Don’t draw the downs apart – How to best simulate asset price drawdowns

This paper evaluates bootstrap simulation techniques for calculating the distribution of the maximum drawdown (MDD), an important risk indicator in stock and cryptocurrency markets. Using stochastic dominance tests, we assess the full distributional properties of MDD under different methods. Our findings reveal that the standard Efron (1979) bootstrap, which assumes independence and identically distributed random variables, systematically underestimates the true MDD. While the moving block bootstrap provides reasonable estimates, it is subject to non-stationarity bias, particularly when large drawdowns occur at the boundaries of a return series. Alternative procedures, such as the block-block bootstrap and the tapered bootstrap, do not lead to better results. Of all the methods studied, the stationary bootstrap of Politis and Romano (1994) produces the most accurate and robust results, particularly with longer block lengths. We recommend this method as the preferred choice for researchers and practitioners modelling drawdown risk.