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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.

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Who was the best in 2014 Winter Olympics: benchmarking in ‘The Economist’ and Tolstoy style

As a late followup to all the press, summarizing results of 2014 Winter Olympics, I decided to apply data envelopment analysis to find most efficient teams on 2014 Winter Olympics. I’ll use Benchmarking package to estimate efficiencies and ggplot2 + ggthemes to visualize it.

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