financeФинансы: теория и практика/Finance: Theory and PracticeFinance: Theory and Practice2587-56712587-7089Financial University under The Government of Russian Federation10.26794/2587-5671-2015-0-1-30-36finance-109Research ArticleФИНАНСЫ, ДЕНЕЖНОЕ ОБРАЩЕНИЕ И КРЕДИТFINANCES, MONETARY ADDRESS AND CREDITПРОГНОЗИРОВАНИЕ КОГЕРЕНТНЫХ РАЗРЫВОВ ВОЛАТИЛЬНОСТИFORECASTING COHERENT VOLATILITY BREAKOUTSДиденкоА. С.DidenkoA. S.alexander.didenko@gmail.comДубовиковМ. М.DubovikovM. M.noemail@neicon.ruПуткоБ. А.PoutkoB. A.noemail@neicon.ruФинансовый университетРоссияFinancial universityRussian FederationОАО «ИНДЕКС-XX»Россия«INDEX-XX» companyRussian Federation201510102017013036Copyright © Диденко А.С., Дубовиков М.М., Путко Б.А., 20172017Диденко А.С., Дубовиков М.М., Путко Б.А.Didenko A.S., Dubovikov M.M., Poutko B.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://financetp.fa.ru/jour/article/view/109

Разработана методика долгосрочного (до нескольких месяцев) прогнозирования разворотной динамики волатильности с использованием свойств длинной памяти финансовых временных рядов. Предложенный в [1] алгоритм вычисления фрактальной размерности через покрытие предфракталами используется для декомпозиции волатильности на удельную0A (t) и структурную Hµ(t). Предложены модели динамических компонентволатильности, способные предсказывать длинные восходящие в ней тренды. Для проверки статистическойзначимости прогнозов введены функции оценки условных и безусловных вероятностей для наблюдаемых и прогнозируемых компонент. Наши результаты могут быть использованы для предсказания точек перехода рынка в нестабильное состояние.

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.

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The authors declare that there are no conflicts of interest present.