Neural network volatility forecasts
Abstract
We analyse whether the use of neural networks can improve ‘traditional’ volatility forecasts from time‐series models, as well as implied volatilities obtained from options on futures on the Spanish stock market index, the IBEX‐35. One of our main contributions is to explore the predictive ability of neural networks that incorporate both implied volatility information and historical time‐series information. Our results show that the general regression neural network forecasts improve the information content of implied volatilities and enhance the predictive ability of the models. Our analysis is also consistent with the results from prior research studies showing that implied volatility is an unbiased forecast of future volatility and that time‐series models have lower explanatory power than implied volatility. Copyright © 2008 John Wiley & Sons, Ltd.
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The artificial neural network method used by Aragones et al. (2007) is back-propagation.
“The ANN method used by Aragones, et al. (2007) is back-propagation (see Exhibit I).”
From A Neural Network Approach to Estimating the Allowance for Bad Debt (2011) · cites this paper as Aragones, et al. (2007)
A general rule of thumb is that no layer should have more than five elements; more elements make it harder for the artificial neural network to generalize.
“According to Aragones, et al. (2007) a general rule of thumb is that no layer should have more than five elements.”
From A Neural Network Approach to Estimating the Allowance for Bad Debt (2011) · cites this paper as Aragones, et al. (2007)
Artificial neural networks are excellent tools because they do not rely on strong assumptions about the distribution of underlying variables and can overcome limitations of more restrictive traditional volatility estimation models.
“Aragones, et al. (2007) reached the conclusion that ANNs are excellent tools because they “do not rely on strong assumptions about the distribution of the underlying variables and, therefore, can overcome some of the limitations of more restrictive traditional volatility estimation models.””
From A Neural Network Approach to Estimating the Allowance for Bad Debt (2011) · cites this paper as Aragones, et al. (2007)
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A Neural Network Approach to Estimating the Allowance for Bad Debt
Joyner, D. (2011) · VCU Libraries · doi:10.25772/h720-xy24 · Cited by 2 · see citing works
- Authors
- Aragonés, J. R., Blanco, C., & Estévez, P. G.
- Affiliations
- Universidad Complutense de Madrid (ES)
- Corresponding author
- Aragonés, José R.
- Venue
- Intelligent Systems in Accounting, Finance and Management
- Publisher
- Wiley
- Published
- 2007-07
- Volume
- 15
- Issue
- 3-4
- Pages
- 107-121
- ISSN
- 1055-615X (ISSN-L)
- 1550-1949
- 2160-0074
- OpenAlex
- W4252296371
- Cited by
- 11
- References
- 0
- Citations by year
- 2025: 1 · 2023: 1 · 2022: 1 · 2020: 2 · 2017: 3 · 2015: 2 · 2014: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Intelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management (publishedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Risk and Volatility Modeling
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- volatility forecasting, artificial neural networks, implied volatility, futures options, IBEX 35, Spanish stock market, generalized regression neural network, forecast accuracy
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