We often
only think in terms of market risk and return where risk is measured by the
standard deviation of returns. It is easy to calculate and update.
Unfortunately, the changing nature of markets makes for messy calculations and
analysis. Assuming a normal distribution is just too simple for measuring risk.
Investors have to be aware of skew in return distributions. More specifically, investors
have to account for negative skew because the unexpected extra downside risk is
what really hurts portfolio returns.
Skew can
be explained or models through a mixed distribution approach. Simply put, if
you have normal price dynamics mixed with a jump process for some negative
shock, you will get negative skew in the mix. You can think of jumps as regime
shifts that have some low probability of occurring.
All an
investor has to do is look at the shock behavior of markets from financial
crises or recessions to see that a mixed distribution is relevant. Jumps can be
predicted to occur but that does not mean we know when they will occur.
Interestingly, the impact of jumps and thus negative skew become more relevant
when normal volatility is low. High
volatility will mask the jump impact, so skew risk is greater when markets seem
calm.
The
impact of skew can be incorporated in a risk parity portfolio approach with
meaningful results. If a Gaussian mixed distribution is used to proxy for skew,
risks can be balanced beyond just volatility. If there is more skew within the
assets to be allocated, there will be larger adjustments in portfolio
allocations relative to the conventional approach of risk parity or just
mean/variance optimization.
New
research by some leading advocates of risk parity show that accounting for skew
is relevant. The adjustments in allocations are meaningful and can help offset
the risks from jumps in return behavior. See “Risk Parity Portfolios with Skewness Risk: An Application to Factor Investing and alternative Risk Premia”.
Volatility management in a risk parity
framework will create more turn-over and will only capture events after the
fact. Accounting for skew can reduce turnover and account for jumps before they
occur. For example, if there is a negative jump or shock, volatility will rise
after the event causing a decline in allocation to this risky asset.
Unfortunately, this is too late. Accounting for skew from the chance of jumps would
have forced a lower allocation to the risky asset and better addressed the
potential for a negative shock.
Since not
all strategies or asset class behavior have the same skewness or response to
jumps, there has to be a careful analysis of strategies, risk premiums, and
price behavior. Some risk factors are more subject to jumps, so accounting for
skew is more important. Skew should be measured, but more importantly, it can
be managed separately from volatility.
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