Volatility: What does it tell you about investment risk?

Definition: Volatility measures how much the value of a security, fund or portfolio fluctuates over a given period. It is usually calculated as the standard deviation of returns and is often expressed on an annualised basis. High volatility means larger price swings; low volatility means smaller fluctuations. On its own, volatility tells you neither the direction of price movements nor the maximum potential loss.

In financial markets, volatility is often used as a synonym for risk. But the two are not the same. A stock with sharp price swings does not necessarily produce large losses. Equally, an investment can lose substantial value even when its day-to-day movements are relatively small.

At its most basic, volatility describes the extent to which the value of a security, fund, index or portfolio fluctuates. It measures the size of those movements, not their direction. Yet it is also an important risk metric, a gauge of market expectations and something that can be traded in its own right.

So what does volatility actually tell you about an investment? How is it measured, and why does it matter so much in portfolio management?

• What is stock market volatility, and why do prices fluctuate?
• How is volatility calculated, and what does 20 per cent mean?
• What is the difference between historical and implied volatility?
• Why is volatility not the same as risk, and how can it be managed in a portfolio?
• How can volatility be traded and used for hedging?

Volatility is a statistical measure of how much the price of a security or market fluctuates over a particular period. The larger the price swings, the higher the volatility. Smaller price movements indicate lower volatility.

Consider a simple example: A stock whose price moves by around one per cent a day on average is less volatile than one that regularly swings by five, six or seven per cent.

The crucial point is that volatility says nothing about direction. Sharp price rises increase measured volatility just as sharp falls do. High volatility therefore implies neither poor performance nor particularly high returns.

One important reason is that market participants have different expectations. Investors act on different information, hold different views and pursue different objectives. One investor may buy a company's shares because they believe it is undervalued, while another sells the same shares because they are less optimistic about its prospects.

Market microstructure also shapes price movements. This includes the order book, available liquidity and trade execution, meaning the actual process of carrying out a buy or sell order. Large orders can move prices significantly, particularly when liquidity is limited.

Trading hours and patterns of market activity matter too. A large transaction during Asian trading hours, for example, can move prices before investors in Europe or the US become active.

New information is another catalyst. Quarterly earnings, company announcements or clinical trial results relevant to regulatory approval can abruptly change expectations and temporarily increase volatility.

Technical factors also play a part. When a stock joins a major index, index-tracking exchange-traded funds (ETFs) may generate additional buying. Rebalancing, which involves adjusting portfolio weights, and trading to hedge existing derivatives positions can also amplify price movements.

Volatility is therefore not driven by economic news alone. It reflects the interaction of expectations, trading activity, liquidity and the mechanics of the market itself.

Volatility is usually calculated from a security's returns over a specified period. The calculation uses standard deviation, which measures how widely individual returns are dispersed around their average.

The first step is to calculate the percentage change between consecutive prices. The next is to measure how far those changes deviate from their average. Put simply, the greater the dispersion of returns, the higher the volatility.

The choice of measurement period depends on the question being asked. To examine short-term market movements, an investor might look at fluctuations over the past month. For a longer-term perspective, a year or several years may be more useful.

To make different figures easier to compare, volatility is often annualised, or expressed on a yearly basis. For example, the standard deviation of daily returns can be converted into an annual figure by multiplying it by the square root of the assumed number of trading days in a year.

Both the measurement period and the calculation method affect the result. Low volatility over the past month does not necessarily mean that volatility has also been low over several years.

Suppose an investment is worth EUR 10,000 and has annual volatility of 20 per cent. What can you infer from that figure?

Under the highly simplified assumptions that annual returns follow a normal distribution and the expected return is zero per cent, the investment would end the year between EUR 8,000 and EUR 12,000 in roughly two-thirds of the modelled outcomes.

This example illustrates the statistical meaning of one standard deviation. It should not be taken as an assurance that the investment's actual value will fall within those limits.

In practice, returns may not follow a normal distribution. In particular, exceptionally large price movements can occur more often than a simple statistical model would suggest.

Nor does volatility of 20 per cent mean that an investor can lose no more than 20 per cent. The figure describes the dispersion of returns, not the maximum possible loss.

There is no guaranteed relationship between volatility and returns either. Investors often demand a higher expected return as compensation for greater volatility. Whether that additional return is actually achieved remains uncertain.

Financial markets distinguish between historical, or realised, volatility and implied volatility. Both describe fluctuations, but they look at them from different perspectives in time.

• Historical, or realised, volatility is calculated from price movements that have already occurred. It shows, for example, how much a stock fluctuated over the past month or year. This helps investors understand an investment's past behaviour, but it does not reliably predict future price movements. A stock that has barely moved for an extended period can suddenly experience sharp swings within a matter of days.
• Implied volatility, by contrast, is forward-looking. It is derived from option prices and reflects the level of future volatility currently priced into the options market. An option is a derivative whose price depends, among other things, on the expected volatility of the underlying security. Implied volatility can be calculated from that price.

Different time horizons can be considered, such as a week, a month or a year.

However, implied volatility is not a precise forecast either. Option prices reflect not only market expectations but also the risk premiums that investors demand for taking on risk.

Volatility indices show the expectations for future fluctuations that are priced into the options market. Well-known examples include the CBOE VIX® for the US S&P 500®, the VDAX-NEW for Germany's DAX® and the VSTOXX® for the European EURO STOXX 50®.

These indices are calculated from the prices of the relevant index options. The CBOE VIX®, VDAX-NEW and VSTOXX® focus on the annualised volatility priced in for the next 30 days.

A sharp rise in one of these indices signals that larger price swings are being priced in. It does not automatically mean that stock prices will fall.

Historically, stock market returns and volatility indices have often been negatively correlated, particularly during periods of severe stress. In those periods, rising uncertainty has tended to coincide with falling stock prices.

There are also periods when stock prices and volatility rise sharply together. Volatility indices therefore offer useful information about expected fluctuations, but no unambiguous signal about the direction of the stock market.

Volatility is one of the most widely used risk metrics in financial markets, but it has limits. It measures the size of fluctuations, not their ultimate effect on the value of an investment.

Comparing two stocks makes the distinction clearer.

One stock might swing sharply up and down each day without suffering a particularly large decline from its previous high over a longer period.

Another might move much less from day to day but lose value steadily over several months. Despite its lower volatility, the second stock could experience a much larger overall decline.

Maximum drawdown is an important complementary measure. It describes the largest peak-to-trough decline over the period being examined.

Where volatility measures the size of fluctuations, maximum drawdown shows the largest decline an investment actually experienced between a peak and a subsequent low.

Fat-tail events also matter. These are rare, unusually large market moves that can be underestimated when returns are assumed to follow a simple normal distribution.

A more comprehensive risk assessment therefore considers not only volatility and maximum drawdown, but also extreme stress scenarios and the behaviour of different investments during a crisis.

Stocks typically have higher volatility than many bonds with medium-term maturities. However, bond volatility depends on factors including the time remaining to maturity and prevailing market conditions, so the comparison does not hold in every case.

For ETFs and investment funds, diversification also matters. When a portfolio holds a range of different securities, their price movements can partly offset one another.

A broadly diversified equity fund may therefore be less volatile than some of its individual holdings. That does not mean that ETFs or investment funds are inherently low-volatility investments.

In actively managed investment funds, portfolio managers can also adjust the holdings to influence the portfolio's volatility.

A portfolio's volatility depends on more than the volatility of its individual holdings. Their correlation, or the extent to which their prices move together or in opposite directions, also matters.

A simple thought experiment illustrates the relationship.

Consider an equally weighted portfolio of two stocks. If stock A rises by five per cent while stock B falls by five per cent, the two movements cancel each other out in this simplified example.

If the stocks maintained a perfectly negative correlation of -1 and their volatilities were appropriately matched, their movements could theoretically offset each other entirely. This is, however, an idealised extreme.

As a general rule, the lower the correlation between portfolio holdings, the greater the potential diversification benefit.

The difficulty is that correlations are not constant. In calm markets, securities may move largely independently of one another. During a severe market crisis, their correlations can rise abruptly.

Diversification that previously worked well may then offer substantially less protection.

Comprehensive risk management therefore looks beyond the historical volatility of individual investments to consider how they interact under different market conditions.

The appropriate portfolio composition ultimately depends on an investor's objectives and willingness to tolerate fluctuations and losses along the way.

Volatility is more than a risk metric. It can also be traded through derivatives and incorporated into an investment strategy.

Implied volatility plays a central role. Embedded in option prices, it allows market participants to respond to changes in expectations for future price fluctuations.

The instruments used include options and more complex derivatives such as volatility swaps. While an option's performance depends on several factors, volatility swaps provide more direct exposure to the difference between an agreed volatility level and the volatility subsequently realised.

One feature of the options market is that implied volatility has historically often exceeded the volatility subsequently realised.

This difference is associated with what is known as the volatility risk premium.

One reason for the premium is that market participants require compensation for assuming volatility risk. Insurance offers a useful analogy.

An insurer receives a premium for accepting a particular risk. If an insured event occurs, the insurer must cover the associated costs.

Short volatility strategies follow a similar principle. They sell volatility, for example by writing options, with the aim of collecting a premium.

If realised volatility remains below the level priced into the market, the strategy may generate a return. If volatility rises unexpectedly and sharply, however, losses can be substantial.

The volatility risk premium is therefore not a guaranteed source of return. It is potential compensation for taking on risk.

There are two broad approaches.

• Short volatility strategies seek to earn premiums by assuming volatility risk. 
• Long volatility strategies, by contrast, take positions that can benefit from rising volatility.

The two approaches can also be combined. For example, a strategy might sell implied volatility on an equity index while buying options on individual stocks to hedge particular risks.

Other approaches use the premiums collected to finance protection against exceptional market movements.

The important point is that hedging has a cost, and even complex strategies cannot eliminate risk entirely.

The dangers of an inadequate assessment of risk were illustrated by the episode known as "Volmageddon" in February 2018.

A sudden surge in volatility caused heavy losses in certain short volatility products. One well-known ETF following such a strategy lost more than 90 per cent of its value in a very short time and was subsequently redeemed early.

Before that event, the product had shown relatively low historical volatility for an extended period.

The episode highlights a fundamental limitation of relying solely on past fluctuations: Low historical volatility does not protect against extreme future losses.

Volatility strategies therefore require a thorough understanding of the underlying derivatives, potential extreme scenarios and the mechanics of the relevant markets. Active, forward-looking risk management and carefully designed hedges play an important role.

Volatility measures how much the value of an investment fluctuates. It tells you neither the direction of price movements nor, directly, the size of potential losses.

It is an essential risk metric, but it is not sufficient on its own. A fuller picture of portfolio risk emerges only when volatility is considered alongside other measures such as maximum drawdown, correlations between investments and the effects of extreme market conditions.

Implied volatility also shows that fluctuations are not merely something to measure. They can be traded through derivatives, creating opportunities to earn risk premiums or hedge exposures, while introducing risks of their own.

Understanding volatility therefore means distinguishing between the size of price fluctuations, the actual risk of loss and the market's expectations for future fluctuations.