Traditional risk measures such as standard deviation and CVaR are properties of the distribution of returns over a specified investment horizon. Because such fixed holding period analysis is relatively easy, and because derivative contracts are fixed-term products, the analysis of derivative strategies is normally conducted for a fixed horizon. Investors are, however, also likely to be concerned with changes in the value of an asset or portfolio at all times during the life of the contract. A more dynamic analysis is clearly desirable. Maximum drawdown is a path-dependent quantity that has emerged as a means to express interim risk. However, partly because of modelling complexities, maximum drawdown is often quoted as a single number based on the historical performance of a portfolio. This is potentially very misleading and ignores the analytic richness which can be derived from this measure. In this work we emphasise the fact that maximum drawdown is a random quantity with an associated distribution of possible values. An analysis of future maximum drawdown distributions is carried out in which the distributions are generated by means of Monte Carlo simulation. Using this technology we are able to report on a range of characteristics of the drawdown statistic for a given portfolio or instrument. The impact of hedging instruments is investigated and it is shown that strategies in which an index is hedged with a put option or a put spread exhibit significant reductions in the magnitudes of potential maximum drawdowns. This demonstrates the value of options in controlling dynamic risk over the entire term to expiry.