Volatility Control Strategies and Risks

Any investor would theoretically wish to leave behind human bias, slow reactions, and instead automate (systematize with predefined rules) their investment decisions. The dream, in short, is to exploit millions of opportunities, however small, through tools that few will possess. The path chosen in finance, mainly for this purpose, is systemic strategies—among them, volatility control.

This strategy shows that each investment cycle is defined by collective knowledge that seeks to prevent the mistakes of previous cycles. In practice, it models to mitigate already known risks.

Volatility Control

Controlling volatility consists of managing assets through continuous rebalancing between holdings of riskier assets and, generally, cash. The risky asset—for example, stocks—must maintain constant volatility. If the residual volatility of stocks increases, more cash is allocated, reducing stock exposure.

This strategy differs from the so-called Constant Proportion Portfolio Insurance (CPPI). CPPI seeks to replicate the delta of an option—that is, the change in the option’s price relative to the change in the underlying asset’s price. The strategy measures the sensitivity of the option premium to the volatility of the underlying asset. For those familiar with options, it seeks to determine whether the option will end “in the money.”

Here lies something very interesting: volatility control leaves room to recover full exposure, because it allows being fully invested at certain times, regardless of capital level. CPPI does not allow this, since it requires deleveraging the portfolio.

But let’s not be deceived. The idea behind it is not to outperform traditional long-term allocations, but rather to achieve better risk-adjusted returns. It also allows proper adjustment of budgeted capital with the portion allocated to risky assets, requiring only an assumption (based on data) of volatility.

Obviously, it demands implementation in highly liquid assets and low (non-punitive) rebalancing costs. If the investor ultimately mandates this system, it is crucial to have full clarity on portfolio risk.

Impact of Volatility Control

Evidence shows that if the market becomes agitated, agitation fosters more agitation. Conversely, in calm periods, greater stability prevails. Thus, in volatility control, strategists tend to exit the market to some extent during volatility episodes, but return in calmer times.

For example: our mandate is 10% volatility, but the market only exposes us to 5%. What will the manager do? Raise us to the mandated 10%. Therefore, to achieve that volatility, among other alternatives, the portfolio is leveraged so that exposure doubles, allowing access to 10% volatility. If the market has 5% volatility, we double exposure through debt, reaching 10%.

If agitation rises beyond the mandated volatility, we reduce exposure to bring it back to the mandated level.

Fears

The problem, however, is that this strategy represents a radical shift. In our grandparents’ era, when something happened, one wanted to hold more and more, hoping the asset would rise again. The logic of volatility control, instead, is to sell when it falls and buy when it rises; we want more when it rises, and less when it falls.

But there are further fears. Academic research investigates what happens when these strategies are automated, systematically feeding machines the correct data. As mentioned, this may complicate matters, because there may be cases where the robot exits precisely when the market needs the opposite position. Moreover, High-Frequency Trading or Commodity Trading Advisor strategies could also increase volatility. Together, they could pose problems.

What Lies Beneath: Volatility and Risk

Volatility has dominated financial engineering because it can be traded—cut, bought, sold, and created like any asset. Since Markowitz’s 1952 paper on portfolio selection, variance (volatility) has been a key indicator. We say, therefore, that stocks are more volatile than bonds, or that one option is more volatile than another. Hence, investors should expect higher returns if there is greater risk. Volatility is how we perceive risk—or at least how we optimize it.

Volatility is also part of how strategists are compared. For example, the “Sharpe Ratio” allows comparing a manager’s return with the volatility of that return. This is part of performance evaluation.

We can add risk management. Managing risk, for this simple comment, means creating rules in the form of limits. It is the need to establish when we already have enough—or too little—of an asset. But the challenge of risk management is finding the exact measure “in general.” If I face a lawsuit where I risk losing $4,000, and another where I could win $4,000, based on probabilities (and in law, certainty never exists), can I claim I have balanced exposure? The problem is correlation. My total exposure is not necessarily a clean sum.

Thus, risk rules must not only consider asset holding limits but also the different hypotheses according to asset quantities. Holding $1,000 in stocks and $1,000 in bonds may be far from the risk ratio of holding $500 in stocks and $1,500 in bonds. Compensation is not always zero.

Correlation in risks is important, because in finance some assets fall when others rise, meaning a portfolio never has real exposure equal to the sum of its assets. Again, volatility comes into play, as it allows analyzing asset correlation. Asset volatility leads to correlation, and thus, risk management.

The point is to learn what happens when volatility becomes an irreplaceable factor in models and algorithms. Some evidence already exists, but we cannot yet conclude whether it will be more or less beneficial than the classical era of finance. What we can affirm is that volatility control strategies, volatility itself, and risks—as ways of measuring, investing, and transforming—are here to stay.