Chen Liu, Chao Wang, Minh-Ngoc Tran, Robert Kohn
arXiv 16 Feb 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2302.08002 · PDF · DOI · OpenAlex · Extracted main text
We propose a new approach to volatility modeling by combining deep learning (LSTM) and realized volatility measures. This LSTM-enhanced realized GARCH framework incorporates and distills modeling advances from financial econometrics, high frequency trading data and deep learning. Bayesian inference via the Sequential Monte Carlo method is employed for statistical inference and forecasting. The new framework can jointly model the returns and realized volatility measures, has an excellent in-sample fit and superior predictive performance compared to several benchmark models, while being able to adapt well to the stylized facts in volatility. The performance of the new framework is tested using a wide range of metrics, from marginal likelihood, volatility forecasting, to tail risk forecasting and option pricing. We report on a comprehensive empirical study using 31 widely traded stock indices over a time period that includes COVID-19 pandemic.
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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 | Hansen, Peter Reinhard, Huang, Zhuo, Shek, Howard Howan (2012) Realized GARCH: a joint model for returns and realized measures of volatility | 0.928 | 4 | 3 | 100% |
| 2 | Andersen, Torben G., Bollerslev, Tim (1998) Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts | 0.874 | 6 | 2 | 100% |
| 3 | Barndorff-Nielsen, Ole E., Hansen, Peter Reinhard, Lunde, Asger, She… (2008) Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise | 0.737 | 3 | 2 | 100% |
| 4 | Shephard, Neil, Sheppard, Kevin (2010) Realising the future: forecasting with high-frequency-based volatility (HEAVY) models | 0.737 | 3 | 2 | 100% |
| 5 | Barndorff-Nielsen, Ole E., Shephard, Neil (2004) Power and Bipower Variation with Stochastic Volatility and Jumps | 0.644 | 2 | 2 | 100% |
| 6 | Engle, Robert (2002) New frontiers for arch models | 0.644 | 2 | 2 | 100% |
| 7 | Gerlach, Richard, Wang, Chao (2016) Forecasting risk via realized GARCH, incorporating the realized range self | 0.644 | 2 | 2 | 100% |
| 8 | Glosten, Lawrence R., Jagannathan, Ravi, Runkle, David E (1993) On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks | 0.644 | 2 | 2 | 100% |
| 9 | Hansen, Peter Reinhard, Huang, Zhuo (2016) Exponential GARCH Modeling With Realized Measures of Volatility | 0.644 | 2 | 2 | 100% |
| 10 | Hochreiter, Sepp, Schmidhuber, Jürgen (1997) Long Short-Term Memory | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 48 scored citations.