our formulation implies that the conditional mean of each component model is a linear function of past observations and the conditional variance is also time varying. Compared to previous mixture autoregressive models our model may therefore be useful in applications where the data exhibits rather strong conditional heteroskedasticity. Our formulation also has the theoretical advantage that conditions for stationarity and ergodicity are always met and these properties are much more straightforward to establish than is common in nonlinear autoregressive models. An empirical example employing a realized kernel series based on S&P 500 high-frequency data shows that the proposed model performs well in volatility forecasting.
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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 | Wong, C. S., Chan, W. S. & Kam, P. L (2009) A student $t$-mixture autoregressive model with applications to heavy-tailed financial data | 1.000 | 12 | 4 | 100% |
| 2 | Kalliovirta, L., Meitz, M. & Saikkonen, P (2015) A Gaussian mixture autoregressive model for univariate time series self | 0.874 | 7 | 2 | 100% |
| 3 | Wong, C. S. & Li, W. K (2001) On a logistic mixture autoregressive model | 0.843 | 3 | 3 | 100% |
| 4 | Glasbey, C. A (2001) Non-linear autoregressive time series with multivariate Gaussian mixtures as marginal distributions | 0.644 | 4 | 1 | 100% |
| 5 | Kalliovirta, L., Meitz, M. & Saikkonen, P (2016) Gaussian mixture vector autoregression self | 0.644 | 2 | 2 | 100% |
| 6 | Wong, C. S. & Li, W. K (2000) On a mixture autoregressive model | 0.644 | 2 | 2 | 100% |
| 7 | Barndorff-Nielsen, O. E., Hansen, P. R., Lunde, A. & Shephard, N (2008) Designing realized kernels to measure the ex post variation of equity prices in the presence of noise | 0.405 | 1 | 1 | 100% |
| 8 | Corsi, F (2009) A simple approximate long-memory model of realized volatility | 0.405 | 1 | 1 | 100% |
| 9 | Dueker, M. J., Sola, M. & Spagnolo, F (2007) Contemporaneous threshold autoregressive models: estimation, testing and forecasting | 0.405 | 1 | 1 | 100% |
| 10 | Frühwirth-Schnatter, S (2006) Finite Mixture and Markov Switching Models | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 22 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2003.05221 | 0.909 | 16 | 7 |