As with lookback options, the first instinct is to price them using Monte Carlo techniques, but one can actually do so much more quickly using a multi-level PDE solver, at least for sufficiently simple SDEs. So far as efficient computation goes, we can regard this as a payoff very similar to a lookback option (much as in the PDF you linked). Therefore you should play with a variety of parameterizations to estimate your model error. (After the clarification, this answer is no longer relevant)Įxpected maximum drawdown is going to be highly sensitive to your choice of SDE, and to your calibration of it. Rd.DrawDown, rd.DrawDownPeak, rd.DrawDownTrough, rd.PeakIndex, rd.TroughIndex)) import pandas as pd def drawdownCalculator(data): highwatermark py(). He codes it in MATLAB, but I wanted to try my hand at the same code in Python. Int _startIndex, _endIndex, _troughIndex Chans book, Im attempting to calculate the maximum drawdown and the longest drawdown duration from cumulative portfolio returns. Here is the code of the simple drawdown class used for the comparisons: public class SimpleDrawDownĪnd here is the code for the full efficient implementation. We are achieving about a 20:1 improvement in calculation time. Test10 - running drawdown test with 500 period rolling window. Test9 - running drawdown test with 360 period rolling window. Test8 - running drawdown test with 180 period rolling window. Test7 - running drawdown test with 60 period rolling window. Here we compare to the results generated from my efficient rolling window algorithm where only the latest observation is added and then it does it's magic test6 - running drawdown test with 30 period rolling window. Test5 - simple drawdown test with 500 period rolling window. Test4 - simple drawdown test with 360 period rolling window. Test3 - simple drawdown test with 180 period rolling window. Test2 - simple drawdown test with 60 period rolling window. Updated What is a Maximum Drawdown A maximum drawdown (MDD) measures the maximum fall in the value of the investment, as given by the difference between the value of the lowest trough and that of the highest peak before the trough. Here we take a simple drawdown implementation and re-calculate for the full window each time test1 - simple drawdown test with 30 period rolling window. I want to share this as the effort required to replicate this work is quite high. I have gone ahead and written a solution to this in C#. Since it became clear to market participants in late summer/early fall of 2014 that QE3/4 was ending (and purchases would taper to zero), there have been three palpable drawdowns, and the one ending in October was larger than any since the start of QE4.This is quite a complex problem if you want to solve this in a computationally efficient way for a rolling window. This drawdown envelope gives a less noisy representation of each event. This exhibit shows the cumulative maximum drawdown during each drawdown period, and hence the curve declines to its maximum drawdown and remains there until it returns to zero when the drawdown recovers. "The duration of these drawdowns was also much longer outside of QE periods. "Maximum drawdowns of the S&P 500 have been much larger in periods without QE than those with QE," Parker writes. He illustrates it in this chart of maximum drawdowns, or the percentage decline stocks experience from a recent high. Morgan Stanley's Adam Parker notes that the more notable bouts of volatility of late have come after the Fed pulled the plug on its third and fourth rounds of QE*. So for many market-watchers, it was no surprise to see sharper sell-offs and heightened volatility in the financial markets during the periods after the Fed ended QE. It often indicates a user profile.ĭuring quantitative easing, the Fed is buying billions of dollars of bonds in the bond market, adding liquidity to the markets and arguably keeping asset prices propped up. Account icon An icon in the shape of a person's head and shoulders.
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