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I'm working with a heterogenous basket of instruments (in volatility terms). Risk parity allocation seems to be useful for the portfolio( * 1/Volatility).

However, there are times when the volatility of a couple of instruments drops down significantly compared to the rest of the universe. Risk parity asset allocation shifts too much capital to the low-vol instruments(20-50%) which is highly undesirable.

Basic improvement to the allocation approach introducing a cutoff rule based on comparing the risk parity allocation to a naive equal weight approach and limiting the allocation to a maximum percentage(10-20%). However, this doesn't seem to be optimal especially when universe size becomes too small or too large.

Any ideas/improvements/suggestions on how to deal with risk allocation in heterogenous universes while actively avoiding concentrating the portfolio weights to a few instruments?

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2 Answers 2

I would create categories, and work on risk parity among the categories.

Otherwise, variance is not really a good measure of downside risk: Change your risk measure, use a rolling window historical VaR or Expected Shortfall at some horizon that matches your investment style. downside semi-variance could do the trick too if don't want to change your algo too much.

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For example, what we do is to constrain one category, "traditional fixed income" into a size that fits our view of the market. –  ghost.comet Mar 27 at 3:02
    
Also, if you use a window of estimation that is too short, you might get crazy, unrepresentatively low risk evaluation, The latter could explain extremely large components in terms of notional. –  ghost.comet Mar 27 at 3:04
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Are you working with futures data (mixing rate futures with equity futures) OR allocating between macro instruments?

If so, using non-linear variants of GARCH (GJR-GARCH, TGARCH etc.) are common way to solve your risk parity allocation issue that you might be facing.

One common related issue is not that the volatility to a couple of instruments drop down significantly per se as much as that the volatility of those instruments which saw a drop in volatility happened to pick up very rapid and the portfolio was unable to response to fast increase in volatility of such instruments. The non-linear variants of GARCH can resolve this too as they can pick up very fast increases or decreases in volatility than regular GARCH or EWMA volatility.

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yes, i am working with futures. I think your understanding of the underlying issue makes sense. Deceptive (under)estimates of volatility seem to be the bane for risk parity. Could you please elaborate on the non-linear GARCH models that you are referring to? Is there any reference I can look up in this context? –  Mindstorm Mar 27 at 23:36
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