Drawdown Measures: Are They All the Same?

Korn, Olaf; Möller, Philipp M.; Schwehm, Christian

doi:10.3905/jpm.2022.1.346

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…This includes Value at Risk (VaR); see Jorion (2000), Conditional Value at Risk (CVaR); see Rockafellar and Uryasev (2000), absolute drawdown; see Magdon-Ismail and Atiya (2004); Hayes (2006), conditional expected drawdown (CED), the tail mean of maximum drawdown distributions, see Goldberg and Mahmoud (2017), and the more general coherent risk that axiomatize the risk measures; see Luenberger (2013); Shapiro et al (2021). See also Korn et al (2022) for an empirical study of comparing various drawdown-based risk metrics. In this paper, we focus on a more practical drawdown measure, the maximum percentage drawdown, the maximum percentage drops in wealth over time, as the risk measure.…”

Section: Downside Risksmentioning

confidence: 99%

On Data-Driven Drawdown Control with Restart Mechanism in Trading

Hsieh¹

2023

Preprint

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This paper extends the existing drawdown modulation control policy to include a novel restart mechanism for trading. It is known that the drawdown modulation policy guarantees the maximum percentage drawdown no larger than a prespecified drawdown limit for all time with probability one. However, when the prespecified limit is approaching in practice, such a modulation policy becomes a stop-loss order, which may miss the profitable followup opportunities, if any. Motivated by this, we add a data-driven restart mechanism into the drawdown modulation trading system to auto-tune the performance. We find that with the restart mechanism, our policy may achieve a superior trading performance to that without the restart, even with a nonzero transaction costs setting. To support our findings, some empirical studies using equity ETF and cryptocurrency with historical price data are provided.

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