Managing (systemic) risk

The importance of managing systemic risk

Economic and social benefits

• Systemic risk characterizes the contingency of a malfunctioning financial system. Systemic risks build gradually but materialize abruptly and rarely and, hence, are mostly neglected in the day-to-day considerations of investment managers.
  • Yet, how a manager prepares for and deals with systemic risk often makes or breaks long-term performance. Most investment strategies rely on some combination of directional or alternative risk premia and information-based relative value. Systemic crises typically derail all of these. This is because in contrast to normal market drawdowns, systemic pressures trigger funding, accounting or legal constraints that force position liquidation with little freedom of choice for portfolio managers. When a systemic crisis escalates the priority of institutions shifts from seeking returns to short-term capital preservation. As a result, the principles of efficient positioning or flows may not only be suspended but reversed, with the best-value positions (by conventional standards) posting the greatest loss. This happens because value is correlated with positions in normal times. Diversification is of little help because the scope of inefficient flows is wide. Empirical research shows that a single global financial cycle floats or sinks most markets at the same time. (view post here). Hence, for responsible portfolio managers systemic risk is an extreme form of setback risk, i.e. of a gap between downside and upside risk unrelated to fundamentals (view summary of setback risk here).
• If asset managers prepare for systemic crises, the impact of shocks on the financial system and the economy will be less severe.  It is often the very lack of protocol for difficult situations that leads to poor decisions and crisis escalation (view post here). And the record of the financial industry in respect to crises is poor. There is ample evidence of regular boom and bust investment cycles, herding, trend-chasing (view post here), inefficient expectation formation, and speculative bubbles. It has been shown that standard cognitive behavior is often inconsistent with efficient markets (view post here).
• Managers’ consideration of specific systemic crisis risk can also reduce the probability of such crises. Most crises follow from excesses and economic imbalances. An excess of leverage reduces the capacity to absorb drawdowns. The recognition of risk typically increases the price for exposure to the risk and hence discourages its build-up. A relevant example for the future could be the unsustainable use of the environment and climate change (view post here). However, markets can only serve sustainability if managers think longer term, rather than in terms of annual performance fees. That is because “shorts” and protection strategies are typically negative carry trades with unknown duration.

Approaches to managing systemic risk

Preparing crisis strategies

Researching systemic risk creates awareness of vulnerabilities in the financial system. This may not suffice to predict crises but prepares managers’ judgment and response when crises occur. Familiarity with systemic risk factors shortens decision-making time, increases the probability of portfolio adjustment ahead of the crowd and, thereby, reduces both mark-to-market losses and trading costs.

• Focusing on weak spots: It is hard to predict the timing and dynamics of systemic crises. However, it is possible to understand the nature of vulnerability in economies and markets. Systemic risk indicators are not too hard to construct across many countries and have proven their value and relevance in the past (view post here). They are not often keenly followed by investment managers because of their slow-moving nature and long lead times ahead of crises. Yet, through being informed about systemic vulnerability asset managers can judge whether a specific shock is likely to be transitory or escalatory after it has occurred. Two important criteria for escalation risk are vulnerability of the system and rarity of the type of shock that hit it.
  • Vulnerability here means that parts of the financial system have become reliant on favorable conditions. Typically protracted periods of low market volatility lead to build-ups of leverage and risk until they reach a tipping point (Minsky hypothesis, view post here). Long-term empirical analysis suggests that asset price booms are most dangerous when they are associated with rising financial leverage. Combinations of housing price bubbles and credit expansions have been the most detrimental of them all. (view post here). Also, economies that have grown accustomed to low real interest rates for long periods of time are subsequently susceptible to stress when rates or credit spreads are rising. Whether this stress is likely to escalate depends on whether the government or central bank have the means and will to intervene. (view post here).
  • Rarity means that the type of shock that has occurred had a low a-priori probability: it was not on the radar screen of either investors or policymakers. Theory suggests that senior decision makers rationally do not prepare for rare events as they can only process a limited quantity of information. Hence, expected losses from unpreparedness are inversely proportionate to an event’s rarity (view post here). Indeed, expected losses from unpreparedness are even higher if managers bear only limited liability.
• Adjusting portfolios early: There are two rational bases for adjusting positions to systemic risk: information advantage and market stress. The latter is far more common. A wide range of systemic risk indicators have been developed and majority relies on distress information provided by market prices, correlations, and volatility (view post here).
  In particular, volatility targeting is a valid default rule for reducing tail risk if one is uncertain as to the severity of a shock. This is because recent volatility is a good predictor of future volatility but not generally for future returns (view post here). Over long periods of time and based on U.S. equity data, volatility targeting strategies have produced significant increases in return per unit of tail risk (view post here).
  Effective volatility targeting and dynamic hedging can make use of options-implied volatilities, which not only help to predict future actual volatilities but also asset price correlations (view post here). High implied volatilities translate into high correlation whenever a single global factor is dominating price moves across all global markets.
• Running with the herd: Following market trends is often rational. And macro research can help to figure out when this is the case. For example, research may tell us that markets face a critical risk (e.g. a major bank may be at risk of default) but may not tell whether or not the risk will manifest (e.g. there may be a government bailout). In “make-or-break” situations it is rational to herd, i.e. to trade in the direction of prices, as private information disseminates in the market through prices (view post here). In this case, inaction would be an irrational and dangerous choice, even if we had no information advantage on the evolution of the crisis.
  Historically, simple trend-following strategies have reduced maximum drawdowns in equity portfolios and provided some hedging against losses in FX carry trades (view post here). Trend following removes psychological and institutional obstacles to exiting positions early in escalatory crises. Trend following is a market directional strategy that promises “convex beta” and “good diversification” for outright long and carry portfolios as it normally performs well in protracted good and bad times alike (view post here). Drawdown control strategies, such as volatility targeting and “MinMax” (forthcoming post), use elements of trend-following for risk management.
• Avoiding expensive liquidation: Market liquidity can evaporate in systemic crises. This can give rise to outsized price movements and, at the same time, make position adjustments prohibitively expensive. To the extent that liquidity problems rather than fundamental changes account for major market moves, position liquidation destroys investor value. Indeed, structural and regulatory changes in recent years seem to have made liquidity more precarious than in the past (view post here, here and here). Hence, calibrating or structuring positions such as to withstand liquidity events can be a major cost saver and performance factor. Researching the nature and potential of systemic risk is critical, both for preparation and for forming judgment whether or not it is really in the investor interest to liquidate positions in the thick of crises.

Investment managers can also benefit directly from systemic events to the extent that they have sufficient flexibility and risk limits to exploit price distortions and high risk premia paid. For systemic value based on price distortions see the related summary here. And for detecting and receiving high risk premia see section on “implicit subsides” here.

Calibrating tail risk

Standard risk management relies on past volatility of price changes, historical correlation, and assumptions regarding outliers of price changes beyond normal ranges. On this basis, the majority of portfolios of liquid financial instruments is managed based on some form of Value-at-Risk (VaR) model, a statistical estimate of a loss threshold that will only be exceeded with a low probability.

Unfortunately, past volatility is not always a helpful gauge for financial markets risk. Volatility is merely the magnitude of historic price fluctuations, while risk is the probability and scope of future permanent losses (view post here). The two are not equivalent and may even become opposites.  In particular, reliance on historic volatility can create an illusion of predictability that gives rise to carelessness in specific markets. Indeed, low volatility itself is often a cause of excessive leverage and crowded positioning and hence conducive to subsequent outsized market movements.

Therefore, it is helpful to go beyond conventional risk metrics when assessing and calibrating the risk of large outlier events (“tail risk”):

• Risk estimation should include expert assessment with subjective and non-quantifiable elements. For example, the risks and consequences of political upheavals, monetary policy regime breaks, or first-time sovereign defaults are not typically quantifiable through price history. A broad assessment of risk always requires a broad perspective, common sense, and an open mind.
• Portfolio risk estimates can explicitly consider extreme market regimes. For example, the basic idea behind extreme value theory is to fit an appropriate limiting distribution over returns that exceed a specific threshold. In particular, extreme value mixture models simultaneously estimate the threshold for extreme distributions and the extreme distribution itself (view post here). An alternative statistical approach is Bayesian risk forecasting, which accounts for the considerable distributional parameter uncertainty of Value-at-Risk and Expected shortfall estimates (forthcoming post).
• There are also quantitative warning signs of increased “tail risk” other than volatility. The simplest are valuation metrics for detecting bubbles (view post here), i.e. asset prices that are unusually high relative to the present value of estimated future cash flows. Academic papers have argued that equity markets with low dividend yields relative to local government bond yields are prone to large corrections (view post here). Similarly, countries with overvalued exchange rates and high short-term interest rates are prone to currency crises (view post here).
• Volatility-based risk management metrics can be adapted for “tail events” and “gap risk”. Historically, diversification and downside risk analyses have assumed normal (“Gaussian”) probability distributions. Those are convenient for calculation but give little weight to large outliers. By now this “normality assumption” has been widely refuted and better gauges of tail risk are available (view post here), such as conditional Value-at-Risk. The distribution assumption is crucial for setting risk management parameters realistically and for assessing the potential upside of long-volatility and short-risk strategies.

Exchanging market risk information

No single investor or institution has all pieces of the puzzle that is systemic risk. Investors specialize on markets or countries. However, every financial market depends on all other financial markets to some extent. At times particular market segments such as asset-backed securities or technology stocks can have a dominant global influence. Even small and remote markets, such as Iceland or Greece have triggered sizeable global market moves in the past. Connections between seemingly unconnected markets often reflect their communal dependence on global liquidity, which is the ease of financing transmitted by a small number of financial centers (view post here).

Therefore, investment managers must engage in active risk information exchange, trading their insights for the insights of colleagues. Indeed, theoretical and experimental research suggests that portfolio managers will generally share ideas and research if mutual feedback is valuable (view post here). This creates investor value at all times but particularly when systemic risk is rising, because investment managers that are part of an information network are better positioned to act early, as they know more and know better what others know. From a social welfare angle this process of information exchange is essential to disseminate concerns over systemic crises. The dissemination turn may serve to warn market participants, policymakers and the broader public, smoothing market volatility.