Forecasting Coherent Volatility Breakouts (with M.Dubovikov, B.Poutko)

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 is used to decompose volatility into two dynamic components: specific and structural. We introduce two separate models for both, 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.

Read: http://www.fa.ru/dep/vestnik/Documents/VFU_01-2015.pdf#page=30

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 two
dynamic components: specific 0
A ( )t and structural 
H ( )t . We introduce two separate models for 0
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.
Keywords: stock market; price risk; fractal dimension; market crash; ARCH-GARCH; range-based volatility models;
multi-scale volatility; volatility reversals; technical analysis.

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