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Rolling-sampled parameters of ARCH and Levy-stable models

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  • Degiannakis, Stavros
  • Livada, Alexandra
  • Panas, Epaminondas

Abstract

In this paper an asymmetric autoregressive conditional heteroskedasticity (ARCH) model and a Levy-stable distribution are applied to some well-known financial indices (DAX30, FTSE20, FTSE100 and SP500), using a rolling sample of constant size, in order to investigate whether the values of the estimated parameters of the models change over time. Although, there are changes in the estimated parameters reflecting that structural properties and trading behaviour alter over time, the ARCH model adequately forecasts the one-day-ahead volatility. A simulation study is run to investigate whether the time variant attitude holds in the case of a generated ARCH data process revealing that even in that case the rolling-sampled parameters are time-varying.

Suggested Citation

  • Degiannakis, Stavros & Livada, Alexandra & Panas, Epaminondas, 2008. "Rolling-sampled parameters of ARCH and Levy-stable models," MPRA Paper 80464, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:80464
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    Cited by:

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    3. Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
    4. Stavros Degiannakis & Alexandra Livada, 2016. "Evaluation of realized volatility predictions from models with leptokurtically and asymmetrically distributed forecast errors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(5), pages 871-892, April.
    5. Vinodh Madhavan & Rakesh Arrawatia, 2016. "Relative Efficiency of G8 Sovereign Credit Default Swaps and Bond Scrips: An Adaptive Market Hypothesis Perspective," Studies in Microeconomics, , vol. 4(2), pages 127-150, December.
    6. Peng, Lijuan & Liang, Chao, 2023. "Sustainable development during the post-COVID-19 period: Role of crude oil," Resources Policy, Elsevier, vol. 85(PA).
    7. Amira Akl Ahmed & Doaa Akl Ahmed, 2016. "Modelling Conditional Volatility and Downside Risk for Istanbul Stock Exchange," Working Papers 1028, Economic Research Forum, revised Jul 2016.
    8. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2018. "Forecasting global stock market implied volatility indices," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 111-129.
    9. Degiannakis, Stavros & Filis, George, 2016. "Forecasting oil price realized volatility: A new approach," MPRA Paper 69105, University Library of Munich, Germany.
    10. Degiannakis, Stavros & Floros, Christos & Dent, Pamela, 2013. "Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence," International Review of Financial Analysis, Elsevier, vol. 27(C), pages 21-33.
    11. Wang, Lu & Ma, Feng & Hao, Jianyang & Gao, Xinxin, 2021. "Forecasting crude oil volatility with geopolitical risk: Do time-varying switching probabilities play a role?," International Review of Financial Analysis, Elsevier, vol. 76(C).

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    More about this item

    Keywords

    ARCH model; GED distribution; Leverage effect; Levy-stable distribution; Rolling sample; Spill over; Value at risk.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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