How Does Dynamic Risk Management Work?

20 September 2017  |  Professor Stefan Mittnik, PhD
How Does Dynamic Risk Management Work?
Market crashes rarely arrive unannounced, the trick is to recognise the warning signs.
We implement an investment process which is able to pick up on some of the indicative market movements, leading to greater investment stability over the long term.

A founding principle lies at the heart of every investment manager – a basis on which to profit from the seemingly confused vagaries of the stock market. Scalable Capital relies on dynamic risk management. Effective use of this investment style should mean that clients’ portfolios are more stable than markets during periods of turbulence. This yields above-average risk-adjusted returns and should enable a better night’s sleep.

The foundation of dynamic risk management is actually fairly straightforward: if the risk within a portfolio increases, the number of risky assets in that portfolio (such as equities) is reduced. This action is reversed if the risk in the portfolio falls – the number of risky assets is increased. The level of risk in the portfolio is constantly analysed and managed. But this is easier said than done. The process only works if signs of market movements are picked up in good time, it isn’t much use if the markets have already crashed.

Dynamic risk management observes and forecasts market risk (volatility). It picks up tremors of volatility in the markets and adjusts portfolios accordingly. Not all market downturns can be predicted this way – the methodology will struggle to predict crashes caused by external events, such as terrorist attacks or natural disasters which come without warning. But some market downturns are preceded by clear warning signs.

Here we look at the 2008 global financial crisis to better understand how dynamic risk management is able to identify these early tremors. The charts below show the following:

  • The 3-year returns of the major US stock index, the S&P 500.
  • The daily returns of this index over the same period.
  • The risk of the index as measured by value-at-risk (VaR).

So what happened? There are two events that really stand out. On 17 July 2007, US investment bank Bear Stearns reported that two of its hedge funds were virtually worthless because they had used derivatives to speculate on mortgage loans. Bear Stearns’ problems, arising from the issues in the US real estate market, should have come as a warning sign to the financial industry, but they were largely ignored. The S&P 500 kept going sideways, with a few swings up and down, for quite some time after this event. By the time the financiers realised the full extent of the real estate bubble, it was too late to do anything about it.

The second major event was the failure of Lehman Brothers, reported on 16 September 2008. It had now become clear that the real estate crisis was eroding even the most powerful financial houses. As terrifying as this realisation was, the S&P 500 didn’t fall immediately on the news. Two weeks passed before the crash really began. By the end of the year, the crash had destroyed roughly 16 trillion dollars worldwide.

Dynamic Risk Management During the Financial Crisis

The S&P 500 didn’t fall immediately after the failure of the Bear Stearns funds in the summer of 2007. It swung up and down for a few months before collapsing. But…

Dynamic Risk Management During the Financial Crisis: S&P 500 2006 to 2009

…the daily returns of the index were already fluctuating strongly immediately after the collapse…

Dynamic Risk Management During the Financial Crisis: Daily Returns

...and the risk measure value-at-risk (VaR) rose steadily from this point onwards (upwards of 20% VaR)

Dynamic Risk Management During the Financial Crisis: Risk (VaR %)

Past performance or future projections are not indicative of future performance.

The crucial point is this: the heavy fluctuations that erupted following Bear Stearns and Lehman were clearly visible in the data. This is shown in the second chart which reflects the fluctuations of the daily returns of the S&P 500. This chart shows what is really going on under the surface. You can see that when the two Bear Stearns funds collapsed, the market entered a new phase. The fluctuations in daily returns doubled and a similar change can also be seen around the Lehman collapse. The pattern begins to change before Lehman even applied for insolvency and the fluctuations remained at three times the level of the previous period for a few weeks.

What does that mean for the investor? The price fluctuations act as an alarm. Those who switched into lower-risk assets after the Bear Stearns crisis would have been at least partially protected against the downturn of 2008. Those who waited until the shock demise of Lehman to say goodbye to their riskier asset classes would have escaped with a few scratches. Most investors took a couple of weeks to realise the full impact of this event, subsequently pulling their money out and causing one of the biggest financial meltdowns in recent history.

To summarise: the catastrophic bear market did not come out of the blue, even if some investors missed the early warning signs. Dynamic risk management is able to pick up these indicators and adjust portfolios accordingly, by constantly measuring risk in the markets.

However, this method is not completely infallible, as mentioned earlier. For example, any risk management process would have struggled to predict the devastating disaster at the Japanese nuclear power plant in Fukushima in March 2011. No one can foresee these natural disasters, but dynamic risk management will be able to pick up an increase in market fluctuations and adjust positions accordingly. This isn’t as trivial a task as monitoring daily returns, as shown in the second chart above. Modern financial econometrics offer us a much more advanced set of tools to work with.

The third chart illustrates just that. It shows the value-at-risk (VaR) of the S&P 500 over three years. This risk measure specifies the percentage loss that the index will not exceed over the next one-year period, with a probability of 95%. After the collapse of the Bear Stearns funds in 2007, the loss threshold was almost consistently over 20%, when it had almost always been below that mark beforehand. By Q3 2008, there was a 5% chance of losing more than 80% over the course of the next year.

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To illustrate, go back to July 2007 and imagine you are an investor willing to accept 20% VaR (you’re comfortable with a 5% chance of losing more than 20%). At this time you would have had some US equities in your portfolio and this asset would be getting riskier. Had that portfolio benefitted from dynamic risk management, the increase in the risk level of US equities would have been picked up early and the asset allocation adjusted to prevent your portfolio’s risk level from increasing beyond its target level. This shift in asset allocation would have averted a lot of expensive damage away from your portfolio.

High Risk, High Return?

And how about the rule that more risk generates more return? Instinctively, it may feel like dynamic risk management goes against this rule as it stops assets being exposed to phases of high risk. However, the ‘high risk leads to high returns’ rule only works over a long-term average. In the short term, we now know that increases in risk actually lead to lower returns and even losses.

Two American scientists from the Yale School of Management, Alan Moreira and Tyler Muir, recently published a paper which explored risk-based investment strategies. They analysed how a risk-based strategy would have performed over the past 90 years relative to a simple buy-and-hold strategy. The risk-based strategy delivered an average annual return of 11%, two percentage points more than the buy-and-hold strategy. Muir summarises: “In volatile markets, there’s a lot of additional risk that investors are exposed to, and if they’re not being adequately compensated for that risk, then the right thing to do, actually, is to exit”.

This is where the strengths of dynamic risk management are best illustrated; it avoids risks that do not pay off (to read more, refer to our Performance Optimisation web page) and seeks risks that investors get rewarded for – in line with their individual tolerance for risk. This methodology doesn’t absolutely guarantee success, but dodging a few violent downturns will benefit your returns over the long term.

Image: Arnold Exconde/

Risk Warning – With investment comes risk. The value of your investment can go down as well as up and you may get back less than you invest. Past performance or future projections are not indicative of future performance. We do not provide any investment, legal and/or tax advice. If this website contains information regarding capital markets, financial instruments and/or other topics relevant for investments of assets, the exclusive purpose of this information is to give general guidance on investment management services provided by members of our group. Please note our Risk Warning and the Website Terms.


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Stefan Mittnik
Professor Stefan Mittnik, PhD
Stefan is Professor of Financial Econometrics and Director of the Centre for Quantitative Risk Analysis at the Ludwig Maximilians University in Munich. He served on the Research Advisory Board of the Deutsche Bundesbank and was research director at the CFS and the Institute for Economic Research in Munich. He has spent about 30 years researching, analysing, modelling and forecasting financial risk.