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Forecasting coherent volatility breakouts

Author

Listed:
  • DIDENKO ALEXANDER

    (Financial university)

  • DUBOVIKOV MIKHAIL

    («INDEX-XX» company)

  • POUTKO BORIS

    (Financial university)

Abstract

The paper develops an algorithm for making long-term (up to three months ahead) predictions of volatility reversals based on long memory properties of financial time series. The approach for computing fractal dimension using sequence of the minimal covers with decreasing scale (proposed in [1]) is used to decompose volatility into two0dynamic components: specific A (t ) and structural Hµ(t ). We introduce two separate models forA (t ) and Hµ(t ), based on different principles and capable of catching long uptrends in volatility. To test statistical significanceof its abilities we introduce several estimators of conditional and unconditional probabilities of reversals in observed and predicted dynamic components of volatility. Our results could be used for forecasting points of market transition to an unstable state.

Suggested Citation

  • Didenko Alexander & Dubovikov Mikhail & Poutko Boris, 2015. "Forecasting coherent volatility breakouts," Вестник Финансового университета, CyberLeninka;Федеральное государственное образовательное бюджетное учреждение высшего профессионального образования «Финансовый университет при Правительстве Российской Федерации» (Финансовый университет), issue 1 (85), pages 30-36.
    • Handle: RePEc:scn:031255:15897514
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    References listed on IDEAS

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    1. M. F. M. Osborne, 1959. "Brownian Motion in the Stock Market," Operations Research, INFORMS, vol. 7(2), pages 145-173, April.
    2. Putko, Boris & Didenko, Alexander & Dubovikov, Mikhail, 2014. "The model of volatility of the exchange rate (RUR/USD), based on the fractal characteristics of time series," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 36(4), pages 79-87.
    3. Dubovikov, M.M & Starchenko, N.V & Dubovikov, M.S, 2004. "Dimension of the minimal cover and fractal analysis of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 339(3), pages 591-608.
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    More about this item

    Keywords

    ФОНДОВЫЙ РЫНОК; ЦЕНОВОЙ РИСК; ФРАКТАЛЬНАЯ РАЗМЕРНОСТЬ; КРАХИ РЫНКА; ARCH-GARCH МО- ДЕЛЬ; МОДЕЛИ ВОЛАТИЛЬНОСТИ КАК АМПЛИТУДЫ; МНОГОМАСШТАБНАЯ ВОЛАТИЛЬНОСТЬ; РАЗВОРОТЫ ВОЛАТИЛЬНОСТИ; ТЕХНИЧЕСКИЙ АНАЛИЗ;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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