DeepVol: Volatility Forecasting from High-Frequency Data with Dilated
Causal Convolutions
- URL: http://arxiv.org/abs/2210.04797v2
- Date: Thu, 13 Oct 2022 09:59:26 GMT
- Title: DeepVol: Volatility Forecasting from High-Frequency Data with Dilated
Causal Convolutions
- Authors: Fernando Moreno-Pino, Stefan Zohren
- Abstract summary: We propose DeepVol, a model based on Dilated Causal Convolutions to forecast day-ahead volatility by using high-frequency data.
We show that the dilated convolutional filters are ideally suited to extract relevant information from intraday financial data.
- Score: 78.6363825307044
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Volatility forecasts play a central role among equity risk measures. Besides
traditional statistical models, modern forecasting techniques, based on machine
learning, can readily be employed when treating volatility as a univariate,
daily time-series. However, econometric studies have shown that increasing the
number of daily observations with high-frequency intraday data helps to improve
predictions. In this work, we propose DeepVol, a model based on Dilated Causal
Convolutions to forecast day-ahead volatility by using high-frequency data. We
show that the dilated convolutional filters are ideally suited to extract
relevant information from intraday financial data, thereby naturally mimicking
(via a data-driven approach) the econometric models which incorporate realised
measures of volatility into the forecast. This allows us to take advantage of
the abundance of intraday observations, helping us to avoid the limitations of
models that use daily data, such as model misspecification or manually designed
handcrafted features, whose devise involves optimising the trade-off between
accuracy and computational efficiency and makes models prone to lack of
adaptation into changing circumstances. In our analysis, we use two years of
intraday data from NASDAQ-100 to evaluate DeepVol's performance. The reported
empirical results suggest that the proposed deep learning-based approach learns
global features from high-frequency data, achieving more accurate predictions
than traditional methodologies, yielding to more appropriate risk measures.
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