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Generalized value at risk forecasting

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  • Aerambamoorthy Thavaneswaran
  • Alex Paseka
  • Julieta Frank

Abstract

In this paper, using estimating function approach, a new optimal volatility estimator is introduced and based on the recursive form of the estimator a data-driven generalized EWMA model for value at risk (VaR) forecast is proposed. An appropriate data-driven model for volatility is identified by the relationship between absolute deviation and standard deviation for symmetric distributions with finite variance. It is shown that the asymptotic variance of the proposed volatility estimator is smaller than that of conventional estimators and is more appropriate for financial data with larger kurtosis. For IBM, Microsoft, Apple stocks and SP 500 index the proposed method is used to identify the model, estimate the volatility, and obtain minimum mean square error(MMSE) forecasts of VaR.

Suggested Citation

  • Aerambamoorthy Thavaneswaran & Alex Paseka & Julieta Frank, 2020. "Generalized value at risk forecasting," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(20), pages 4988-4995, October.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:20:p:4988-4995
    DOI: 10.1080/03610926.2019.1610443
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    Cited by:

    1. Sulalitha Bowala & Japjeet Singh, 2022. "Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures," JRFM, MDPI, vol. 15(10), pages 1-16, September.
    2. Shafiqah Azman & Dharini Pathmanathan & Aerambamoorthy Thavaneswaran, 2022. "Forecasting the Volatility of Cryptocurrencies in the Presence of COVID-19 with the State Space Model and Kalman Filter," Mathematics, MDPI, vol. 10(17), pages 1-15, September.

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