Sign up to save your podcasts
Or
Share Volatility-based allocation: avoid overexposure when copying traders.
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Volatility-based allocation: avoid overexposure when copying traders.
Summary:
- The episode discusses volatility-based allocation for copy trading as a way to avoid overexposure and painful drawdowns when volatility spikes.
- Core idea: allocate capital based on each trader’s historical volatility, using inverse-volatility weights so steadier traders get more exposure.
- Practical steps include: gather a universe of traders, obtain volatility data, and implement a risk-management framework with minimum/maximum exposure per trader and an overall portfolio risk target.
- How weights are calculated: compute each trader’s historical volatility, set weights as w_i = (1/σ_i) / sum(1/σ_j), and apply exposure caps (e.g., 5%–35% per trader). Rebalance regularly (e.g., monthly) to reflect changing volatility and correlations.
- Example given: three traders with volatilities 0.20, 0.35, 0.15 yield inverse-vol weights that allocate roughly 34%, 20%, and 46% respectively, illustrating that steadier traders receive more exposure without domination.
- Discussion prompts: assess whether your portfolio already uses diversification or relies on a single star trader; consider risks of concentrating on one operator, especially during volatile streaks.
- Additional insights: high macro volatility can raise asset correlations, reducing diversification; volatility filters and exposure limits tend to smooth maximum drawdowns.
- Practical considerations: decide between monthly or more frequent rebalances, set exposure stops (e.g., if a trader loses 8–10% of their allocated share), and automate rebalances if possible.
- Philosophical takeaway: volatility is a compass that helps navigate markets; the goal is to balance potential gains with stability, not copy the biggest winner.
- Practical enhancements: track each trader’s maximum drawdown and correlations; adjust weights if Drawdown spikes or correlations change; maintain discipline to avoid overconfidence during favorable periods.
- Closing: invites listeners to follow the speaker’s strategies via links in the podcast description and emphasizes disciplined risk management for sustainable growth.
Remeber you can contact me at
View all episodes
By Andrés DíazSummary:
- The episode discusses volatility-based allocation for copy trading as a way to avoid overexposure and painful drawdowns when volatility spikes.
- Core idea: allocate capital based on each trader’s historical volatility, using inverse-volatility weights so steadier traders get more exposure.
- Practical steps include: gather a universe of traders, obtain volatility data, and implement a risk-management framework with minimum/maximum exposure per trader and an overall portfolio risk target.
- How weights are calculated: compute each trader’s historical volatility, set weights as w_i = (1/σ_i) / sum(1/σ_j), and apply exposure caps (e.g., 5%–35% per trader). Rebalance regularly (e.g., monthly) to reflect changing volatility and correlations.
- Example given: three traders with volatilities 0.20, 0.35, 0.15 yield inverse-vol weights that allocate roughly 34%, 20%, and 46% respectively, illustrating that steadier traders receive more exposure without domination.
- Discussion prompts: assess whether your portfolio already uses diversification or relies on a single star trader; consider risks of concentrating on one operator, especially during volatile streaks.
- Additional insights: high macro volatility can raise asset correlations, reducing diversification; volatility filters and exposure limits tend to smooth maximum drawdowns.
- Practical considerations: decide between monthly or more frequent rebalances, set exposure stops (e.g., if a trader loses 8–10% of their allocated share), and automate rebalances if possible.
- Philosophical takeaway: volatility is a compass that helps navigate markets; the goal is to balance potential gains with stability, not copy the biggest winner.
- Practical enhancements: track each trader’s maximum drawdown and correlations; adjust weights if Drawdown spikes or correlations change; maintain discipline to avoid overconfidence during favorable periods.
- Closing: invites listeners to follow the speaker’s strategies via links in the podcast description and emphasizes disciplined risk management for sustainable growth.
Remeber you can contact me at