Backtesting correlated quantities
A technique to decorrelate samples and reach higher discriminatory power is presented
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Backtesting financial models over long horizons inevitably leads to overlapping returns, giving rise to correlated samples. In this paper Nikolai Nowaczyk and Vladimir Piterbarg propose a new method of dealing with this important problem by using decorrelation and show how this increases the discriminatory power of the resulting tests
Correlations arise naturally in many backtests – for example, as the autocorrelation of time series or cross-correlation of any model
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