Abstract
Previously, application of deep learning based sequential model drastically improved accuracy of volatility prediction in modelling of financial time series. However, unlike traditional financial time series model such as GARCH family of models, majority of deep learning based financial time series models focus solely on optimizing a single-step volatility prediction error and are not capable of conduct multi-step training and prediction of volatilities since volatility is the inherent uncertainty of the model prediction, whose multi-step prediction is drastically different from prediction of the mean of the financial time series.
In this work, a parsimonious autoregressive multi-step density regression (PA-MS-DR) framework is proposed to solve this problem. Our model framework can accurately capture the heavy-tail property of financial asset returns. In addition, our model is autoregressive, and it allows multi-step ahead training and forecasting, which significantly expands the applicability of the model in real world scenario. Finally, the structure of our method inspires us to devise a novel training method, which greatly accelerates the training speed of the algorithm.
The performance of PA-MS-DR is tested by comparing it with traditional time series models such as GARCH family of models and a non-autoregressive baseline model with similar structure. The result shows that our model consistently and significantly outperforms GARCH family of models. In addition, our model consistently outperforms the non-autoregressive baseline model, which demonstrates the effectiveness of our autoregressive model structure.
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Notes
- 1.
Supplementary material is available at https://github.com/imo1991/appendix4papers.
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Fan, X., Wei, X., Wang, D., Zhang, W., Qi, W. (2020). Multi-step Prediction of Financial Asset Return Volatility Using Parsimonious Autoregressive Sequential Model. In: Bitetta, V., Bordino, I., Ferretti, A., Gullo, F., Pascolutti, S., Ponti, G. (eds) Mining Data for Financial Applications. MIDAS 2019. Lecture Notes in Computer Science(), vol 11985. Springer, Cham. https://doi.org/10.1007/978-3-030-37720-5_9
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