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Anwendung von Deep Learning in der Prognose der Volatilität des DAX: Ein Vergleich der Prognosegüte von GARCH und LSTM

Author

Listed:
  • Nico Knuth
  • Andreas Nastansky

    (Hochschule für Wirtschaft und Recht (HWR) Berlin)

Abstract

Die Fähigkeit Volatilität präzise vorherzusagen, ist von zentraler Bedeutung für das Risikomanagement in Banken und für das Treffen fundierter Anlageentscheidungen. In diesem Beitrag wird der Einsatz von Deep-Learning-Methoden − speziell des Long Short-Term Memory (LSTM)-Netzwerkes − zur Prognose der Volatilität des Deutschen Aktienindex analysiert. Hierbei wird die Prognosegüte des LSTMs mit der von gängigen zeitreihenökonometrischen Ansätzen, wie den Generalized Autoregressive Conditional Heteroscedaticity (GARCH)- Modellen, verglichen. Obwohl LSTMs in vielen Bereichen zunehmend Anwendung finden, ist ihr Einsatz in der Vorhersage von Finanzmarktzeitreihen im Vergleich zu etablierten ökonometrischen Modellen noch wenig untersucht. Die empirischen Ergebnisse zeigen, dass das LSTM verschiedene symmetrische und asymmetrische GARCH-Modelle in Bezug auf die Vorhersagegenauigkeit sowohl im Trainings- als auch im Testdatensatz deutlich übertrifft. Künstliche Neuronale Netze bieten eine bessere Generalisierungsfähigkeit und niedrigere Prognosefehler. Gleichzeitig werden Herausforderungen des Einsatzes neuronaler Netze im stark regulierten Bankensektor diskutiert.

Suggested Citation

  • Nico Knuth & Andreas Nastansky, 2025. "Anwendung von Deep Learning in der Prognose der Volatilität des DAX: Ein Vergleich der Prognosegüte von GARCH und LSTM," Statistische Diskussionsbeiträge 59, Universität Potsdam, Wirtschafts- und Sozialwissenschaftliche Fakultät.
    • Handle: RePEc:pot:statdp:59
    DOI: 10.25932/publishup-67486
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    References listed on IDEAS

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    1. Brooks,Chris, 2008. "RATS Handbook to Accompany Introductory Econometrics for Finance," Cambridge Books, Cambridge University Press, number 9780521896955, July.
    2. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    5. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    6. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    7. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    8. Audrino, Francesco & Sigrist, Fabio & Ballinari, Daniele, 2020. "The impact of sentiment and attention measures on stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 334-357.
    9. Till Barz & Andreas Nastansky, 2024. "Herausforderungen des finanziellen Risikomanagements: Eine empirische Untersuchung des Value at Risk-Ansatzes in Stresssituationen," Statistische Diskussionsbeiträge 57, Universität Potsdam, Wirtschafts- und Sozialwissenschaftliche Fakultät.
    10. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    11. Pesaran, M. Hashem, 2015. "Time Series and Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780198759980, Decembrie.
    12. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    13. Bera, Anil K & Higgins, Matthew L, 1993. "ARCH Models: Properties, Estimation and Testing," Journal of Economic Surveys, Wiley Blackwell, vol. 7(4), pages 305-366, December.
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    More about this item

    Keywords

    asymmetrische Volatilität; GARCH; LSTM; Künstliche Neuronale Netze; Volatilitätsprognosen;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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