just finished my Friday night backtests - risk parity does seem to perform pretty well a lot of the times. So when does it not?
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Replying to @o_wutang
thats a great question. risk parity is at its core a bet on macro-factor dispersion and regime-stability on volatility at the macro factor level. a bad daily print looks like thursday: some reversals in time-series momentum, higher real rates AND B/Es, lower stock prices
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Replying to @NewRiverInvest @o_wutang
but thats a lazy answer. a bad year in risk-parity is characterized by a repeating pattern of sudden crashes followed by quick recoveries and unstable correlations forcing de-levering before sharp recoveries and crashing again right after a lever-up signal
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Replying to @NewRiverInvest @o_wutang
Only if you use quite short-term estimates for volatility and correlation! Risk parity does perfectly well with long-run (eg 10y) vol estimators and correlation doesn’t need to be an input at all if you have a working model of the factors driving risk premiums in each asset class
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Would love if you could expand on this! I can understand using slower vol signals, but how exactly do you substitute out correlations from the optimisation?
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You have a rough model of the factors that drive each asset class (eg equities long real growth, nominal bonds are long real rates and short inflation, commodities long real growth and long inflation, mortgages long real rates, short inflation and rates vol etc)
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Each macro factor contributes some risk and some return. The goal is to have equal risk *in each factor* so you are insensitive to macro outcomes. If two assets are exposed to the same factor you have less of each of them. If they are exposed opposite, you have more of them.
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eg equities and corporate credit are both exposed to real growth which justifies smaller positions than if you balanced risk among asset classes. Nominal govt bonds and commodities have opposite exposure to real growth and inflation which justifies larger positions in both.
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