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Modeling CAC40 Volatility Using Ultra-high Frequency Data

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  • Degiannakis, Stavros
  • Floros, Christos

Abstract

Fractionally integrated autoregressive moving average (ARFIMA) and Heterogeneou Autoregressive (HAR) models are estimated and their ability to predict the one-trading-day-ahead CAC40 realized volatility is investigated. In particular, this paper follows three steps: (i) The optimal sampling frequency for constructing the CAC40 realized volatility is examined based on the volatility signature plot. Moreover, the realized volatility is adjusted to the information that flows into the market when it is closed. (ii) We forecast the one-day-ahead realized volatility using the ARFIMA and the HAR models. (iii) The accuracy of the realized volatility forecasts is investigated under the superior predictive ability framework. According to the predicted mean squared error, a simple ARFIMA model provides accurate one-trading day-ahead forecasts of CAC40 realized volatility. The evaluation of model's predictability illustrates that the ARFIMA forecasts of realized volatility (i) are statistically superior compared to its competing models, and (ii) provide adequate one-trading-day-ahead Value-at-Risk forecasts.

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  • Degiannakis, Stavros & Floros, Christos, 2013. "Modeling CAC40 Volatility Using Ultra-high Frequency Data," MPRA Paper 80445, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:80445
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    2. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    3. 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.
    4. Stavros Degiannakis & Christos Floros & Enrique Salvador & Dimitrios Vougas, 2022. "On the stationarity of futures hedge ratios," Operational Research, Springer, vol. 22(3), pages 2281-2303, July.
    5. Yarovaya, Larisa & Matkovskyy, Roman & Jalan, Akanksha, 2021. "The effects of a “black swan” event (COVID-19) on herding behavior in cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    6. Leandro Maciel, 2020. "Technical analysis based on high and low stock prices forecasts: evidence for Brazil using a fractionally cointegrated VAR model," Empirical Economics, Springer, vol. 58(4), pages 1513-1540, April.
    7. Stavros Degiannakis & Andreas Andrikopoulos & Timotheos Angelidis & Christos Floros, 2013. "Return dispersion, stock market liquidity and aggregate economic activity," Working Papers 166, Bank of Greece.
    8. Leandro Maciel & Rosangela Ballini, 2021. "Functional Fuzzy Rule-Based Modeling for Interval-Valued Data: An Empirical Application for Exchange Rates Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 743-771, February.

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    More about this item

    Keywords

    intra-day data; long memory; predictability; realized volatility; ultra-high frequency modeling; Value-at-Risk.;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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