Abstract
Neural network models are appealing tools in finance because of the abundance of high quality financial data and the paucity of testable financial models. This class of models has been very popular in the last decade for estimating nonlinear models and financial risk measures such as Value at Risk and Expected shortfall. However, there are a number of alternative nonparametric approaches that can be used, each with its own advantages and disadvantages. In this paper we compare nonparametric volatility function estimators based on kernel estimators and on neural networks in terms of their accuracy to fit the true unknown volatility function.
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Giordano, F., La Rocca, M., Perna, C. (2012). Nonparametric estimation of volatility functions: Some experimental evidences. In: Perna, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer, Milano. https://doi.org/10.1007/978-88-470-2342-0_27
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DOI: https://doi.org/10.1007/978-88-470-2342-0_27
Publisher Name: Springer, Milano
Print ISBN: 978-88-470-2341-3
Online ISBN: 978-88-470-2342-0
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