Strategy #0000000039 · Risk management and allocation
Volatility targeting
Scale base exposure by estimated volatility and state leverage caps, costs and estimation lag.
A volatility-targeting rule scales base exposure by target volatility divided by estimated realised volatility, then applies leverage and position limits. It scales risk; it does not create a positive return forecast. Inverse-volatility allocation, risk parity and minimum-volatility portfolios are related but distinct.
Volatility scaling sets target exposure; jumps, gaps and leverage limits can make realised risk differ.
Three ways in
Understand the story. Then inspect the risk.
Start with a familiar situation, check the payoff, then test how the numbers change.
The idea · in two minutes
Can scaling exposure to recent volatility keep portfolio risk near a target?
Rose targets 10% annualised volatility while the recent estimate is 15%, scaling a 1× base exposure to about 0.67×. This is risk sizing, not a higher return forecast.
Jack wants leverage in calm markets; Rose adds a cap, estimation window, rebalance frequency and costs to avoid chasing noise.
Crisis gaps make estimates lag, and deleveraging after a spike can miss a rebound. Testing needs point-in-time estimates, next-tradable execution and out-of-sample evaluation.
- Target / realised volatilityScale exposure by target-to-realised volatility, apply a leverage cap, then calculate assumed next-period return less costs.
- Capped exposureNot a backtest or forecast; the one-period model omits gaps, financing, estimation error, tax and execution.
- Assumed next-period scenario less costIllustrative expiry outcome.
Illustrative research examples only. They are not live signals or a recommendation.
The mechanics · payoff and exposure
w_t=\min(w_{max},w_0\cdot\sigma^*/\widehat{\sigma}_t),\quad R_{p,t+1}=w_tR_{a,t+1}-Costs
w₀ is base exposure, σ* target volatility, σ̂ₜ the estimate available at decision time and wmax the leverage cap. Future asset return is unknown and user-entered.
Greeks: the directional intuition
- 估計延遲
- Measures sensitivity to implied volatility.
- 槓桿路徑
- Measures sensitivity to implied volatility.
- 成本
- Measures sensitivity to implied volatility.
w_t=\min(w_{max},w_0\cdot\sigma^*/\widehat{\sigma}_t),\quad R_{p,t+1}=w_tR_{a,t+1}-Costs
Practical context · published observations
Volatility targeting: read the record with its caveats.
The chart uses the published figures or explicitly labelled study inputs below. A historical index or model replay is not the same as an investor’s realised account return.
不是回測或預測；單期模型不計跳空、融資、估計誤差、稅費或執行。 Source ↗ · Methodology ↗
Change assumptions and inspect the scenario
Inputs illustrate the strategy mechanics; they are not live quotes, forecasts or investment advice.
Variants · same family, different risk levers
Related methods change their signal or exposure.
Each entry states the mechanism and trade-off.
| Related strategy | Relationship | What changes | New cost or risk |
|---|---|---|---|
| Inverse-volatility allocation | Cross-asset weights | Allocates capital inversely to each asset’s volatility. | Ignoring correlation does not equalise risk contributions. |
| Volatility-managed factor | Exposure overlay | Scales a factor position using realised variance. | Deleveraging can miss rebounds. |
| Volatility-control index | Rules-based index | Adjusts index exposure and cash. | Rules, funding and tracking error need separate tests. |
Not a backtest or forecast; the one-period model omits gaps, financing, estimation error, tax and execution.
Read beyond the summary
References
Primary sources and research used to describe the strategy and its limits.
- 01Moreira & Muir (2017), Volatility-Managed Portfolios.research
- 02NBER Working Paper 22208.research