Don’t draw the downs apart: How to best simulate asset price drawdowns
评估了多种自助法模拟技术，用于计算最大回撤的分布，发现标准自助法低估真实回撤，而Politis和Romano的平稳自助法最准确稳健，对股票和加密货币市场的风险管理有参考价值。
This paper evaluates bootstrap simulation techniques for calculating the distribution of maximum drawdown (MDD), an important risk indicator in the stock and cryptocurrency markets. Using stochastic dominance tests, we assess the full distributional properties of the 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 block-block bootstrap, tapered bootstrap and robust resampling, 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 for longer block lengths.