Volatility Targeting in Trading and Portfolio Construction

Volatility targeting is a trading and portfolio construction strategy that tries to achieve a desired level of volatility by dynamically adjusting the allocation of assets.

This approach helps in maintaining a consistent risk level, which can lead to more stable returns and better overall discipline and risk management.

Many institutional traders set volatility targeting levels so their investors can understand their relative risk levels.

Key Takeaways – Volatility Targeting

• Consistent Risk Management
  • Volatility targeting maintains a stable risk profile by dynamically adjusting asset allocations to meet that figure.
• How to Target Volatility
  • Many traders will run backtests on the allocations they’re considering or use quantitative models (e.g., GARCH for existing portfolios, Monte Carlo simulation for prospective allocations).
• Adaptive Strategy
  • Some versions adapt to market conditions, reducing exposure during high volatility and increasing it during low volatility.
• Enhanced Risk-Adjusted Returns
  • By stabilizing performance, it can improve risk-adjusted returns and provide better overall portfolio stability.

Principles of Volatility Targeting

Adjusting Exposure

The core idea is to increase or decrease the exposure to various assets based on their volatility.

When the volatility of an asset class is low, the strategy increases exposure, and when volatility is high, it reduces exposure.

Target Volatility

A pre-determined volatility level is set as the target.

Examples you popularly see include 12%, 15%, 18%, and 24% for institutional funds. (For reference, the S&P 500 historically averages around 15%.)

The portfolio is rebalanced to ensure the actual volatility matches the target.

Risk Management

Consistent Risk Profile

By targeting a specific volatility level, the strategy ensures that the portfolio’s risk profile remains consistent over time.

This enforces discipline and the likelihood of large drawdowns during volatile periods.

Non-professional traders typically blow out their risk levels and have large drawdowns.

Adaptive to Markets

This strategy can be adaptive to changing markets, automatically reducing risk exposure during turbulent periods and increasing it during calmer periods.

Implementation in Trading

Asset Selection

A diverse range of assets is selected, including equities, bonds, commodities, and alternative investments.

For those who trade more frequently, that trading is expected to be done within that particular structure to keep volatility at targeted levels.

The selection process considers historical volatility and correlation among assets.

Private assets and those that aren’t marked-to-market have their risk evaluated in different ways.

Volatility Measurement

Volatility is measured using statistical measures like standard deviation or the more sophisticated GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model to forecast future volatility.

Some traders will also run Monte Carlo simulations.

Knowing that financial distributions tend to be fat-tailed and understanding higher-order moments of distributions is also helpful.

Private Assets

Private assets and those not marked-to-market are typically evaluated using models that estimate their risk based on historical data, cash flow projections, and comparable market benchmarks, rather than real-time market prices.

This helps in assessing their inherent risks and potential returns.

How to Target Volatility in a Portfolio

Volatility is a mean-reverting process, meaning it tends to fluctuate around a long-term average.

Though there, of course, can be structural changes over time based on duration, credit risk, and so on.

When volatility deviates significantly from this average, it will likely return to it over time.

Let’s look at implementation strategies.

Algorithmic Adjustments

Real-Time Monitoring

Use algorithms to determine volatility levels of various asset combinations and monitor an existing portfolio’s volatility.

These algorithms can dynamically adjust asset allocations to make sure the portfolio’s volatility remains close to the target level.

Rebalancing Rules

Establish rules within the algorithm to increase exposure to assets when volatility is low and decrease exposure when volatility is high.

Backtesting

Historical Analysis

Perform backtesting to analyze how different mixes of assets have performed in various volatility regimes.

This helps in understanding the impact of asset allocation changes on portfolio volatility.

For example, let’s say a trader wanted to structure their portfolio using three assets (stocks, bonds, gold) to have a target volatility of 10% and was wondering how much of an equities allocation could be tolerated.

He tried two example allocations:

Sample Portfolio

Performance Summary

Optimization

Use historical data to optimize the asset mix to achieve the desired volatility target.

This involves testing various asset combinations and their respective volatilities to find the optimal mix.

Practical Steps

Set a Target Volatility Level

Define the desired level of volatility for the portfolio based on objectives and risk tolerance.

Measure Current Volatility

Use statistical models or algorithms to measure the current volatility of the portfolio.

Common methods include standard deviation and GARCH models.

Adjust Asset Allocation

Increase allocations to higher volatility assets when current volatility is below the target and decrease allocations when current volatility is above the target.

Rebalancing Mechanism

Frequency

Rebalancing can occur daily, weekly, or monthly, depending on the desired responsiveness of the portfolio.

Position Sizing

Positions are adjusted based on the current and target volatility levels.

For example, if the current portfolio volatility exceeds the target, positions are reduced proportionally.

Benefits of Volatility Targeting

Smoother Performance

By maintaining a consistent volatility level, the strategy can lead to smoother performance and improved risk-adjusted returns, often measured by the Sharpe ratio or similar.

Downside Protection

During periods of high market volatility, reducing exposure can protect the portfolio from significant losses.

Investor Confidence

A strategy that aims for consistent risk levels can provide psychological comfort, knowing their risk isn’t changing significantly.

Applications

Hedge Funds

Many quantitative hedge funds use volatility targeting to manage risk.

They use algorithms to adjust positions based on real-time volatility estimates, so that the portfolio remains within the desired risk parameters.

Challenges and Considerations

Accuracy of Volatility Estimates

The effectiveness of volatility targeting depends on the accuracy of volatility forecasts.

Incorrect estimates can lead to inappropriate adjustments and suboptimal performance.

Overfitting

There is a risk of overfitting the model to historical data, which may not hold in future markets.

FAQs – Volatility Targeting

Why is volatility mean-reverting?

Volatility is mean-reverting because it tends to fluctuate around a long-term average due to the intrinsic nature of the assets (e.g., duration, credit risk), market mechanisms, and trader/investor behaviors. 

When volatility deviates significantly from its average, market participants often adjust their positions, which can help bring volatility back to its average level over time.

What are the benefits of volatility targeting?

• Consistent Risk Profile – Volatility targeting helps keep a portfolio’s risk profile stable over time. This reduces the likelihood of large drawdowns during periods of high volatility.
• Improved Risk Management – By dynamically adjusting asset allocations based on volatility, the strategy provides better risk management.
• Smoother Performance – Maintaining a consistent volatility level can lead to smoother performance and more stable returns.
• Comfort – A consistent risk level can provide psychological comfort to those pursuing the strategy. It can make it easier to stay invested and continue to trade during volatile periods.

How do you set a target volatility level?

Setting a target volatility level involves:

• Defining Your Risk Tolerance – Determining the trader/investor’s risk tolerance and investment objectives. For institutional funds, common target volatility levels are 12%, 15%, 18%, and 24%, with the S&P 500 historically averaging around 15%.
• Performance Goals – Aligning the target volatility with desired performance outcomes, balancing the trade-off between risk and return.
• Historical Analysis – Reviewing historical volatility of the asset classes in the portfolio and the overall market to understand typical volatility levels.
• Regulatory Requirements – Considering any regulatory or compliance requirements that may impact the acceptable level of portfolio volatility. Pensions, sovereign wealth funds, endowments, foundations, hedge funds, etc., may all have contractual or regulatory requirements on the type of risk they’re able to take on.

How does backtesting work in volatility targeting?

Backtesting in volatility targeting involves:

• Historical Data Collection – Gathering historical data on asset prices, returns, and volatility for the assets considered in the portfolio.
• Simulation – Simulating different asset allocation strategies over historical periods to see how they would have performed in various volatility regimes.
• Performance Metrics – Evaluating performance metrics such as annualized return, standard deviation, Sharpe ratio, Sortino ratio, and maximum drawdown for each strategy.
• Optimization – Using the backtested results to optimize the asset mix, looking to achieve the desired target volatility while maximizing risk-adjusted returns.
• Scenario Analysis – Conducting scenario analysis to test the robustness of the strategy under different market environments and stress scenarios.

How does volatility targeting compare to other risk management strategies?

Versus Fixed Allocation

Unlike fixed allocation strategies that maintain constant asset weights, volatility targeting may adjust allocations dynamically based on current volatility.

Versus Value-at-Risk (VaR)

While VaR focuses on potential losses within a specific confidence interval, volatility targeting mostly aims for a consistent volatility level, which can provide more stability in returns.

VaR is heavily focused on tail risk while volatility targeting is concerned mostly with what risk might look like smoothed out over time.

Versus Stop-Loss Strategies

Stop-loss strategies involve selling assets when losses reach a certain level, whereas volatility targeting proactively adjusts exposures to maintain a target volatility.

Versus Diversification

Diversification reduces risk by spreading investments or capital across uncorrelated assets or different trades, while volatility targeting goes a step further by adjusting the exposure to these assets based on their volatility and how they fit into a cohesive whole in the portfolio.

Can volatility targeting improve risk-adjusted returns?

Yes, volatility targeting can improve risk-adjusted returns by:

• Reducing Drawdowns – By decreasing exposure during high volatility periods, the strategy can limit significant losses, which improves the Sharpe ratio and other risk-adjusted return metrics.
• Stabilizing Performance – Maintaining a consistent volatility level can reduce the variability in performance.
• Adaptive Risk Management – The dynamic adjustment of asset exposures based on volatility ensures that the portfolio is continuously optimized for current market conditions.

How do you measure the current volatility of a portfolio?

Measuring the current volatility of a portfolio involves:

• Standard Deviation – Calculating the standard deviation of the portfolio’s returns over a specific period to assess its historical volatility.
• GARCH Models – Using advanced statistical models like GARCH to forecast future volatility based on historical return data.
• Rolling Window Analysis – Applying rolling window analysis to compute volatility over different time periods. Captures recent changes in market conditions.
• Implied Volatility – Considering implied volatility from options markets, which reflects market expectations of future volatility.

How do you balance the trade-off between risk and return in volatility targeting?

Balancing the trade-off between risk and return in volatility targeting involves:

• Setting Appropriate Targets – Establishing realistic target volatility levels that align with the trader’s risk tolerance and return objectives.
• Dynamic Adjustments – Continuously adjusting asset allocations to maintain the target volatility.
• Risk-Return Optimization – Using optimization techniques to find the optimal asset mix that provides the highest expected return for the target level of risk. We have an article about that here.
• Monitoring and Rebalancing – Regularly monitoring the portfolio’s performance and rebalancing as needed to make sure it remains aligned with the target volatility and desired risk-return profile.