Forecasting Coherent Volatility Breakouts

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 two 00 dynamic components: specific A (t) and structural H (t).We introduce two separate models for A (t) and H (t), based on different principles and capable of catching long uptrends in volatility. To test statistical significance of 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.

Экономика и экономические науки

Вуз: Финансовый университет при Правительстве Российской Федерации

ID: 57034b485f1be72e70aa697e
UUID: 22f49a10-dd1c-0133-2307-525400003e20
Язык: Английский
Опубликовано: около 8 лет назад
Просмотры: 6


Alexander Didenko

Финансовый университет при Правительстве Российской Федерации


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