Volatility not an accurate risk measure
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Dan Kemp is head of investment consulting and portfolio management with Morningstar Investment Management Europe.
Canadian philosopher Marshall McLuhan once said that "we drive into the future using only our rear-view mirror". Nowhere is this description more accurate than in the investment industry and specifically the area of risk management.
Rather than engaging in a realistic and informed conversation about future risks, the industry has become myopically focused on a simplistic, backward-looking measure -- volatility.
In this short article, we examine the problems inherent with this simplistic approach, and suggest steps to improve the accuracy of the risk-profiling process to reduce the suitability mismatch that we perceive with some existing tools.
The adoption of simple risk-mapping tools that rely on volatility is an understandable reaction to increased regulatory scrutiny and the disappointing performance of many multi-asset portfolios during the financial crisis.
However, in an attempt to create a demonstrable process for matching clients to funds, risk-assessment tools have tended to "boil down" risk to the single measure of volatility. In doing so, these tools ignore other risks, including permanent capital loss, illiquidity, default, failure to meet objectives, and counterparty.
While the use of volatility as a proxy for risk provides a statistical basis for describing the randomness of capital market movements, its reliance on assumptions and its poor predictive power mean that volatility is both a weak proxy for risk and an unreliable way to predict severe capital loss.
It is therefore of limited use in matching funds to clients. The calculation of volatility makes two big assumptions: first, that returns are normally distributed, and second, that correlations are stable.
Neither is true. A cursory glance at equity return data over very long periods shows that the distribution of returns is subject to both skewness and positive kurtosis. This means that the typically used metrics of mean return and volatility do not fully describe the distribution of returns.
The problem is magnified when using historic data from multi-asset portfolios to predict the future. Traditional risk tools tend to use average correlation data. In reality, the correlation relationships that create the diversification benefit of a multi-asset portfolio are unstable.
Correlations tend to increase during periods of stress, reducing diversification benefits and increasing losses beyond that which would be expected using average data. As a consequence of these failings, investors who base their risk assessment on volatility are like McLuhan's driver -- they are susceptible to nasty surprises that are not obvious from the view they have of the market.
By focusing on absolute levels of volatility as the key measure of risk, investors are prevented from buying risk assets when prices are low as these typically corresponded to periods of high volatility.
Equally, portfolio managers are encouraged to buy risk assets when prices are high. This buy high, sell low strategy is unlikely to be in the clients' best interests. The practical problems with this approach are especially evident when using absolute levels of volatility to match funds to client risk profiles.
Morningstar has recently conducted research that shows that the volatility of a conventional multi-asset portfolio varies widely through the market cycle. We created a series of multi-asset portfolios and tracked their volatility using the approach stipulated for the calculation of a fund's synthetic risk return indicator (SRRI) that is included in key information documents (KIID).
The volatility of these portfolios varied significantly over time. For example, the volatility of a moderate risk portfolio comprised of recognised benchmark indices varied by 5.3 per cent over the last 9.5 years.
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