It is useful to decompose setback risk into two factors: positioning and exit risk. Positioning refers to the “crowdedness” of a trade. Exit risk refers to the probability of liquidation, i.e. that the crowd will run for the exit. Setback risk is high when a trade is “crowded” and near-term position reductions are probable. While the positioning component always relates to a specific contract, exit risk can be a global factor, such as tightening dollar funding conditions.
Positioning relative to market liquidity principally indicates the potential size of the PnL setback. For some contracts exchanges or custodian banks provide outright positioning data. However, these are not always easy to interpret. In practice, macro traders pay much heed to informal warning signs, such as anecdotal evidence of positioning provided by their brokers, surveys among investment managers, return correlation with market benchmarks (view post here) and lack of position performance in spite of positive news. Also, medium-term historic performance of popular risk premium strategies is often good indirect indicators of their popularity and, hence, positioning. For some popular algorithmic strategies, such as trend following, positioning can be estimated based on the replicated stylized position signals and the size of assets managed under this type of strategy (view post here).
Conceptually, the crowdedness of trades in a portfolio can be measured by “centrality”, a concept of network analysis that measures how similar one institution’s portfolio is to its peers (view post here). Empirical evidence suggests that the centrality of portfolios is negatively related to future returns.
Exit risk principally indicates the probability of a near-term setback, be it small or large. The most prominent triggers of large-scale unwinding of macro trades are volatility or Value-at-Risk jumps (view post here) and liquidity and funding pressure (view post here and here). The term trigger here refers to an endogenous market shock that is likely to lead to subsequent escalatory price dynamics. Catching such triggers requires estimation of  the complacency of the market with respect to an adverse shock and  the gravity of specific adverse shocks.
Complacency here means lack of resilience to adverse shocks. This lack of resilience arises from an optimistic mode of expectations, maybe fuelled by positive publicity for assets and trades, or by implausibly low-risk perceptions that are likely to be revised upward during the lifetime of the trade, even if the risk itself does not manifest. Risk perceptions can be measured in a wide range of news-based, survey-based and asset price-based indicators (view post here). Direct measures of complacency include variance risk premia (view post here) and the term structure of option-implied equity volatility (view post here). Asset return expectations of retail investors can be estimated based on demand for various types of leveraged or inverse ETFs (view post here). Another plausible indication for complacency is the homogeneity of economist forecasts. Empirical analyses point to an important principle: when economists are clustered tightly around a consensus, actual data surprises tend to have stronger market impact (view post here). Generalizing this point, it seems plausible that a strong analyst consensus that supports a macro position makes this position more vulnerable to data surprises.
Gravity of shock refers to the probability that a shock is rated as significant and consequential by market participants. This depends upon type and strength of shock. Note that the shock itself can be exogenous (come from outside the market) but is evaluated due to its potential for unleashing escalatory endogenous market dynamics.
- One of most toxic types of shock is a “black swan”, an event had been rated as highly unlikely, has extreme impact and is incorrectly rationalized even after it occurred. Put simply, the less probable a negative shock, the harder its impact. The worst market crises are the ones that investment managers have never prepared for (view post here).
- Another particularly dangerous type of shock is a decline of liquidity or capital ratios of financial intermediaries. This type of shock diminishes the capacity of dealers to warehouse the net risk position of other market participants (view post here). The result can be forced liquidations that put particular pressure on risk positions that offer high expected long-term value or that are popular for other reasons.
- A more frequent shock with escalatory potential is a surge in people’s fear of disaster. Theoretical research shows that a re-assessment of beliefs towards higher disaster risk triggers all sorts of uncertainty shocks, for example with respect to macro variable, company-specific performances and other people’s beliefs (view post here). This can derail both directional and relative value trades.
Nowadays there is a broad range of measures tracking market risk and uncertainty (view post here). Risk refers to the probability distribution of future returns. Uncertainty is a broader concept that encompasses ambiguity about the parameters of this probability distribution. Changes in risk and uncertainty measures indicate the gravity of shocks.
From a statistical angle, escalatory shock detection often focuses on ”volatility surprises” (market price changes outside the range of expected variation) that make investors revise drastically the probabilities for various risks. Volatility shocks typically draw attention to previously underestimated risks and transmit easily across markets and asset classes (view post here). Moreover, volatility shocks are critical in a statistical sense because financial returns plausibly have “fat tails”. This means that  financial returns have a proclivity to extreme events and  the occurrence of extreme events changes our expectations for uncertainty and risk in the future significantly (view post here). Such a reassessment may take days or weeks to complete and give rise to negative trends.
It is important to discriminate between the medium-term volatility trends and short-term volatility spikes. Longer-term changes of volatility mostly reflect risk premiums and hence establish a positive relation to returns. Short-term swings in volatility often indicate news effects and shocks to leverage, causing a negative volatility-return relation. (view post here).