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You are here: Home / Investment News / Measure for measure: uneven returns, lumpy risks and how to iron out kinks in the data

Measure for measure: uneven returns, lumpy risks and how to iron out kinks in the data

December 18, 2016

Mark Kritzman: senior lecturer finance MIT Sloan School of Management
Mark Kritzman: senior lecturer finance MIT Sloan School of Management

Investors need to look beyond traditional measures of asset class volatility and correlation in portfolio construction, according to Mark Kritzman senior lecturer in finance MIT Sloan School of Management.

In a paper published last week by the International Forum of Sovereign Wealth Funds (IFSWF), Kritzman says while textbook portfolio theory assumes a “single distribution” regime of risks and returns, it “turns out that, empirically, that has not been the case”.

“One way of categorizing history is to try to categorize it in terms of fragile or turbulent periods versus resilient or calm periods,” he says in the IFSWF report. “The way to distinguish these periods is not best done with volatility and correlation.”

Kritzman says two relatively new measures of market instability and risk concentration – ‘financial turbulence’ and ‘absorption ratio’, respectively – “are better or, at least, more informative than volatility and correlation”.

Financial turbulence provides insight into “how statistically unusual a set of returns is in a given period given their historical pattern of behaviour”; the absorption ratio, meanwhile, quantifies ‘systemic risk’, or the likelihood of isolated market shocks (for example, an unexpected rise in oil prices) triggering a wider meltdown.

He says in the paper, titled ‘Asset Allocation for the Short- and Long-Term’, the two tools help investors identify whether the market is in either a fragile or resilient state.

“This is something that one should take into account not only in modifying your exposure to risk through time but also in figuring out what your policy portfolio is in the first place,” Kritzman says. “For example, if you want to build a portfolio that is diversified against losses, you don’t want to look at the correlations and volatilities that prevailed, on average, across the entire history of returns. It is much more effective to pay attention to the volatilities and correlations that prevailed during these periods of market fragility because that is when losses typically occur.”

He also says investors should be careful in extrapolating monthly return data into annual results, which can overlook “divergent performance” of asset classes over different time periods.

“My presumption is that when you build a portfolio, when you do asset allocation, you are designing a portfolio to be optimal over some multi-year horizon,” Kritzman says. “The risk profile over that multi-year horizon is going to be vastly different than what you are going to infer from volatilities and correlations estimated from monthly returns.”

The paper offers a number of other techniques to consider such as incorporating “error estimation” in portfolio construction and how to set asset allocation according to a “kinked utility function” designed to avoid losses above a certain threshold.

Kritzman says history doesn’t support the common assertion by institutions that as long-term investors they can handle large short-term losses.

“I’ve been in this business for over 40 years and that is not the case. People may like to think that they are long-term investors, but people do care about what might happen along the way,” he says in the report. “You can say that I’ll structure my portfolio based on estimates of long-term risk, but then, if you do that you are going to make your portfolio vulnerable to large interim drawdowns. If you focus on just short-term risk, you are going to expose your portfolio to sub-optimal growth over the long-term. This is something that has to be balanced.”

As well as the interview with Kritzman the IFSWF paper includes discussions on identifying asset classes, short- and long-term portfolio construction techniques, an overview of the ‘reference portfolio’ method, and, expanded data from a survey of sovereign wealth funds on investment trends and organisational structures.

 

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