Endogenous risk arises from the interaction of financial market participants, as opposed to traded assets’ fundamental value. It often manifests as feedback loops after some exogenous shock. An important type of endogenous market risk is setback risk, which refers to the asymmetry of the upside and downside potential of a trade that arises from market positioning. Setback risk is a proclivity to incur outsized mark-to-market losses even if the fundamental value proposition of the trade remains perfectly valid. This makes it the natural counterweight to popular positioning. A useful two-factor model for detecting setback risk can be based on market positioning and exit risk. There are quantitative metrics for both. The highest setback risk is characterized by crowded positions that face an incoming type of shock that most investment managers had not considered.
The below is an updated summary.
What is endogenous market risk?
Endogenous market risk is the risk generated and reinforced within the financial system by the interaction of its participants. This is opposed to exogenous risk which refers to shocks that come from outside the financial system, such as changes in fundamental asset values or political risk. The term endogenous risk was coined by researchers at the London School of Economics (LSE) and this risk is the focus of the LSE’s Systemic Risk Centre.
A key propagation mechanism of endogenous market risk is feedback loops: one trader’s losses and liquidation often trigger another trader’s risk reduction and so forth. Risk management, balance sheet constraints and publicity all can act as amplification mechanisms. Importantly, the actively trading part of the market is not a zero-sum book but often jointly bets on a growing financial wealth of the world or takes positions that are implicitly subsidized by non-financial institutions. The habitual focus of most professional traders on flows and positions testifies to the critical importance of endogenous market risk for short-term price action.
What is setback risk?
Setback risk is a particularly important form of endogenous market risk. Technically speaking, setback risk is the difference between downside and upside risk to the mark-to-market of a contract due to investor positioning. Like all endogenous risk, it is in itself unrelated to the fundamental value of the underlying assets. If an investor believes that a security is fundamentally overvalued and may reprice in the future this is not endogenous market risk, but merely a disagreement with the prevailing market valuation.
Setback risk indicates the market’s latent tendency to revert to a state where positions are “cleaner”, meaning less crowded or reliant on leverage. Importantly, such risk arises merely through the “crowdedness” of trades and their risk management, irrespective of whether the fundamental value proposition is good or not. In fact, it is often the trades that offer the highest and most plausible long-term expected value that are subject to the greatest setback risk. This is consistent with a negative skew in the returns of popular risk premium strategies. For example, FX carry trade returns have historically displayed a proclivity to much larger negative than positive outliers (view post here), even when they remained profitable in the long run.
Setback risk in trading practice
Setback risk is the natural counterweight to popular positioning motives, such as implicit subsidies, fundamental trends or statistical trend-following. Its presence means that the popularity and crowdedness of trades should be justified by a sufficient risk premium. Therefore, systematic strategies that rely on popular factors can often be improved by complementary setback risk measures.
Setback risk also ties the prospects of popular presumed market-neutral strategies to the state of overall market risk prices. When risk-off shocks hit the dominant directional exposure of financial market participants their capacity for maintaining other positions also decreases. Hence all crowded and popular positions are exposed, even if they have no significant historical market beta. For example, there is empirical evidence that momentum strategies that buy winners and sell losers in terms of recent price trends have greater sensitivity to downside than to upside market risk across asset classes (view post here).
Generally, the presence of endogenous market risk has profound consequences on trading returns across many valid trading styles and systematic strategies. This risk is hard to avoid and skews the probability of future price moves against valid positioning motives, as long as these motives are common to a significant part of the market. Mechanical risk reduction rules and market liquidity constraints also suggest that the distribution of returns will have “fat tails”. Put simply, large adverse outliers relative to standard deviations should be expected in most value-generating trading strategies.
Information on endogenous market risk comes from a broad variety of sources, including positioning data, short-term correlation of PnL’s with hedge fund benchmarks, asymmetries of upside and downside market correlation, or simply past performance and the popularity of trades in broker research recommendation. Endogenous market risk of relative value and arbitrage trades often arises from outflows in the hedge fund industry. Hedge funds’ capital structure is vulnerable to market shocks because most of them offer high liquidity to loss-sensitive investors (view post here). Moreover, the build-up of endogenous market risk can be inferred from theory. For example, compressed interest rate term premia at the zero lower bound for policy rates are naturally quite vulnerable to any risk of future rates increases (view post here).
A two-factor model for detecting setback risk
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.
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 self-reinforcing 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, typically fuelled by marketing pitches for assets and trades, or from 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). 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 self-reinforcing endogenous market dynamics.
- One of the most toxic types of shock is a “black swan”, an event had been rated as highly unlikely, has an 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 self-reinforcing 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.
From a statistical angle, 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).