Trong-Nghia Nguyen, Minh-Ngoc Tran, David Gunawan, R. Kohn
arXiv 7 Jun 2019 · Econometrics · publishedJournal of Business and Economic Statistics (2022) · 13 citations (OpenAlex)
arXiv:1906.02884 · PDF · DOI · OpenAlex · Extracted main text
The Stochastic Volatility (SV) model and its variants are widely used in the financial sector while recurrent neural network (RNN) models are successfully used in many large-scale industrial applications of Deep Learning. Our article combines these two methods in a non-trivial way and proposes a model, which we call the Statistical Recurrent Stochastic Volatility (SR-SV) model, to capture the dynamics of stochastic volatility. The proposed model is able to capture complex volatility effects (e.g., non-linearity and long-memory auto-dependence) overlooked by the conventional SV models, is statistically interpretable and has an impressive out-of-sample forecast performance. These properties are carefully discussed and illustrated through extensive simulation studies and applications to five international stock index datasets: The German stock index DAX30, the Hong Kong stock index HSI50, the France market index CAC40, the US stock market index SP500 and the Canada market index TSX250. An user-friendly software package together with the examples reported in the paper are available at \url{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 | Yu, J., Yang, Z., and Zhang, X (2006) A class of nonlinear stochastic volatility models and its implications for pricing currency options | 1.000 | 7 | 3 | 100% |
| 2 | Kim, S., Shephard, N., and Chib, S (1998) Stochastic volatility: likelihood inference and comparison with ARCH models | 1.000 | 5 | 3 | 100% |
| 3 | Breidt, F., Crato, N., and de Lima, P (1998) The detection and estimation of long memory in stochastic volatility | 0.956 | 8 | 4 | 88% |
| 4 | Deligiannidis, G., Doucet, A., and Pitt, M. K (2018) The correlated pseudo marginal method | 0.737 | 3 | 3 | 67% |
| 5 | Andrieu, C., Doucet, A., and Holenstein, R (2010) Particle Markov chain Monte Carlo methods | 0.737 | 3 | 2 | 100% |
| 6 | Duan, J.-C. and Fulop, A (2015) Density-tempered marginalized Sequential Monte Carlo samplers | 0.737 | 3 | 2 | 100% |
| 7 | Lo, A. W (1991) Long-term memory in stock market prices | 0.737 | 3 | 2 | 100% |
| 8 | Oliva, J. B., Póczos, B., and Schneider, J. G (2017) The statistical recurrent unit | 0.737 | 3 | 2 | 100% |
| 9 | Taylor, S. J (1982) Financial returns modelled by the product of two stochastic processes — a study of daily sugar prices 1961-79 | 0.737 | 3 | 2 | 100% |
| 10 | Granger, C. W. J. and Joyeux, R (1980) An introduction to long-memory time series models and fractional differencing | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 66 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Generalized Autoregressive Score Trees and Forests | 0.405 | 1 | 1 |
| 2 | Variational Inference for GARCH-family Models | 0.405 | 1 | 1 |