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volatilityforecastingpackage: A Financial Volatility Package in Mathematica

Author

Listed:
  • Noorshanaaz Khodabaccus

    (University of Technology, Mauritius)

  • Aslam A. E. F. Saib

    (University of Technology, Mauritius)

Abstract

The relevance of financial volatility forecasting in efficient decision making regarding risk-related assets cannot be subdued. In the financial world, asset price volatility plays a pivotal role in investment decision making and portfolio setups. The prediction of these volatilities usually deal with noisy and non-stationary data bearing heteroscedastic nature. This paper introduces the volatilityforecastingpackage for financial volatility modelling, forecasting and visualization using state-of-the art algorithms. This package allows recourse to algorithms through a user friendly interface supported by the Mathematica framework, that provides easy access to models for high and low frequency data, while accessibly generating forecasts, estimating errors and generating plots. The package also allows analysis of user data and based on the results, a set of models appropriate for the data is suggested for eventual use.

Suggested Citation

  • Noorshanaaz Khodabaccus & Aslam A. E. F. Saib, 2024. "volatilityforecastingpackage: A Financial Volatility Package in Mathematica," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2307-2324, June.
  • Handle: RePEc:kap:compec:v:63:y:2024:i:6:d:10.1007_s10614-023-10406-2
    DOI: 10.1007/s10614-023-10406-2
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    References listed on IDEAS

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    1. McAleer, Michael & Jimenez-Martin, Juan-Angel & Perez-Amaral, Teodosio, 2013. "GFC-robust risk management strategies under the Basel Accord," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 97-111.
    2. repec:agr:journl:v:4(621):y:2019:i:4(621):p:35-52 is not listed on IDEAS
    3. M. MALLIKARJUNA & R. Prabhakara RAO, 2019. "Volatility experience of major world stock markets," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(621), W), pages 35-52, Winter.
    4. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Dima Alberg & Haim Shalit & Rami Yosef, 2008. "Estimating stock market volatility using asymmetric GARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 18(15), pages 1201-1208.
    7. 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.
    8. Spyridon D. Vrontos & John Galakis & Ioannis D. Vrontos, 2021. "Implied volatility directional forecasting: a machine learning approach," Quantitative Finance, Taylor & Francis Journals, vol. 21(10), pages 1687-1706, October.
    9. Batra, Amit, 2004. "Stock return volatility patterns in India," Indian Council for Research on International Economic Relations, New Delhi Working Papers 124, Indian Council for Research on International Economic Relations, New Delhi, India.
    10. Wu, Xinyu & Hou, Xinmeng, 2020. "Forecasting volatility with component conditional autoregressive range model," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    11. 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.
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    More about this item

    Keywords

    Volatility forecasting; Volatility models; Financial econometrics;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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