Forecasting the Volatility of Stock Price Index

  • Tae Hyup Roh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


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.


Time Series Model GARCH Model Financial Time Series Leverage Effect Conditional Volatility 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tae Hyup Roh
    • 1
  1. 1.Department of Business, Management Information SystemSeoul Women’s UniversityNowon-GuKorea

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