Abstract
Accurate volatility forecasting is the core task in the risk management in which various portfolios’ pricing, hedging, and option strategies are exercised. Prior studies on stock market have primarily focused on estimation of stock price index by using financial time series models and data mining techniques. This paper proposes hybrid models with neural network and time series models for forecasting the volatility of stock price index in two view points: deviation and direction. It demonstrates the utility of the hybrid model for volatility forecasting.
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© 2006 Springer-Verlag Berlin Heidelberg
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Roh, T.H. (2006). Forecasting the Volatility of Stock Price Index. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_47
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DOI: https://doi.org/10.1007/11811305_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
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