FORECASTING COHERENT VOLATILITY BREAKOUTS
https://doi.org/10.26794/2587-5671-2015-0-1-30-36
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.
About the Authors
A. S. Didenko
Financial university
Russian Federation
Russian Federation
M. M. Dubovikov
«INDEX-XX» company
Russian Federation
Russian Federation
B. A. Poutko
Financial university
Russian Federation
Russian Federation
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Review
For citations:
Didenko A.S., Dubovikov M.M., Poutko B.A. FORECASTING COHERENT VOLATILITY BREAKOUTS. Finance: Theory and Practice. 2015;(1):30-36. (In Russ.) https://doi.org/10.26794/2587-5671-2015-0-1-30-36
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