Home » Research Blog » Risk management shocks and price distortions

Risk management shocks and price distortions

Risk management relies on statistical metrics that converge on common standards. These metrics can change drastically alongside market conditions. A risk management shock is a large unanticipated market-wide change in statistical risk estimates. These shocks give rise to coerced or even distressed flows, typically subsequent to an initial large move in market prices. Risk management shocks and related flows can team up with other dynamics in the financial system to form feedback loops. Such reinforcing dynamics include dynamic hedging, market price-driven credit downgrades, popular fear of crisis, investment fund redemptions, and forced deleveraging. Feedback loops can trigger large and persistent price distortions and offer special trading opportunities.

The below is the third instalment of this site’s updated summary of key forces behind market price distortions, i.e. deviations of quoted prices from a level that would clear the market if all participants were trading for conventional risk-return optimization. Previous posts explained price distortions that arise from liquidity conditions and rebalancing rules.

Risk management shocks

The risk management rules of most institutional investors follow commonly accepted standards. Alas, similar rules often coerce similar flows.  And one-sided flows in markets with limited liquidity can push prices far from fundamental values. In this way, conventional risk management rules can be a cause of distortions and even set in motion self-reinforcing feedback loops.

Prominent risk metrics are value-at-risk (VaR), a statistical measure of expected maximum loss at a specific horizon within a specific range of probability, and expected shortfall, a measure of expected drawdown in a distress case. These statistical assessments of risk rely on historical variances and covariances, and can be subject to sudden major revisions.

  • The calculation of risk metrics depends on the lookback window, i.e. the history of the price return experiences used for its calculation and the weighting of recent versus distant observations. Lookback windows that rely on multi-year experience adapt poorly to a changing risk environment. Therefore, many risk metrics are short, with a half-time of lookbacks of no more than 11 days. This makes them susceptible to drastic reassessments based on market volatility alone. Such “statistical” reassessment would occur without any consideration of the underlying causes of changes in volatility.
  • Even with many years of data history, risk estimates are still vulnerable to event shocks. Small variations in assumptions can cause large changes in forecasts. Some research claims that it would take half a century of daily price data for VaR and expected shortfall models to reach their theoretical asymptotic properties. Intuitively, even long historical samples have only limited data on actual crises and hence are subject to revision with each new crisis experiences (view post here).
  • Risk models are prone to compounding uncertainty when they matter most: in financial crises. Research shows that different types of statistical risk models tend to diverge during market turmoil and hence become themselves a source of fears and confusion (view post here). Acceptable performance and convergence of risk models in normal times can lull the financial system into a false sense of reliability

Reliance on statistical metrics can give rise to so-called ‘VaR shocks’: If estimated risk metrics surge, VaR-sensitive institutions recalibrate the risk of their existing positions and subsequently reduce their positions (view post here). For example, if an institution has a fixed “statistical” risk budget a doubling of the estimated value-at-risk or expected shortfall requires it to liquidate half of its nominal positions. Importantly, this type of selling pressure typically arises after the initial price decline.

Analogously, many trading desks or asset management companies set “drawdown limits” for their managers. These are loss thresholds for a portfolio’s net asset value beyond which traders must liquidate part of all of their positions. Managers are typically under obligation to cut risk regardless of asset value and return prospects. Hence, once the common drawdown limits are broken additional flows ensue in the same direction of the original loss, accentuating price movements for no fundamental reason.

Feedback loops

Initial shocks to risk metrics and related flows can team up with other forces to form feedback loops:

  • Dynamic hedging: Many institutions run explicit or implicit “short volatility” positions. Indeed, such short-volatility strategies seem to have expanded strongly in the wake of declining fixed-income yields. They pay steady positive risk premia in normal times, just like a fixed income asset, but at the peril occasional outsized losses. Dynamic hedging refers to sales and purchases of underlying assets in order to contain the risk related to volatility. This gives rise to feedback loops in two ways.
    • From a macro perspective, there is reinforcement between volatility and the scale of short-volatility strategies (view post here). In particular, there is a plausible feedback loop between low interest rates, debt expansion, (low) asset volatility, and financial engineering that allocates risk based on that volatility.
    • From a micro or trading perspective, dynamic hedging is common practice for option books but is applied widely in other markets, including credit, rates and leveraged risk parity. For example, U.S. financial institutions have historically been “short volatility” with respect to long-term interest rates because of homeowners’ option to repay mortgages early (view post here). In times of declining yields delta and probability of execution of this implied option is increasing, forcing institutions to hedge by further extending duration exposure. The probability of severe “convexity events” has been reduced since the Federal Reserve has bought a sizable share of mortgage-backed securities from the market (view post here), but not eliminated.
  • Credit risk: Risk management can also form feedback loops with credit risk, particularly country risk and counterparty risk.  A good illustration for this is the Credit Default Swaps (CDS) market. CDS are assumed to represent a measure of default risk. In practice, this (less liquid) market can gap in large moves, simply as a consequence of one-sided institutional order flows, which themselves could be motivated by risk management or regulatory considerations (view post here). As CDS spreads themselves are used as a measure of credit risk, institutional flows and spreads can reinforce each other to form escalatory dynamics.
  • Public fear: Financial market turbulences typically focus popular attention on crisis risk. Bouts of fear of extreme events, such as economic depressions or war, are more frequent than the actual occurrence of disasters (view post here). In normal or good times, people tend to pay little attention to extremes. As economic or political conditions deteriorate, people begin to contemplate the possibility and consequences of disasters. Such enhanced awareness plausibly changes subjective expectations and price of risk. This is called “salience theory”(view post here). If public fear of crisis is rising, financial risk managers experience pressure from investors, shareholders and even governments to position more defensively.
  • Redemptions: Significant declines in the net asset values of investment vehicles usually give rise to redemptions, often from investors that cannot afford or bear watching wealth dwindling beyond certain thresholds. This is supported theoretically and empirically for equity, bond and credit markets (view post here). In many cases, funds provide daily liquidity and costs of redemptions are effectively borne by investors that do not redeem or redeem late. This creates incentives for fire sales and causes price distortions (view post here). Indeed, the pro-cyclicality of redemptions is consistent with survey evidence of pro-cyclicality of equity return expectations of investors (view post here).
  • Forced deleveraging: Risk-reduction in banks and other financial intermediaries does not only constrain their own asset holdings but, indirectly, those of other market participants, particularly leveraged investors such as hedge funds. This creates both relative price distortions and high directional risk premia. Most obviously, limitations of arbitrage capital give rise to price differentials between contracts with similar risk profiles (view post here). Also, empirical analyses have found that the leverage provided by Broker-Dealers, i.e. their funding of others, is an important explanatory variable for the risk premium paid on equity and credit exposure (view post here). When credit supply is ample, risk premia and future excess returns are low. When credit supply is scarce, risk premia and future excess returns are high.

Related articles