Friday, 30 September 2016
Last updated 9 hours ago
Aug 7 2013 | 5:58am ET
By Michael Markov
Co-founder and Chairman
Markov Processes International
In recent weeks risk parity funds have been the focus of unfavorable reports on their performance. Bridgewater’s All Weather Portfolio, the original and most famous risk parity fund, is often held up as an example. Originators of risk parity and managers of the world’s largest hedge fund, Bridgewater Associate’s now $65 billion All Weather Fund are legendary among institutional investors and other asset management firms looking to weatherproof their beta, and modernize their portfolios. Built on Ray Dalio, Bob Prince and Greg Jensen’s belief that there will always be surprises, the strategy seeks to balance a portfolio’s risk in an effort to perform in any economic environment.
The risk parity approach has performed particularly well over the past decade, minimizing losses during both the tech bubble and the financial crisis. As with any fairly new strategy it has yet to prove itself outside of simulation in a broad spectrum of economic environments. This combination of popularity and novelty can increase the desire for close monitoring of performance in situations like the one we now observe.
With the majority of risk parity assets invested in hedge funds, the need for careful observation poses a problem in terms of data frequency and timeliness. A solution can be found by forecasting daily performance based on exposure estimates from the previous month, following the unique approach developed by MPI in collaboration with Prof. Russ Wermers.
For this, we performed a quantitative analysis of All Weather returns using MPI’s proprietary Dynamic Style Analysis (DSA) technique. Although the fund return data is monthly, the underlying factor data is daily which allows us to create the intra-month hypothetical track record of the fund even when the current month’s return is not yet available.
The chart above shows the cumulative performance of the All Weather Fund YTD as well as daily estimates using a synthetic portfolio consisting of daily frequency market factors for the intra-month periods. Estimates up to May 31 are in-sample, while all subsequent estimates are out of sample.
The daily forecast implies a June return of approximately -6.4% and a YTD loss for the fund of approximately -8.6%. Also indicated is a drawdown of approximately 14% between May 3 and June 25.
Limited transparency, illiquidity and irregular performance reporting of hedge funds create significant barriers for both allocation and risk management decisions. By utilizing cutting edge analytical tools investors and analysts are able to monitor daily hedge fund proxy returns and make proactive investment decisions intra-month, rather than after they receive month-end performance results from the fund. For hedge fund investors faced with redemption restrictions this methodology provides a means to implement risk controls and to effectively hedge against unwanted risks.
Michael Markov is co-founder and chairman of Markov Processes International, LLC (MPI). Recognizing the power of quantitative analysis for investors, in 1992 he led the development of the industry’s first returns-based style analysis (RBSA) application based on William Sharpe’s groundbreaking methodology. Continuing this spirit of innovation, he co-authored Dynamic Style Analysis (DSA), a significant advancement to legacy returns-based modeling techniques for investors seeking the most precise analysis of managed investment products and portfolios.