The two most important moments of a distribution are the mean and standard deviation of the distribution. In the case of leverage, drawdowns have a much larger effect on portfolio performance. Strong drawdowns may force traders to temporarily stop trading and may even lead to a re-evaluation of their trading strategy. Under such circumstances, the value of active portfolio management is brought to the fore by comparing the MDD of the portfolio with the markets’ MDD. Even though the performance of a portfolio is to be measured against its long-term objectives, short-term performance matters, especially when there is a market sell-off. This is because there are a greater number of data points when using daily data compared with using monthly data.Ĭlients, both retail and institutional, place a lot of importance on MDD. For instance, over a one-year period, the drawdown of an investment measured in monthly returns may be significantly lower than the drawdowns measured on a daily basis. An investment may have many drawdowns over a period of time but has only one MDD.įigures 1 and 2 exhibit the drawdowns and MDD for the MSCI Emerging Markets Index based on monthly total return data from September 2001 to September 2011.Īpart from the time horizon under consideration, the magnitude of drawdowns also depends on the frequency of measurement. Drawdown, in contrast, refers to the decline in value from the previous local maximum to a subsequent trough. The term “local” refers to the time horizon over which the investment is analyzed or transacted. Maximum drawdown (MDD) is the accumulated loss of buying an investment at its highest local maximum price and selling it at its lowest local minimum price. Mandelbrot claims that fractals and multi-fractals generate a more realistic picture of market risks and can be used to better stress-test portfolios. Examples of fractal patterns include coastlines, cauliflowers, and distributions of galaxies. A fractal is a geometric shape that can be separated into parts, each of which is a reduced scale version of the whole.
#DEFINE DRAWDOWN SERIES#
As a result, risk measures that depend on the above assumptions end up masking and underpricing true risk.Īccording to Mandelbrot (2008), a financial time series can be better modeled using fractal geometry.
#DEFINE DRAWDOWN SERIAL#
However, positive serial correlation does exist, which leads to temporary bouts of up or down rallies depending on the sentiment prevailing in the market. This basically means that predicting future market returns is impossible.
Statistical independence of returns means yesterday’s return has no influence on today’s, and similarly, today’s price has no influence on the change between current price and tomorrow’s price. Hence, assuming a normal distribution or independence of returns can lead to a severe underestimation of “real world” risk. However, the S&P 500 has had about 10 such months in the past 85 years. Based on a normal distribution, the S&P 500 Index should have had only 1.3 months of –15% or worse returns over the past 85 years. The normality of the return distribution and the statistically “independent” nature of returns are two such key assumptions. Most portfolio/risk management models make assumptions about asset returns that are not entirely appropriate in the real world. This post discusses the different ways in which maximum drawdown can be used as a risk factor to construct optimal portfolios. While “risk” can be defined in a number of ways, maximum drawdown is probably one of the clearest ways to measure an investor’s risk appetite and thus is an important factor to consider when building portfolios. The fundamental aim of any portfolio construction methodology is to deliver optimal risk-adjusted performance.
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