Price distortions

Price distortions are apparent price-value gaps. Trading strategies that are based on such distortions rely less on information advantage than on consistent price monitoring, flexibility of trading, privileged market access, superior financial product knowledge and –  most of all – rational discipline in turbulent times. Price distortions arise from inefficient flows and prevail as long as a sizable share of market participants is either unwilling or unable to respond to obvious dislocations. There are many causes of such inefficiencies, including risk management rules, liquidity disruptions, mechanical rebalancing rules and government interventions.

The basics

What are price distortions?

In the present context price distortions are defined as deviations of quoted prices from a level that would clear the market if all participants were trading for conventional risk-return optimization. In principle, all flows distort transaction prices relative to contract value (view post here). However, mostly the effects are small. Price distortion here means significant gaps between mark-to-market prices and a plausible range of economic values of a contract.

Like information inefficiency, price distortions lead to a mispricing of financial contracts relative to their fundamental value. Unlike information inefficiency, this mispricing is not based on ignorance, but on ”inefficient flows”. These are transactions in financial markets that are motivated by objectives other than return optimization. In practice, one can observe many market flows and transactions that obstruct the alignment of price and value. Common causes or triggers for such “inefficient flows” include:

  • formal and rigid risk management rules that apply to many institutions,
  • liquidity shocks, i.e. a sudden deterioration of the tradability of assets or the risk thereof,
  • mechanical allocation rules, for example of exchange-traded funds, indexed fund and related structured products, and
  • government intervention and regulation.

Detecting price distortions

Unlike information-based trading, price distortion-based strategies do not require information advantage in respect to the traded contract. They do not focus on in-depth research of its expected value. Instead, these strategies ascertain some apparent price-value gap and market inefficiency. They subsequently use advantages in market access or in pricing know-how to extract value. Sometimes, trading speed (view post here) and financial leverage can be of the essence.

Detecting inefficient flows and related distortions is not trivial. Most of what is commonly called “market noise” is actually rational trading disguised by complexity (view post here). However,  price distortions frequently do arise pursuant to major information or price shocks that create a state of confusion or even panic. Moreover, trading in times of turmoil often bears high transaction cost, which deters market participants from immediately taking advantage of price-value gaps. In order to detect price distortions systematically one can take three different angles:

  • The first is to understand and identify the causes of distortions, such as institutional risk management constraints, market liquidity problems and so forth, which are explained in the sections below. If a market is being heavily influenced by any of these causes it is more probable that prices will be regularly distorted and that there will be payback subsequently. For example, empirical evidence suggests that a wide range of equity return anomalies is related to market inefficiencies (view post here). Malfunctioning markets can be diagnosed in real-time with the help of “market distress indices” that include issuance volumes and issuer characteristics in the primary market and trading volumes and liquidity in the secondary market (view post here).
  • The second angle is metrics of misalignment between prices and fundamental value, such as in financial bubbles (view post here). Diagnosing price distortions this way is not the same as estimating price-value gaps, as the latter would require superior information efficiency. Price distortions can be detected by conventional valuation metrics but with a focus on extreme price value gaps that are associated with obstacles to arbitrage or trading. An example would be gaps between credit spreads of individual bonds and a plausible grid of credit spread curves that is estimated based on a range of maturitie s and ratings (forthcoming post).
  • The third approach is to investigate the time-series pattern of asset prices. For example, higher-than-exponential asset price growth with apparent feedback loops is often an indication of an unsustainable asset price bubble (view post here). Also, temporary mild explosiveness in asset prices or exchange rates in conjunction with relative stability in underlying fundamentals is usually indicative of short-term distortions (view post here). Generally, a self-reinforcing price dynamics that is not a reflection or cause of underlying value changes is prone to producing price distortions. Technically, price distortion time series are characterized by “strict local martingales”, i.e. episodes when the risk-neutral return temporarily follows a random walk while medium-term return expectations decline with the forward horizon length. Such strict local martigales can be identified by modeling return volatility with the help of a recurrent neural network (view post here).

Price distortions prevail because most investors are either unable or unwilling to exploit them. This is very realistic.

  • The vast majority of investors are unable to exploit relative price distortions because their access to arbitrage capital and leverage is restricted. These restrictions can hamper even sophisticated investors, particularly in times of financial turmoil. They are the very cause of persistent relative value opportunities, particularly in the fixed income space (view post here).
  • Meanwhile, many investment strategies explicitly disregard price distortions, and their flows may for some time overpower more subtle relative value flows. Simple trend following has been a common and successful algorithmic investment strategy (view post here) that deliberately blanks out the fundamental value of a contract altogether. Similarly, many discretionary traders follow “momentum strategies” based on perceived dominant information flows (trading on the news). Moreover, herding is a well documented behavioural pattern in the investment industry (view post here) that can be efficient from the individual portfolio manager’s perspective, because it saves research costs (view post here). Herding can, however, lead to price distortions, particularly if it is motivated by non-fundamental shocks in markets with limited liquidity and a homogeneous investor community, such as in corporate credit markets (view post here). Moreover, there is also reason and evidence of so-called “beta herding”, which means convergence of market betas of individual assets that arises from investors’ biased perceptions, such as overconfidence in predicting directional market moves (view post here). If assets have the ‘wrong’ beta subsequent market moves lead to price bias relative to underlying value and trading opportunities.

Price distortions and risk management 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 perspectivedynamic 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 deleveragingRisk-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.

Price distortions and market liquidity

The various price effects of liquidity

Market liquidity here refers to the cost of buying and selling a security or derivative. It measures the efficiency of trading. Separately, market liquidity risk refers to the probability that trading costs surge when the need for trading becomes more urgent (view post here). Both liquidity and liquidity risk influence prices.

  • First, most institutional and private investors are willing to pay a premium on securities with high and reliable liquidity and require a discount on securities with low and uncertain liquidity (view post here). Illiquidity translates into higher transaction cost for a given trading pattern and eats into returns. The illiquidity risk premium is an excess return paid to investors for tying up capital. The premium compensates for the loss of flexibility to contain mark-to-market losses and to adjust positions to a changing environment.
  • Second, changes in liquidity or liquidity risk of a contract lead to a change in its price, irrespective of its expected discounted present value. For example, a rise in market illiquidity, which means a greater cost of trading, makes forward-looking investors require higher future yields on a security. Thus, uncertain and unstable liquidity conditions lend themselves to price distortions. Small shocks can produce large price moves and apparent dislocations.

Poor liquidity can also cause rational price distortions when market participants keenly observe each other’s positions and trading activity (view post here). For example, in OTC (over-the-counter or bilateral) markets lack of liquidity means that dealers do not much “buffer” flows and institutional investors effectively transact with each other. In this case, investors take each other’s bids and offers as signals and plausibly operate under the laws of game theory. In particular, when investors observe each other’s selling pressure they can rationally transact at prices below true value and give rise to so-called run equilibria, self-reinforcing price dynamics away from fundamental value.

There is evidence that liquidity as a price factor and source of price distortions has increased since the 2000s:

  • Regulatory tightening after the great financial crisis has reportedly discouraged risk warehousing of banks, which would make global liquidity more precarious (view posts here and here). For example, in the U.S. the Volcker Rule has banned proprietary trading of banks with access to official backstops.  Market making has become more onerous as restrictions and ambiguities of the rule make it harder for dealers to manage inventory and to absorb large volumes of client orders in times of distress (view post here).
  • By contrast, the role of institutional asset managers as liquidity providers has increased (view post here). Investment funds often buy and sell with the market, chasing return trends (view post here), due to redemptions and reliance on collateralized funding. Also, asset managers often engage in cash hoarding, which means that they sell more underlying assets in market downturns than is necessary to meet redemptions (view post here). This holds true particularly in markets with more precarious liquidity. On the whole, investment funds seem to make liquidity more pro-cyclical and may aggravate market price swings, thereby giving rise to upside price distortions in bull markets and downside price distortions in downturns.

Past experiences

In the 2010s there have been many examples of price distortions and trading opportunities that were shaped by liquidity conditions.

  • In developed foreign exchange markets liquidity shocks have been highly cross-correlated. In systemic crises FX liquidity shocks have formed negative feedback loops with funding constraints and volatility, leading to escalatory dynamics and fire sales (view post here). Even regular episodes of tightening dollar funding conditions have triggered one-sided flows in FX swap markets. FX swap markets serve as a conduit for secured dollar funding. Large one-sided flows can lead to a breakdown in the conventional non-arbitrage condition of the “covered interest parity”, leading to arbitrage or enhanced trading opportunities (view post here). Such opportunities can be measured by the “cross-currency basis” and have become common since the great financial crisis (view post here). Indeed, a new theory of risk-adjusted covered interest parity suggests that FX swap rates, i.e. the difference between FX spot and forward prices, deviate from risk-free interest rate differentials in accordance with the relative liquidity risk premia for the relevant currency areas (view post here).
  • The 2013 sell-off in the U.S. treasury market (“taper tantrum”) illustrated that dealers or intermediaries may reduce their own inventories and market making after risk shocks, thereby aggravating rather than buffering liquidity shocks (view post here). More generally, empirical research has shown that sudden large drawdowns in government bond markets are aggravated by poor liquidity (view post here). This tendency could increase over time, as a consequence of increased capital charges on market making, extended transparency rules for dealers, elevated assets under management in bond funds and the liquidity transformation functions of these bond funds (view post here).
  • Emerging markets appear to be particularly vulnerable to liquidity conditions Assets under management of dedicated EM funds have increased markedly since the 1990s. Trading flows have been highly correlated due to the usage of benchmarks, and EM asset prices and final investor flows tend to be pro-cyclical and mutually reinforcing (view post here). The discretionary decisions of fund managers seem to aggravate this pro-cyclicality: they usually increase cash holdings in times of market turmoil due to increased risk of future client redemptions(view post here). Local-currency emerging debt markets, in particular, have become more vulnerable to global liquidity and other market shocks, with foreign ownership being a key determinant of that vulnerability (view post here). As a consequence, global shocks can trigger sizable relative price distortions between markets and currencies that feature high foreign participation and those that are more isolated.

Price distortions and rebalancing rules

What is rebalancing?

Rebalancing is the process of realigning the weights in a portfolio with the designed purpose of the investment vehicle or strategy. Rebalancing seeks to limit exposure to unwanted risk, regardless of whether that risk pays a high premium or not. The main rebalancing processes simply prescribe periodically buying or selling assets to maintain an original or desired allocation.

Rebalancing is a source of inefficient flows. Importantly, mechanical rebalancing rules are in fact active algorithmic strategies, even if they do not explicitly seek return optimization. For example, simple periodic reallocation to fixed asset weights means that winning assets are systematically sold and losers are bought. In markets with trends and relative price momentum this creates losses and slows trends at the market level.

It is important to understand the motivation behind specific rebalancing flows in order to detect potential price distortions. Academic research shows that the effect of rebalancing cascades on the net demand for individual assets will look like noise, even if the flows are fully rational (view post here). Predictions of aggregate flows become infeasible because of alternating buy and sell orders, feedback loops and threshold-based execution rules. This cautions against just dismissing seemingly non-fundamental market flows as irrational and betting against them.


The most common motive of rules-based rebalancing is benchmarking, i.e the use of pre-set standards for allocation, risk, and return. Many investment managers are formally or informally benchmarked against some market index and prefer to contain deviations of their returns from those of the benchmark index.

“Underweights” in volatile but outperforming assets are the main risk of violating these margins, because such assets simultaneously outperform and gain weight in the benchmark. Hence investment managers often find themselves compelled to buy overpriced and risky assets merely to contain streaks of underperformance (view post here). Profit maximizing traders can exploit the market’s proclivity to overvalue high-beta and high-volatility assets on these occasions. Empirical research has provided evidence for a “low risk effect” in financial markets, i.e. the recurrent outperformance of low-risk versus high-risk assets, once both are scaled by volatility (view post here).

Benchmark effects

Benchmarking effects should not be confused with benchmark effects. Benchmark effects arise from changes in global securities indices that are commonly tracked by investment managers. In particular, the surge in passive investment means that a large share of institutional investors is under obligation to buy and sell in accordance with the constituents and weights used by benchmark indices, regardless of assets’ fundamental values (view post here).

Benchmark companies revise indices regularly, causing re-weighting of sectors or countries that is not in proportion to market capitalization. Sovereign credit rating changes, for example, can establish or remove the eligibility of a country’s securities for inclusion in benchmark indices. There is empirical evidence that these changes induce sizeable portfolio re-allocations and international capital flows, entailing an outperformance of ‘upgraded’ assets at the time of announcement and the time of actual index adjustment (view post here). Upgrading here does not mean necessarily better asset quality, but rather the assets’ greater access to index-tracking capital allocations.

Regulatory effects

Regulatory changes can necessitate the strategic rebalancing of large segments of the market. This motive is particularly important for tightly regulated institutions such as insurance companies and pension funds (view post here). Thus, the EU reform of the regulation and supervision of insurance and reinsurance undertakings in 2016 introduced bias against assets with high market and liquidity risk, such as equity, and in favor of low-yielding sovereign bonds (view post here). Also, regulatory changes seem to be one of the key motivations behind herding in the pension industry. Greater complexity and policymaker discretion in the wake of the great regulatory reform of the 2010s means that investment managers must pay more attention to regulatory policies, not unlike the way they have monitored monetary policies (view post here). Since regulatory allocation changes are unrelated to risk-return optimization resultant flows are likely to be inefficient and conducive to price distortions.


Exchange-traded funds are hybrid investment vehicles that are continuously traded in a liquid market. The goal of a traditional ETF is to match the returns of its associated index or market sector. ETFs have been a major part of the passive investment boom since the 2000s, expanding in size, diversity, scope, and complexity (view post here).

All ETFs rebalance periodically. A regular passive ETF weights its holdings in a fashion that is similar to the underlying index but might rebalance only on an annual or semi-annual basis. Such rebalancing may lead to just subtle market price distortions. Rebalancing flows can become a stronger force when the arbitrage mechanism between ETFs and their constituent securities runs into troubles. ETF prices can deviate significantly from those of the constituent securities, especially at high frequencies, for illiquid assets and during periods of financial stress. Empirically, ETFs have been associated with greater co-movement of asset prices: stocks tend to co-move more with their respective indices once they are included in ETF portfolios. There is also evidence that ETFs are associated with increased price volatility of the constituent securities (paper here).

Rebalancing flows of equity ETFs that are leveraged can be particularly conducive to price distortions. The goal of leveraged ETFs is to realize returns that may be double or triple those of the underlying index or market sector. Leveraged ETFs use borrowed money to increase returns.  Leveraged ETFs are subject to automatic rebalancing rules, requiring them to buy when prices rise and sell when they fall. As leveraged ETFs have become a significant factor in U.S. equity markets they can reinforce or even escalate large directional moves in the stock market, both through their own transactions and other market participants’ front running (view post here).

Equity parity

More subtle rebalancing flows arise from the so-called “uncovered equity parity“. This parity suggests that when foreign equity holdings outperform domestic U.S. holdings, USD-based investors are exposed to elevated exchange rate and country risk in form of higher USD notional in the foreign currency area. There should be a tendency to reduce or hedge the exposure subsequently. There is indeed empirical evidence for investors selling winning equity markets in 1990-2010 (view post here). However, other analyses suggest that this effect may not be dominant (paper here) overtime. Plausibly, equity parity flows introduce subtle short-lived distortions around rebalancing dates.

Constant Proportion Portfolio Insurance

Constant Proportion Portfolio Insurance or CPPI products are capital protection products. Rather than using options they deploy a dynamic asset allocation strategy. Put simply, a CPPI strategy allocates between a riskless asset and a risky asset, such as equity, hedge funds, or commodity indices. The manager defines the “cushion” or the percentage of the fund’s assets that may be put at risk, which is estimated based on the difference between the initial value of the product and the present value minimum necessary to provide the capital guarantee at maturity.

In rising markets, a CPPI strategy allocates more towards the risky asset. In falling market, it allocates more towards the safe asset. Since CPPI flows do not consider any aspects of assets’ fundamental value they are an example of inefficient flows that reinforce market trends.

High-frequency trading

High-frequency traders do not really rebalance, but also follow strict rules-based position management. High-frequency trading became a large-scale business during the 2000s and is managed mostly by independent proprietary traders. It executes large numbers of trades in less than one millisecond, powered by trading algorithms and based on fast-moving market data. The share of high-frequency trading across various markets has been estimated between 10% and 50%. Most high-frequency strategies seek to exploit tiny arbitrage opportunities in large volumes. For example in “slow-market arbitrage”, the high-frequency trader detects price moves on one exchange and picks off orders sitting on another before it can react.

High-frequency trading strategies respond at high speed to changes in prices by using relatively simple strategies. Speed, rather than reflection, is of the essence. In particular, high-frequency trading algorithms have no fundamental anchor and simply move too fast for humans to intervene with judgment. For example, when stocks drop, even if due only to a “fat finger”, the programs may decide to stop trading, withdrawing liquidity from the market, or even aggravate the sell-off.

While high-frequency trading can provide liquidity and efficiency on many occasions it can magnify volatility on others. In particular, it can make markets more prone to vanishing liquidity. Increases in trading speed, in conjunction with market concentration, and regulatory costs of market making, augment the probability of liquidity events (view post here). The May 2010 “flash crash” in the U.S. stock market exemplified that risk. Moreover, there is a non-zero probability of outright “glitches” that can escalate modest price changes toward systemically destabilizing events (view post here). Modern physics teaches that objects behave differently as they reach the speed of light. In particular, quantum physics suggests that ‘freak events’ that destabilize the markets are likely to occur.

Price distortions and government intervention

Political agenda, interventions and regulation

Governments occasionally seek to instrumentalize markets for political-economic purposes, such as affordable housing, currency undervaluations, or financial conditions-driven economic expansions. Resulting legislation, regulations, and interventions can have both intended and unintended consequences.

  • The most common example is government policies that influence interest rates. The dominant influence of central banks over short-term rates is well known, but since the global financial crisis both monetary and regulatory policies also seem to have played an important role in the compression of term premia (view post here), liquidity risk premia and credit risk premia. The pervasive influence of government policies over yields at all maturities arises not only from their direct influence on demand and supply but also from it their repercussions on the functioning of markets. This makes the valuation of many assets highly dependent on the underlying policy agenda. Doubts regarding this agenda could lead to disruptive re-pricing of duration risk (view posts here and here). Note that duration risk has a great bearing on many other financial claims, particularly low-beta high-quality stocks. The concept of equity duration represents stocks’ sensitivity to long-dated discount factors (view post here).
  • Financial laws or rules can severely impair liquidity and the functioning of markets. A drastic case would be the introduction of financial transactions taxes that has been discussed for some time in developed markets (view post here). A more subtle example for secondary unintended consequences are the effects of cheap financing and capital controls in China on demand for physical metals (view post here).
  • Government interventions often seek to “manage” a fundamental trend rather than stop or reverse it. The classical example is currency interventions, which often serve to “control volatility” or to temper the pace of appreciation or depreciation. When central banks “lean against the wind” with sterilized interventions they create a combination of price inertia and carry opportunity (view post here), enhancing the profitability of FX carry trades. Moreover, price distortions arise from the fear of announcement or execution of the intervention. Value generation along the fundamental trend can resume after the intervention has been implemented (view post here).
  • Financial regulation can lead to unintended price distortions. For example, the regulatory reform in the European life insurance industry (“Solvency II”) has enhanced the importance of market-based discount factors of the liabilities of some of the largest global bond investors.  This has accentuated the tendency of declines in (already low) long-term bond yields to escalate in feedback loops, as a consequence of large mechanical hedging flows with little consideration of fundamental asset value (view post here).