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Trading Rules and Value at Risk: Is There a Linkage?

In: Data Analytics for Management, Banking and Finance

Author

Listed:
  • Evangelos Vasileiou

    (University of the Aegean, Department of Financial and Management Engineering)

  • Maria Karagiannaki

    (Hellenic Open University)

  • Aristeidis Samitas

    (National and Kapodistrian University of Athens)

Abstract

This chapter examines whether Technical Analysis (TA) signals could contribute to the generation of accurate Value at Risk (VaR) estimation. We incorporate TA into the widely accepted and applied Delta-Normal (or Variance-Covariance) VaR model. Using simple TA rules, the Simple Moving Average (SMA), we present that with the appropriate adjustments when the TA signals accurately indicate the growth and the recession periods, they could be incorporated as behavioral indicators into the Conventional Delta-Normal VaR (CDNVaR) model. We apply our assumptions in a dataset from the Athex Composite Stock Index, the Borsa Istanbul 100 Index, and the Egyptian Exchange Index for the period 2002–2020, and the empirical evidence confirms our assumption that the incorporation of TA rules not only significantly improves the VaR accuracy of the CDNVaR model, but it also outperforms the exponentially weighted moving average VaR models.

Suggested Citation

  • Evangelos Vasileiou & Maria Karagiannaki & Aristeidis Samitas, 2023. "Trading Rules and Value at Risk: Is There a Linkage?," Springer Books, in: Foued Saâdaoui & Yichuan Zhao & Hana Rabbouch (ed.), Data Analytics for Management, Banking and Finance, pages 319-332, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-36570-6_15
    DOI: 10.1007/978-3-031-36570-6_15
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