Endogenous market risk

Understanding endogenous market risk (“setback risk”) is critical for timing and risk management of strategic macro trades. Endogenous market risk here means a gap between downside and upside risk to the mark-to-market value that is unrelated to a trade’s fundamental value proposition. Rather this specific “downside skew” arises from the market’s internal dynamics and indicates the need to return to “cleaner” positioning. Endogenous market risk consists of two components: positioning and exit probability. Positioning refers to the “crowdedness” of a trade and indicates the potential size of a setback. Exit probability refers to the likelihood of a setback and can be assessed based on complacency measures and shock effect indicators.

Basic points

What is endogenous market risk?

We define endogenous market risk as the difference between downside and upside risk to the mark-to-market performance of a position that arises from investor positioning and is unrelated to the fundamental value of the underlying assets. Thus this risk is due mainly to market dynamics, rather than exogenous shocks, such as changes in “fundamentals”. It can also be called “setback risk” because it indicates that the market may need to revert to a state where positions are “cleaner”, meaning less crowded and reliant on leverage. This risk arises even if the value proposition of the trade remains perfectly valid. In fact, it is often the trades that offer the clearest and most plausible long-term expected value that incur the greatest endogenous market risk. This is consistent with negative skews in the returns of many risk premium strategies. For example, FX carry trade returns have historically displayed larger negative than positive outliers (view post here).

Endogenous market risk is the natural counterweight to many dominant positioning motives, such as implicit subsidies, fundamental trends or even simple momentum trading. For example, for momentum strategies there is empirical evidence of greater sensitivity to downside than to upside market risk across asset classes (view post here). The presence of endogenous market risk has profound consequences. The probability for future price moves is skewed against dominant positioning motives and has “fat tails”. Large adverse outliers relative to standard deviations should be expected.

Information on endogenous market risk can come from various 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, which in conjunction with interactions and short-term performance targets can lead to panic runs (forthcoming post). Also, some forms of endogenous market risk can be detected through theoretical models: 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).

Information efficiency on endogenous market risk creates social value by reducing the risk of dislocation and crisis. It creates investor value by reducing the occurrence of large outsized drawdowns and forced expensive position liquidations.

“Endogenous risk refers to changes in asset prices attributable not to changes in fundamentals, but rather to portfolio adjustments in response to constraints…The maximal level of endogenous risk has very low sensitivity to fundamental risk and it may be slightly increasing as fundamental risk goes down.

Brunnermeier and Sannikov, 2014

The two components of endogenous market risk

Endogenous market risk can be decomposed into two factors: positioning and exit risk. Positioning refers to the “crowdedness” of a trade. Exit risk refers to the probability of meaningful reductions in position, i.e. that the crowd will run for the exit. Endogenous market 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 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, the recent historic price sensitivity of a position to popular benchmark and hedge fund performance (view post here), or recurrent underperformance of positions despite a positive news flow. Also, past performance of popular risk premium strategies is often good indirect indicators of 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 gives 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). Forward-looking indicators that influence the likelihood of such events include an estimation of market “complacency” and the tracking of concurrent shocks.

  • Complacency here means lack of resilience to adverse shocks. Positions are based on optimistic expectations (like planning a picnic in England one week ahead). Often this lack of resilience arises from implausibly low-risk perceptions, which 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.
  • Shock effect indicators represent assessments of whether recent events might trigger escalatory price dynamics. Such assessment requires identifying type and strength of shock.
    • A particularly dangerous type of shock is a deterioration in the liquidity and capital structure of financial intermediaries. This type of shock diminishes the capacity of dealers in certian markets – such as foreign exchange – to warehouse the net risk position of other market participants (view post here). The result can be a firesale that puts particular pressure on risk positions that offer best 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.
    • 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 [1] financial returns have a proclivity to extreme events and [2] 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 to a negative volatility-return relation. (view post here).

“In an unfolding crisis, most market participants respond by liquidating their most liquid investments first to reduce exposures and reduce leverage.”

Myron Scholes, 2000