FX trades after volatility shocks

Currency areas with negative external balances are – all other things equal – more vulnerable to financing shocks. Jumps in market price volatility often indicate such shocks. Realistically it takes a few days for the market to fully price the consequences of shocks consistently across currencies. Hence, the products of external balances-based “resilience scores” and volatility shocks are plausible indicators of “post-shock currency hazards”. This means that they should serve as signals for differences in currency returns after market volatility has surged or dropped. An empirical analysis based on 28 currencies since 2000 shows that a most simple “post-shock currency hazard” measure has significantly helped predict subsequent short-term returns and would have added positive PnL to FX trading strategies, particularly in times of turbulence.

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Classifying market regimes

Market regimes are clusters of persistent market conditions. They affect the relevance of investment factors and the success of trading strategies. The practical challenge is to detect market regime changes quickly and to backtest methods that may do the job. Machine learning offers a range of approaches to that end. Recent proposals include [1] supervised ensemble learning with random forests, which relate the market state to values of regime-relevant time series, [2] unsupervised learning with Gaussian mixture models, which fit various distinct Gaussian distributions to capture states of the data, [3] unsupervised learning with hidden Markov models, which relate observable market data, such as volatility, to latent state vectors, and [4] unsupervised learning with Wasserstein k-means clustering, which classifies market regimes based on the distance of observed points in a metric space.

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Estimating the positioning of trend followers

There is a simple method of approximating trend follower positioning in real-time and without lag. It is based on normalized returns in liquid futures markets over plausible lookback windows, under consideration of a leverage constraint, and uses estimated assets under management as a scale factor. For optimization and out-of-sample analysis, the approach can be enhanced by sequential estimation of some key parameters, such as the momentum lookback, the normalized momentum cap and the lookback for realized volatility calculation. Trend follower positions are an important factor of endogenous market risk due to the size of assets under management in dedicated funds and the informal use of trend rules across many trading accounts.

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External imbalances and FX returns

Hedge ratios of international investment positions have increased over past decades, spurred by regulation and expanding derivative markets. This has given rise to predictable movements in spot and forward exchange rates. First, on balance hedgers are long currencies with positive net international investment positions and short those with negative international investment positions. With intermediaries requiring some profit for balance sheet usage these trades command negative premia and widen cross-currency bases. Second, hedge ratios increase in times of rising FX volatility. An increase in the hedge ratio for a currency puts downward pressure on its market price in proportion to its external imbalance and bodes for higher medium-term returns. Also, the dispersion of cross-currency bases increases in times of turmoil.

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Tracking investor expectations with ETF data

Retail investors’ return expectations affect market momentum and risk premia. The rise of ETFs with varying and inverse leverage offers an opportunity to estimate the distribution of such expectations based on actual transactions. A new paper shows how to do this through ETFs that track the S&P 500. The resulting estimates are correlated with investor sentiment surveys but more informative. An important empirical finding is that expectations are extrapolating past price actions. After a negative return shock, investor beliefs become more pessimistic on average, more dispersed, and more negatively skewed.

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Endogenous market risk: updated primer

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.

Clues

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 riskPositioning 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

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

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 [1] the complacency of the market with respect to an adverse shock and [2] 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 [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 a negative volatility-return relation. (view post here).

Crowded trades: measure and effect

One measure of the crowdedness of trades in a portfolio is centrality. Centrality is a concept of network analysis that measures how similar one institution’s portfolio is to its peers by assessing its importance as a network node. Empirical analysis suggests that [1] the centrality of individual portfolios is negatively related to future returns, [2] mutual fund holdings become more similar when volatility is high, and [3] the centrality of portfolios seems to reflect lack of information advantage. This evidence cautions against exposure to crowded trades that rely upon others’ information leadership or are motivated by widely publicized persuasive views.

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A theory of hedge fund runs

Hedge funds’ capital structure is vulnerable to market shocks because most of them offer high liquidity to loss-sensitive investors. Moreover, hedge fund managers form expectations about each other based on market prices and investor flows. When industry-wide position liquidations become a distinct risk they will want to exit early, in order to mitigate losses. Under these conditions, market runs arise from fear of runs, not necessarily because of fundamental risk shocks. This is a major source of “endogenous market risk” to popular investment strategies and subsequent price distortions in financial markets, leading to both setbacks and opportunities in arbitrage and relative value trading.

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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.

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The dangerous disregard for fat tails in quantitative finance

The statistical term ‘fat tails’ refers to probability distributions with relatively high probability of extreme outcomes. Fat tails also imply strong influence of extreme observations on expected future risk. Alas, they are a plausible and common feature of financial markets. A summary article by Nassim Taleb reminds practitioners that fat tails typically invalidate methods and conventions applied in quantitative finance. Standard in-sample estimates of means, variance and typical outliers of financial returns are erroneous, as are estimates of relations based on linear regression. The inconsistency between the evidence of fat tails and the ongoing dominant usage of conventional statistics in markets is plausibly a major source of inefficiency and trading opportunities.

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