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A Low Price Correction for Improved Volatility Estimation and Forecasting

Author

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  • George-Jason Siouris

    (Department of Mathematics, University of the Aegean, GR-83200 Karlovasi, Samos, Greece
    Current address: Division of Statistics and Actuarial-Financial Mathematics, Department of Mathematics, University of the Aegean, GR-83200 Karlovasi, Samos, Greece
    These authors contributed equally to this work.)

  • Alex Karagrigoriou

    (Department of Mathematics, University of the Aegean, GR-83200 Karlovasi, Samos, Greece
    Current address: Division of Statistics and Actuarial-Financial Mathematics, Department of Mathematics, University of the Aegean, GR-83200 Karlovasi, Samos, Greece
    These authors contributed equally to this work.)

Abstract

In this work, we focus on volatility estimation which plays a crucial role in risk analysis and management. In order to improve value at risk (VaR) forecasts, we discuss the concept of low price effect and introduce the low price correction which does not require any additional parameters and instead of returns it takes into account the prices of the asset. Judgement on the forecasting quality of the proposed methodology is based on both the relative number of violations and VaR volatility. For illustrative purposes, a real example from the Athens Stock Exchange is fully explored.

Suggested Citation

  • George-Jason Siouris & Alex Karagrigoriou, 2017. "A Low Price Correction for Improved Volatility Estimation and Forecasting," Risks, MDPI, vol. 5(3), pages 1-14, August.
    • Handle: RePEc:gam:jrisks:v:5:y:2017:i:3:p:45-:d:110079
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    Cited by:

    1. Alex Karagrigoriou & George-Jason Siouris & Despoina Skilogianni, 2019. "Adjusted Evaluation Measures for Asymmetrically Important Data," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 4(1), pages 41-66, June.

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