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Modelling market volatilities: the neural network perspective

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  • F. Gonzalez Miranda
  • N. Burgess

Abstract

This paper investigates the use of Artificial Neural Networks (NN) to forecast volatility. In particular, we assess the dynamic behaviour of market volatility by forecasting the volatility implied in the transaction prices of the Ibex35 index options. The use of the NN technique is done within the framework of a model building strategy that tries to capitalize on the well known feature of persistence in volatility series. We demonstrate that forecasting with non-linear NNs generally produces forecasts which, on the basis of out-of-sample forecast encompassing tests and mean squared error comparisons, ordinarily dominate forecasts from traditional linear methods. Better forecasting results for the NN are due to its flexibility to account for potentially complex non-linear relationships, which are not well captured by traditional linear methods. We test and reject the hypothesis that volatility changes are unpredictable on an hourly basis.

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  • F. Gonzalez Miranda & N. Burgess, 1997. "Modelling market volatilities: the neural network perspective," The European Journal of Finance, Taylor & Francis Journals, vol. 3(2), pages 137-157.
    • Handle: RePEc:taf:eurjfi:v:3:y:1997:i:2:p:137-157
    DOI: 10.1080/135184797337499
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    2. László Vancsura & Tibor Tatay & Tibor Bareith, 2023. "Evaluating the Effectiveness of Modern Forecasting Models in Predicting Commodity Futures Prices in Volatile Economic Times," Risks, MDPI, vol. 11(2), pages 1-16, January.
    3. Rita Laura D’Ecclesia & Daniele Clementi, 2021. "Volatility in the stock market: ANN versus parametric models," Annals of Operations Research, Springer, vol. 299(1), pages 1101-1127, April.
    4. Melike Bildirici & Nilgun Guler Bayazit & Yasemen Ucan, 2020. "Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM," Energies, MDPI, vol. 13(11), pages 1-18, June.
    5. Psaradellis, Ioannis & Sermpinis, Georgios, 2016. "Modelling and trading the U.S. implied volatility indices. Evidence from the VIX, VXN and VXD indices," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1268-1283.

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