T. -N. Nguyen, M. -N. Tran, R. Kohn
arXiv 25 Oct 2020 · Econometrics · publishedJournal of Applied Econometrics (2022) · 13 citations (OpenAlex)
arXiv:2010.13061 · PDF · DOI · OpenAlex · Extracted main text
We propose a new class of financial volatility models, called the REcurrent Conditional Heteroskedastic (RECH) models, to improve both in-sample analysis and out-ofsample forecasting of the traditional conditional heteroskedastic models. In particular, we incorporate auxiliary deterministic processes, governed by recurrent neural networks, into the conditional variance of the traditional conditional heteroskedastic models, e.g. GARCH-type models, to flexibly capture the dynamics of the underlying volatility. RECH models can detect interesting effects in financial volatility overlooked by the existing conditional heteroskedastic models such as the GARCH, GJR and EGARCH. The new models often have good out-of-sample forecasts while still explaining well the stylized facts of financial volatility by retaining the well-established features of econometric GARCH-type models. These properties are illustrated through simulation studies and applications to thirty-one stock indices and exchange rate data. . An user-friendly software package together with the examples reported in the paper are available at https://github.com/vbayeslab.
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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 | Donaldson, R. G. and Kamstra, M (1997) An artificial neural network-GARCH model for international stock return volatility | 0.928 | 4 | 3 | 100% |
| 2 | Hansen, P. R. and Lunde, A (2005) A forecast comparison of volatility models: does anything beat a GARCH(1,1)? | 0.811 | 4 | 2 | 100% |
| 3 | Nguyen, N., Tran, M.-N., Gunawan, D., and Kohn, R (2019) A long short-term memory stochastic volatility model self | 0.644 | 4 | 2 | 50% |
| 4 | Bollerslev, T (1986) Generalized autoregressive conditional heteroskedasticity | 0.644 | 2 | 2 | 100% |
| 5 | Glosten, L. R., Jagannathan, R., and Runkle, D. E (1993) On the relation between the expected value and the volatility of the nominal excess return on stocks | 0.644 | 2 | 2 | 100% |
| 6 | Goodfellow, I., Bengio, Y., and Courville, A (2016) Deep Learning | 0.644 | 2 | 2 | 100% |
| 7 | Koopman, S. J., Lucas, A., and Scharth, M (2016) Predicting time-varying parameters with parameter-driven and observation-driven models | 0.644 | 2 | 2 | 100% |
| 8 | Chopin, N (2002) A sequential particle filter method for static models | 0.585 | 3 | 1 | 100% |
| 9 | Elman, J. L (1990) Finding structure in time | 0.585 | 3 | 1 | 100% |
| 10 | Baillie, R. T., Bollerslev, T., and Mikkelsen, H. O (1996) Fractionally integrated generalized autoregressive conditional heteroskedasticity | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 60 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Deep Learning Enhanced Multivariate GARCH | 0.644 | 2 | 2 |
| 2 | Deep Learning Enhanced Realized GARCH | 0.585 | 3 | 1 |
| 3 | Generalized Autoregressive Score Trees and Forests | 0.405 | 1 | 1 |
| 4 | Global Neural Networks and The Data Scaling Effect in Financial Time Series Forecasting | 0.405 | 1 | 1 |
| 5 | Variational Inference for GARCH-family Models | 0.405 | 1 | 1 |