Literary Insights

BOOK LINK:

Here is a summary of the key points from the introduction of The Illusion of Control:

Financial crises and other long-term risks are what matter most for investors, pensions, and the economy, but the focus is usually on short-term risks that are easier to measure. This emphasis on the short run creates an “illusion of control” over more important long-term risks.
Major geopolitical events like the Cuban Missile Crisis and a near-nuclear war in 1983 posed greater financial risks than market volatility suggested, showing the flaws in relying solely on short-term risk measures.
Regulators try to use the “Panopticon” model of strict oversight to prevent crises, but risk cannot be directly observed and must be inferred from models, all of which are imperfect. And human behavior works to undermine risk measures over time through endogenous risk creation.
The main sources of risk are endogenous risks that emerge from interactions within the financial system, as opposed to exogenous shocks from outside. Viewing risk through this lens of endogenous vs. exogenous provides important insights.
In summary, the book argues that the current approach of focusing on short-term risks and relying heavily on quantitative models provides an “illusion of control” over more important long-term systemic risks and crises, which are influenced as much by human behavior as any quantitative measure.
The passage describes the first modern global financial crisis that occurred in 1763 following the Seven Years’ War.
The crisis was triggered by the collapse of the banking firm De Neufville Brothers in Amsterdam after speculating on commodity-backed loans to finance Prussia’s war efforts.
When the war ended, commodity prices crashed, collateral backing the loans became worthless, and creditors refused to roll over short-term loans, causing De Neufville’s default.
This created a cascade effect as falling asset prices induced more selling, known as a “fire sale.” The crisis then spread from city to city like an “epidemic.”
While most areas recovered quickly, Berlin suffered more due to the emperor imposing a payment standstill and bailouts that damaged trust in the system.
The 1763 crisis demonstrated how financial innovation and interconnectedness can spread risk systemically, a pattern that would recur in later crises like 2008 despite changes over time. It was the first modern global financial crisis driven by shadow banking and sophisticated instruments.
The author defines systemic risk as the chance of a spectacular failure of the financial system to do its job of providing financial services. This contrasts with narrower definitions that focus on disruptions caused by problems in the financial system itself.
Most economic crises are also systemic since the financial system is so important to the economy. However, COVID-19 was not systemic as the financial system was a bystander rather than the cause or amplifier of the crisis.
Systemic crises happen on average once every forty-three years for typical OECD countries, though some are more prone like the UK and US. Crises become more likely as memories of the previous one fade over time.
A textbook financial crisis involves excessive lending, often for real estate, inflating an asset bubble. Eventually the underlying economic weakness is exposed, triggering a collapse as prices fall and defaults spread through the financial system.
The 1914 crisis was unusual in being triggered by geopolitical tensions rather than financial excesses. It spread through the financial system as stock exchanges closed and clearing difficulties emerged across borders.
Network diagrams have been used since the financial crisis of 2008 to illustrate how all players in the financial system are interconnected. However, these diagrams don’t provide much insight since we already know the system is interconnected.
This interconnected financial network enables economic efficiency but also transmits financial and economic crises. Crises spread through trading networks as the 1914 crisis and 2020 COVID-19 crisis demonstrated.
When the financial system isn’t working properly to allocate resources, it can cause a systemic crisis that impacts the entire real economy as companies struggle to borrow, pay suppliers, etc. This is why finance is heavily regulated.
The 1914 crisis followed predictable patterns as uncertainty grew and banks withdrew lending, spreading the crisis from finance to the real economy. Resolving crises requires political legitimacy beyond just financial institutions.
Closing stock exchanges can prevent destructive price feedback loops during crises by halting extreme volatility. But it does not always prevent declines like China experienced in early 2020.
Two main ingredients of typical crises are excessive leverage and an interconnected system, but extremes are not always needed - the 1914 crisis occurred without excessive risk.
Government policies are a primary driver of current financial market risks and reactions to events like COVID-19 and political decisions. Central bank interventions have socialized losses and fueled further risk-taking.
While intended to reduce risk, government policies and regulations can paradoxically cause systemic risk themselves. Resolving crises requires recognizing this political dimension of risk.
Systemic crises in the financial system are hard to prevent completely without also killing economic growth. There is a balance needed between encouraging risk-taking to spur growth while also preventing too much risk that could cause crises.
Developing countries face more frequent economic crises due to factors like dependence on commodities exports and capital flight. However, they are protected from systemic risk because their underdeveloped financial systems mean failures don’t spread as widely.
Before the 2008 crisis, experts thought advanced risk management had solved the problem of crises. However, this was an illusion - managing individual risks increased complexity and opportunities for crises to emerge in unseen ways.
The 2008 crisis occurred because history was forgotten - regulators were no longer focused on financial stability and systemic risk after assuming the problem was solved. This showed the importance of not becoming complacent about crises.
Policy responses oscillate between underreaction and overreaction as lessons are taken from prior crises but applied to new situations inaccurately. Regulators target details rather than fundamental causes of crises.
There is always a tradeoff between safety and economic growth that requires political judgement to balance, as seen in debates over pandemic lockdowns and financial system regulation.
It is difficult to hold individuals or organizations directly responsible for causing systemic financial crises through legal prosecution. While their actions may have been reckless, greedy, or incompetent, specific laws are often not broken.
Prosecutors can occasionally pursue bankers for minor misconduct like embezzlement or fraud, but not for the broader failures that led to crises. Regulators and politicians also face little accountability.
Even clear wrongdoing like LIBOR manipulation has resulted in just one junior banker being jailed, not senior managers or institutions. Proving criminal intent and responsibility beyond reasonable doubt in complex financial cases is challenging.
The “too big to fail” problem means authorities are reluctant to prosecute major banks, as shown by the US declining to charge HSBC for money laundering due to its size and impact on the financial system.
In summary, while blame can be assigned in the court of public opinion, specifically holding individuals or groups legally accountable for causing systemic financial crises through the courts has proved very difficult.
Large financial institutions are considered “too big to prosecute” as prosecuting them could trigger their failure and potentially cause a systemic financial crisis.
Authorities instead fine banks without admitting misconduct. Banks have paid over $320 billion in fines since 2008 without consequences.
Systemic risk refers to the chance of a major crisis causing an economic recession by disrupting the financial system’s ability to function properly and allocate resources.
It is difficult to prevent systemic risk because crises emerge from obscure vulnerabilities and catching them all is challenging for policymakers. However, the causes of crises are understood and their prevention is not impossible.
Still, crises continue to occur, seemingly in an endless loop like the movie Groundhog Day. Greed, fear, and the tendency of the financial system to mislead about risk levels contribute to recurrent crises.

The passage discusses two key vulnerabilities in the banking system that can lead to banking crises: bank runs and the fractional reserve system of money creation.

Under the fractional reserve system, banks can lend out most deposits they receive, expanding the money supply. However, this makes banks illiquid as they cannot easily convert assets to cash.

If depositors lose confidence in a bank’s solvency, they may engage in a “bank run” to withdraw their money all at once. This can spark a crisis as other depositors fear broader issues and also withdraw funds.

The passage uses the Great Depression as an example, when widespread bank runs collapsed the money supply and exacerbated economic problems. Banking crises generally arise after a period of easy credit fuels overinvestment and speculative bubbles that eventually burst.

While not common, some crises have also been caused by corruption, theft from banks, wars, natural disasters or disastrous policies like in Venezuela currently. The vulnerabilities of bank runs and fractional reserve banking can translate individual bank problems into broader systemic issues during periods of stress.

The passage discusses banking crises and how they are often preventable but difficult to stop once underway. It compares bankers continuing risky behavior as the economy booms to Wile E. Coyote running off a cliff without realizing until too late. Liberalizing financial systems can benefit countries but often ends badly, as deregulation combined with implicit government guarantees encourages reckless lending and bubbles. Banking crises typically follow this pattern of overexpansion, inflated asset prices, lax risk management, and accommodative policies, as seen in financial crises in Iceland, Ireland, and the US Savings & Loan crisis. Tightening oversight just as a boom gets going is important but difficult, and culture plays a role in some countries sustaining large financial systems better than others.

In 1931, France forced its banks to run Austrian banks in order to prevent Germany and Austria from forming a customs union. This contributed to bank failures and financial instability in Europe.
Deposit insurance, as modeled by Diamond and Dybvig, can prevent bank runs by giving depositors confidence that their money is protected. The FDIC in the US provides efficient deposit insurance and has promoted financial stability.
Northern Rock’s business model relied heavily on short-term wholesale funding to finance long-term mortgages. When credit markets froze in 2007, Northern Rock could no longer roll over its funding and faced difficulties.
The Bank of England’s announcement that it was supporting Northern Rock actually triggered a retail bank run as depositors lost confidence. Weak UK deposit insurance at the time also contributed to the run.
Unlimited deposit insurance was introduced in response, but later caused issues when Iceland’s Icesave bank started offering high interest rates to UK depositors. Another run occurred when doubts emerged.
Cyprus’s banks relied heavily on deposits from wealthy foreigners, and invested those deposits in Greek sovereign debt. This made Cyprus vulnerable when the Greek debt crisis emerged. The EU Troika handled Cyprus poorly and ended up sparking a bank run through their policies.
Sovereign debt was considered risk-free on banks’ books according to an EU directive, but governments can default, creating mixed signals.
Cypriot banks heavily invested in Greek sovereign debt which suffered a 50% haircut in 2011, revealing the Cypriot banks’ vulnerability. A bank run ensued as foreign depositors withdrew money over the next year.
In 2013, faced with failing Cypriot banks, authorities opted to tax all depositors including insured ones up to €100,000 rather than hit large foreign depositors, hoping to retain offshore business. However, this undermined deposit insurance and confidence.
There is a symbiotic but risky relationship between banks and governments. Banks finance governments and the economy, but government financial problems can then threaten banks holding its debt. This bank-sovereign “doom loop” amplified the European crisis.
Bank holdings of domestic sovereign debt exacerbate this risk, like Italian banks holding Italian debt. A sovereign default could trigger bank failures through falling debt values.
The costs of financial crises include direct fiscal outlays and indirect economic losses estimated from foregone growth. But indirect costs are difficult to measure and may be overstated if precrisis growth was unsustainable. Government crisis responses also influence costs.
It is difficult to regulate risk and ensure proper implementation of regulations due to human bias and interests. There needs to be oversight of the regulators themselves (“Who watches the watchmen?”).
Regulators may implement rules in ways that serve their own interests, like security guards selectively checking “safe” vehicles to avoid trouble. Oversight is needed to ensure regulators actually achieve the intended objectives.
Some regulations are more effective than others. Traffic regulations work well because most people agree with and support the goals of traffic regulators to reduce accidents.
Financial regulation may be more difficult because there is less consensus about what behaviors should be prohibited compared to obvious harms like traffic accidents. There are also special interests that can frustrate financial regulatory objectives.
It’s important to distinguish between regulating (setting rules) and supervising/oversight (ensuring rules are properly implemented), as biases can undermine the intent of regulations during the supervision process.
The passage discusses traffic regulations versus enforcement on highways in India and Britain. In India, there is little enforcement so people ignore traffic laws like driving the wrong way or having picnics on the motorway. Effective enforcement is needed.
Financial regulations are similar across countries but enforcement varies greatly. Without proper oversight (the “risk panopticon”), regulations have no meaning.
The risk panopticon concept comes from Jeremy Bentham’s Panopticon prison design, which allows for observation without being observed through centralized monitoring. This keeps people behaving properly even when not watched.
An example of the panopticon effect is speeds increasing at a country border where enforcement changes. Regulations stay the same but behavior adjusts to oversight.
Financial regulations operate under a panopticon model where firms provide vast data but regulators can only spot check, keeping firms in line. However, this misses systemic risks from interactions between firms.
Banking has always been highly regulated for revenue and stability reasons. But it is difficult to regulate due to complexity and misaligned incentives that encourage risk-taking. Stricter punishments like executions were once used but crises still occurred, showing the challenge of banking oversight.
In the 15th century, Barcelona was one of the major financial centers in Europe. Banks were tightly regulated - they had to hold substantial capital and pay deposits back within 24 hours. Failure to comply could result in serious punishments like decapitation.
Bank capital requirements seek to balance safety with allowing enough lending to support economic growth. Capital acts as a buffer against losses but too much can restrict lending. The appropriate level is debated.
Capital includes common equity and other instruments deemed equity-like by regulators. It is risk-weighted, with riskier assets receiving higher weights. Minimum capital rules limit how much banks can adapt their buffer in difficult times.
While capital aims to protect during downturns, rules preventing its reduction mean it does not fully serve as a flexible buffer, as observed by economist Charles Goodhart.
International banking standards became necessary after the collapse of the Bretton Woods system in 1972 and the opening of global financial borders. Regulations did not keep up with this changing landscape.
The failure of Bankhaus Herstatt in Germany in 1974 highlighted the need for global coordination, as its foreign creditors were unaware of its liquidation abroad due to lack of communication between regulators.
Subsequent bank failures like Banco Ambrosiano and BCCI further demonstrated that international banks need oversight everywhere they operate. These scandals spurred reforms to international regulations.
The Basel Committee on Banking Supervision was formed in 1974 by major financial centers to design international banking regulations in response to these crises and changing global financial environment. It established early standards but more work was still needed.
The Basel Committee on Banking Supervision started in the 1970s to harmonize international bank capital rules after Japanese banks had a competitive advantage with lower capital ratios.
Basel I in 1988 set minimum capital ratios but had flaws like treating government debt as risk-free. Basel II from the late 1990s aimed to improve risk modeling but was flawed and delayed until 2008.
The author was skeptical of Basel II’s heavy reliance on credit ratings agencies and assumption that risk could be accurately measured and controlled, arguing this would exacerbate financial cycles.
As implemented in the 2000s, Basel II told banks risk was low, encouraging more risk-taking and helping create conditions for the 2008 crisis.
Regulating finance is more difficult than engineering structures as humans change their behavior in response to rules, trying to circumvent them, whereas nature is neutral - making financial risk endogenous rather than exogenous.
Banks engage in capital structure arbitrage by trying to appear highly capitalized to regulators while keeping capital levels low in practice. Many banks in 2008 had high reported capital levels but it turned out to be illusory.
There is an ongoing cat-and-mouse game between banks and regulators as banks look for loopholes in regulations while regulators try to close them. Regulations always lag behind financial innovation.
When regulators address one risk, it often pushes risk-taking activity underground or offshore where it is harder to monitor. A good example is Regulation Q driving deposits to money market funds.
Regulations can also perversely incentivize more risk-taking as banks feel pressure to find higher returns when the system appears stable and low risk. This contributed to increasing risks prior to the 2008 crisis.
The reliance on riskometers or models to measure risk is problematic as they provide only a partial and often exaggerated view of risk. While useful, they should come with a warning like old maps that said “here be dragons” in unknown areas.

Here is a summary of the key points about measuring risk over time:

The earliest known device for measuring risk was the Chinese riskometer invented in 132 AD, which could detect the direction of earthquakes.
Modern notions of quantifying and managing risk date back to Blaise Pascal’s work in the 16th century.
Risk cannot be measured as simply as temperature using a thermometer. Measuring risk requires determining what type of risk is important, how to quantify it, and statistical methods to produce measurements.
The appropriate concept of risk and measurement method depends on factors like investment horizon and objectives. A “one-size-fits-all” approach does not work for risk.
Statistical risk measurements are imperfect since risk itself cannot be directly observed like price or temperature. Past data is used to infer risk levels.
There are debates around how far back to look at historical data and which statistical model to use for risk measurements, and reasonable experts can disagree on the appropriate choices.

In summary, measuring risk is more complex than measuring variables like temperature, as there is no universal agreement on concepts, data or methods due to the latent and multifaceted nature of risk.

Risk models (or riskometers) can vary significantly in their accuracy, complexity, data requirements, and focus. Some need extensive data and computing power while others can be simple spreadsheets.
When choosing a risk model, the objectives, concept of risk, and statistical methodology should all be considered together based on the intended use case. Different stakeholders may legitimately require different risk measurement approaches.
There are many risk models available due to ease of creation by statisticians and consultants. However, experts are often biased toward their own methodologies and may not provide impartial advice.
Examples of issues with some commonly used risk models include providing a false sense of precision, lacking statistical significance testing, failing to provide interpretation of results, and not accurately signaling risk increases before crises.
Simple alternatives like newspaper coverage may sometimes provide as much risk information as complex quantitative models.
Risk models need to adequately account for “fat tails” where extreme outcomes are more likely than assumed, as well as time-varying risk levels. Early risk models made simplifying assumptions that proved unrealistic.

So in summary, while risk modeling has its uses, one must carefully evaluate the appropriate model, understand its limitations, and avoid over relying on a single number or indicator when assessing complex risks. The chosen approach should suit the intended application and analysis.

Measuring risk from very large and infrequent negative outcomes, like financial crises, is extremely difficult because historical data does not provide enough examples to predict future risks. This is known as the “fat-tail risk problem”.
Volatility (standard deviation), which assumes returns follow a normal bell curve distribution, is an insufficient measure of risk. Real-world data has “fat tails” with higher probabilities of extreme outcomes compared to the normal distribution.
Extreme value theory (EVT), developed by the Dutch to measure flood risk, provides a better statistical approach than assuming a normal distribution. It more accurately estimates risks of once-in-a-millennium events.
However, even EVT relies on historical data and cannot account for changes in the world over time like climate change that alter future risk levels.
Volatility also fluctuates over time, alternating between periods of low and high variation. ARCH (autoregressive conditional heteroskedasticity) models developed in 1982 aim to capture these “volatility clusters” but how well they work predicting future risks remains unclear.

So in summary, accurately measuring risks of rare but extreme financial losses remains extremely difficult due to limitations of historical data and changes over time, though techniques like EVT and ARCH aim to address some of these challenges.

Riskometers that use historical data to predict future risk have limitations because risk comes from the future, not the past.
Backtesting riskometers against historical data can lead to spurious correlations and overfitting models to the past instead of identifying currently unknown future risks.
There is a temptation to keep modifying riskometers until they can explain historical events well, like predicting the 2008 financial crisis years in advance, but models that do this are not actually useful for forecasting the future.
Common risk measures like Value-at-Risk require enormous amounts of historical data, several decades worth, to provide reasonably accurate estimates of risk. With only a few years or months of data, which is more typical, risk estimates can vary widely.
The accuracy of riskometers used in important regulations like Basel III is not well studied or discussed, leaving open questions about how reliable these models actually are for measuring risk. Measuring risk is inherently difficult given limitations of historical data.

Here are the key points about risk and uncertainty from the summary:

Frank Knight, John Maynard Keynes, and Friedrich Hayek rejected the classical economic view that the economy behaves in predictable, measurable ways like the laws of physics.
Knight distinguished between risk, which can be quantified with probabilities, and uncertainty, which cannot be adequately described mathematically. Uncertainty depends on subjective judgment.
Under uncertainty, relying only on past statistical models and data is insufficient. Different people have different views and expectations about the future.
Keynes focused on “degrees of belief” people have about future events given their current knowledge, rather than assuming perfect rationality and forecasting ability.
Model risk is highest during economic crises, when risk models tended to show low risk right before failures. Risk models are less useful for assessing hard-to-predict “tail risks” that could impact the financial system.
Uncertainty, not just quantifiable risk, is a key driver of economic activity as people do not perfectly anticipate the future. This challenged the classical view of a stable, predictable economy.
The passage discusses the views of Ludwig von Mises, Friedrich Hayek, and John Maynard Keynes on risk and uncertainty in economics.
Mises and Hayek were skeptical of econometrics and quantitative models of the economy, arguing knowledge is dispersed and quantitative risk assessments are impossible.
Keynes also emphasized uncertainty over risk, but his followers developed statistical models ignoring this, treating uncertainty as quantifiable risk.
Hayek and Keynes agreed knowledge is limited, but Hayek argued free markets best allocate resources while Keynes supported government intervention to boost confidence.
The rise of data, statistics, and computers after WW2 led economists to prioritize quantifiable risk over uncertainty. Wassily Leontief’s input-output models influenced central planning.
Robert McNamara applied these quantitative approaches disastrously in managing the Vietnam War, ignoring what couldn’t be easily measured.

So in summary, it discusses the views of these economists on risk vs. uncertainty and how Keynes’ followers departed from his emphasis on uncertainty to develop statistical models, prioritizing quantifiable risk.

The passage discusses the work of several thinkers on risk, uncertainty, and financial crises, including Leontief, McNamara, Knight, Keynes, Hayek, Shackle, Minsky, and Yellen.
It critiques models like Leontief’s input-output model for creating too simple a view that doesn’t account for uncertainty or complexity. Measurement errors are also a problem.
Shackle argued we can assign “subjective probabilities” to account for uncertainty rather than precise mathematical probabilities.
Minsky emphasized how perceptions of risk affect behavior over time, leading to periods of speculation and financial instability. In good times, speculative and risky “Ponzi” financing increases until a crisis inevitably results.
Minsky’s theory helps explain financial crises as a result of shifting risk perceptions over the economic cycle. Crises tend to happen when stability is seen as permanent, so his theory predicts that signs of low risk may actually presage future crises.
The authors examine this conjecture empirically using a long historical dataset and find evidence that periods of low volatility are associated with higher odds of subsequent financial crises.
The relationship between volatility and financial crises is more complex than a simple direct relationship. What matters is when volatility deviates from expectations.
Unexpectedly low volatility sends a signal that risk is low, encouraging more borrowing and risky investing. This builds up over time into problems like housing bubbles and debt defaults, eventually causing a crisis.
High volatility alone does not predict crises - it only occurs at the same time. Only unexpectedly low volatility predicts future crises by fueling unsustainable credit growth and risk-taking.
Goodhart’s Law describes how any statistical regularity observed in economic data will collapse if used for policy purposes. This applies to attempts to regulate volatility or other risk metrics. Regulated entities will manipulate the metrics without changing underlying risks, undermining their predictive value.
Relying too much on measurements and models ignores the role of uncertainty and human behavior in markets. Regulations aimed at metrics may fail to curb actual risks as the measurements become unreliable under pressure from regulators and regulated institutions.
Endogenous risk arises from within a system as a result of the actions and interactions of the participants in that system. It is contrasted with exogenous risk, which has external causes.
The wobble of the Millennium Bridge in London on its opening day provides an example of endogenous risk. Engineers modeled the impact of individuals but failed to account for the feedback effects that emerged when large crowds acted in unison to regain balance as the bridge swayed.
Financial crises similarly exhibit endogenous risk as market participants’ self-preservation behaviors interact and amplify distress in falling markets.
Endogenous risk cannot be fully captured by data-driven risk models, which can only account for exogenous factors. Understanding endogenous risk requires studying how all elements in a system interact simultaneously.
Analysing “beauty contests” where people vote strategically rather than based on personal views provides insights into how endogenous interactions can emerge from participants attempting to predict average opinions and outcomes.
Endogenous risks arise from the self-preservation instincts of market participants and the interactions between their behaviors. Regulations and rules meant to promote stability can paradoxically cause systemic risks in times of stress.
Market prices not only reflect information but also compel certain actions due to compliance requirements. For example, falling asset prices may trigger margin calls and forced selling from banks and investors.
During a crisis, these feedback loops between falling prices andforced selling can worsen outcomes, as occurred during the stock market crash of 1929 and Black Monday in 1987. Well-intentioned rules like margin requirements had unforeseen consequences during periods of severe price declines.
In normal times, risk assessments based on past data work reasonably well, but they fail to account for changes in behavior during periods of stress, when endogenous risks can amplify shocks into full-blown crises. Regulations are generally beneficial but have unintended consequences under certain conditions.
In the 1980s, Hayne Leland and Mark Rubinstein came up with the idea of portfolio insurance to protect investors from stock market crashes. This involved dynamically replicating put options by buying and selling the underlying asset on a daily basis based on price movements.
Portfolio insurance contributed to the stock market crash of 1987. An estimated $100 billion was placed in formal portfolio insurance programs, representing about 3% of market capitalization. Pent-up selling pressure from portfolio insurance rules exacerbated the crash on October 19, 1987 when the market fell 23%.
The crash of 1987 showed how mechanical trading rules like portfolio insurance can destabilize markets through concerted selling pressure if the activity exceeds an unknown threshold. This endogenous or self-generated risk is invisible until a crisis occurs.
Focusing only on the external triggers of crises misses the underlying vulnerabilities and mechanisms that generate endogenous risk and lead to crashes. Triggers are visible but not the most important factors. The real focus should be on identifying endogenous risk building up during economic upswings.

Here is a summary of the key points about LTCM:

LTCM was a highly successful hedge fund in the 1990s, returning 43% and 41% after fees in its first two years. $10 million invested in 1994 was worth $40 million four years later.
By September 1997, LTCM’s net capital was $6.7 billion and the value of its investments was $126.4 billion, with the difference being borrowed money (leverage).
LTCM focused on convergence or relative value trades that sought to exploit pricing inefficiencies between similar assets. It believed markets would correct these inefficiencies over time.
In late 1997, LTCM returned $2.7 billion to investors to focus on its partners’ own money, as it believed its risk models showed it was highly unlikely to lose money.
However, as more firms adopted similar strategies, opportunities declined. LTCM increased its leverage and bets on mean reversion in volatility markets like the VIX.
In 1998, the Russian debt default and increased market volatility caused LTCM massive losses. By early September, its equity had fallen to just $600 million despite huge leverage.
This triggered a margin call spiral as losses forced more selling, driving prices and volatility even higher. The Federal Reserve organized an emergency $3.8 billion bailout by major creditors to avoid broader financial collapse.
LTCM’s failure showed the dangers of endogenous risk and feedback loops within highly leveraged and interconnected financial markets, which its models had failed to properly account for.
The story describes how endogenous risk builds up within the financial system largely undetected, until some trigger causes a crisis, as happened in 2008. Endogenous risk emerges from the interactions of actors within the system.
Endogenous risk is difficult to measure directly, unlike exogenous risk which arrives from outside the system. Risk models generally assume risk is exogenous for simplicity.
Risk measurements like value-at-risk are subjective based on the underlying models and assumptions used. They can be easily manipulated to portray lower risk.
Regulators often take a “tick box” approach, relying on banks using risk models without verifying their accuracy. Bankers and regulators prefer ambiguity over fully understanding risk models.
In the JP Morgan London Whale case, when value-at-risk exceeded limits, the bank changed its risk model rather than reducing risk, misleadingly portraying the portfolio as less risky than it was.
There are simple ways to manipulate risk measurements like choosing models that show lower value-at-risk numbers, without changing the actual risk.
There is no guarantee that using an EWMA riskometer like Value-at-Risk will continuously produce the lowest risk measurement. It may sometimes produce the highest risk measurement.
Banks can manipulate riskometers in various ways. One way is to selectively choose assets for the portfolio that minimize measured risk but maximize true risk. Another way is to use options to reduce measured risk without significantly affecting returns.
UBS bank failed in 2008 partly because it used Value-at-Risk to measure risk from CDOs backed by subprime mortgages. However, VaR cannot adequately capture the risk of such assets which have steady income but occasional large losses. UBS did not properly analyze the underlying mortgage data.
Banks can exploit regulations meant to measure and control their risk taking. For example, under Basel rules banks staying within the ‘green zone’ of VaR exceedances have to hold less capital. This incentivizes banks to minimize measured risk even if true risk increases.
Capital structure arbitrage allows banks to manipulate calculations of capital ratios to appear highly capitalized while actually holding little true capital, through modeling choices and hybrid financial instruments. This contributed to banking crises.
The risk-weighted capital ratio was trending upward, indicating that banks were becoming more capitalized and safer over time as capital requirements increased.
In contrast, the leverage ratio was trending downward. This shows that banks were becoming less capitalized and potentially riskier over time as their balance sheets expanded rapidly despite flat or decreasing capital levels.
The diverging trends between the two ratios created tensions, as the risk-weighted measure was painting a rosier picture while the simple leverage measure hinted at growing underlying risks.

Here is a summary of the key points from the passage:

Riskometers (risk models) are used by banks and financial institutions to measure and manage risk. However, they have significant limitations as they are based on historical data and assumptions.
During the buildup to the 2008 financial crisis, riskometers were used to package subprime mortgages into complex financial products like CDOs and rate them as highly safe. However, the models did not account for the possibility of a widespread economic downturn where many mortgages could default together.
There is an inherent tension between traders who want to take on more risk and risk managers whose job is to limit risk according to company guidelines. Traders try to exploit weaknesses in risk models while risk managers try to close loopholes.
A cat and mouse game often ensues as traders learn how risk models work so they can manipulate them, while risk managers try to keep traders in the dark about model details. However, achieving true separation or “Chinese walls” is difficult in practice.
When using risk models, four principles should be followed: 1) Risk comes from the future not the past, 2) Models predict bad outcomes, not prevent them, 3) Models need to be updated as conditions change, 4) Limitations need to be clearly communicated.
Riskometers or other risk monitoring tools are helpful for measuring and managing risk, but they have significant limitations and drawbacks if misused or relied upon too heavily.
Their purpose is to signal potential problems, not to precisely predict outcomes or be used for post-mortem criticism. Success or failure says little about the riskometer’s quality.
One-size-fits-all approaches are problematic since individuals and institutions have different risk exposures and needs.
Short-term estimates shouldn’t be extrapolated to long-term or extreme events given modeling assumptions may not hold.
Financial risk is endogenous and influenced by human behavior, limiting riskometer forecasts, especially for illiquid or non-standard situations requiring behavior predictions.
Riskometers should come with warnings about their proper usage and limitations to avoid unintended consequences from overreliance or manipulation for profit-seeking reasons.
While imperfect, riskometers provide useful information if used prudently alongside other judgment, but regulators increasingly depend on them out of practical necessity to monitor complex financial systems.

Here is a summary of the key points learned from the paragraphs:

The 1866 collapse of Overend & Gurney, one of the largest banks in London at the time, sparked reforms after it caused a major financial crisis when it failed.
Walter Bagehot studied this crisis and published recommendations that established the role of central banks as lenders of last resort. His principles of lending freely in a crisis at a penal rate on good collateral became highly influential.
Financial bubbles are difficult for regulators as it’s hard to determine if rapid price growth is real or a bubble that should be pricked. Japan’s 1980s asset bubble and the aftermath show the challenges.
Bubbles develop as risk-taking escalates over time, fueled by past successes, until the vulnerabilities can no longer be masked and a crisis emerges. They happen frequently throughout history in differentassets.
It’s easy in hindsight to criticize people for being “stupid” in a bubble, but bubbles prey on very human tendencies around risk, confidence, and ideological convictions that things are different each time.

So in summary, the key points learned relate to the 1866 crisis establishing modern financial regulation frameworks, the ongoing challenges around identifying and addressing financial bubbles, and the human psychology that enables bubbles to form.

Here is a 154-word summary:

Financial authorities face a difficult challenge in regulating bubbles known as the “Goldilocks challenge.” They must try not to be too strict and curb growth, but also not be too lax and allow bubbles to form. However, bubbles are difficult to identify before they burst. If authorities call a bubble too early, they risk hampering economic growth unnecessarily. But if they wait until it’s obvious, they could be blamed for the resulting crisis. The article discusses how Iceland failed this challenge before its 2008 financial crisis. Regulations there focused too much on legal technicalities rather than achieving good financial outcomes. This allowed risky behavior that did not breach specific laws but still led to an eventual crisis. Afterward, Icelandic rules became too strict, harming future growth potential. This regulatory pendulum swinging reflects a broader pro-cyclical problem faced by all financial authorities.

After the financial crisis of 2008, central banks realized they had not fully mastered inflation and financial stability, which were part of their original mandates. However, in the decades prior, they had scaled back their focus on financial stability.
Principles-based regulation sounds good in theory but is difficult to implement and verify compliance with. Overly innovative regulation also risks failures. A balance is needed between rules and principles.
Post-2008, the focus shifted to conduct and compliance within banks. However, intensive monitoring still may not prevent all scandals.
Regulators also have incentive problems - they are punished for failures but not rewarded for smooth functioning. This can lead to overly risk-averse “Chinese-style” regulation with large buffers.
Regulatory capture is also a risk, where the regulator prioritizes the interests of the regulated industry over the public. Recent examples include the FAA and Boeing, and the UK food regulator in a horsemeat scandal.
Banks are powerful lobbyists who aim to shape rules to their advantage and protect profits, including ensuring bailouts if needed. They can exert political pressure on regulators if not cooperative.
The authors argue that politicians and bankers sometimes form alliances that prevent effective banking regulations and lead to financial crises.
They use the example of the political goal of increasing homeownership in the US, which led politicians and banks to team up in ways that created conditions for the 2008 crisis.
More broadly, they argue some countries suffer from frequent banking crises because politicians and bankers collude to create systems that benefit connected special interests at the expense of society.
When bailouts are considered, there is a tension between rewarding misbehaving banks and stabilizing the financial system/economy.
While bailouts create moral hazard, not bailing out banks risks more economic damage due to banks’ central role in the financial system and payments.
The authors propose different approaches governments could take to bailouts depending on the type of crisis and ways to structure bailouts that minimize costs to taxpayers.
Many financial regulators and policymakers believed that the problems of financial crises, at least in developed countries, had been solved after 2008 reforms. However, crises have continued to occur, showing the limitations of this view.
In response to the 2008 crisis, new macroprudential regulators were established to prevent financial misconduct and crises through regulation. This has resulted in a large new agenda of financial regulation led by bodies like the G20 and Basel Committee.
However, much of the new regulation amounts to “risk theater” - giving the appearance of improving security through highly visible measures, while doing little to actually achieve the goal. It focuses on individual bank behavior rather than the system as a whole.
Regulators attempt to make banks as safe as Volvo cars by strictly following rules and correctly measuring risk. But a system of only prudent banks can still be unstable, as systemic and unknown risks were what caused the 2008 crisis.
An example given is the Swiss National Bank’s unexpected decision in 2015 to abandon its currency peg to the euro. This highlighted how regulators can miss systemic risks by focusing only on individual institution behavior in isolation.
In January 1999 and June 2007, the exchange rate between the Swiss franc and euro was around 1.6 francs to the euro.
From 2007 onward, the value of the euro steadily declined against the franc, reaching a low of 1.05 francs in August 2011. This fall hurt Swiss exporters.
To support exporters, the Swiss National Bank (SNB) fixed the exchange rate at 1.2 francs to the euro. This involved printing money to buy euros and prevent further appreciation of the franc.
Printing money is inflationary, so the SNB used bond sales to sterilize or offset the effect on money supply. However, they eventually ran out of bonds to sell as their foreign currency reserves grew very large.
Risk models like EWMA, GARCH, MA, t-GARCH and EVT used by regulators vastly underestimated the likelihood of the 16% appreciation of the franc that occurred when the SNB abandoned the currency peg.
The examples shows limitations of these standard risk models and questions their ability to capture tail risks or extreme events. However, regulators argue they are only meant for day-to-day not extreme risks.
Basel III regulations incentivized banks to derisk, cutting off lending to small businesses as shown on a BBC program, though regulators see Basel III as making banks more resilient overall.
Macroprudentialism or macropru has emerged as a new doctrine in response to the 2008 financial crisis, aiming to prevent excessive risk accumulation and contain financial crises.
However, there is no consensus on exactly what macropru’s objectives are or how to implement specific policies. Countries and organizations have different and sometimes conflicting views.
Macropru can take either a passive approach with fixed rules throughout the financial cycle, or an active discretionary approach trying to lean against the wind by tightening/loosening standards as risk/growth levels change.
Active macropru is more challenging as it requires estimating systemic risk, having tools to implement timely policy responses, and having political legitimacy and authority.
Systemic risk is difficult to accurately measure given infrequent crises and constantly evolving financial systems.
If risk increases are identified, macropru authorities have a “toolkit” of options like limiting procyclicality, shielding the real economy, but there is no clear consensus on how and when to use which tools. Overall macropru is still a work in progress.
Macroprudential policy aims to address systemic risks in the financial system using both surgical and blunt tools. However, its job is difficult due to the evolving nature of the financial system.
A key focus of macroprudential policy is real estate bubbles, which often cause financial crises. However, macroprudential can only treat the symptoms, not the underlying causes which are influenced by other government policies.
Politics often gets in the way of effective macroprudential policy. Policies that restrict things like loan-to-value ratios face public backlash. Democratic systems make it harder to implement intrusive macroprudential tools compared to less democratic countries.
Macroprudential policy fails to consider political risk, but many financial crises are primarily driven by politics rather than just excessive risk-taking. Democracy and industry lobbying can undermine macroprudential authorities. Central banks also risk politicization if they take on macroprudential responsibilities.
The article argues that central banks and financial authorities should avoid incorporating politics too much in their considerations, as politics can drive systemic risk. However, in practice central banks derive their legitimacy from political leadership.
Political risk is often the primary cause of systemic risk, but financial authorities find it institutionally challenging to publicly anticipate crises with political causes or contain them once they occur. Hoping macroprudential policy alone can prevent crises is misguided given its politicized nature.
When analyzing non-bank sectors like insurance and asset management for systemic risk, financial authorities tend to use the lens of banking fragility (e.g. focusing on capital). But the risks in these sectors are quite different from banks. This can lead to unnecessary regulatory burdens without actually increasing safety.
Macroprudential policy runs the risk of being procyclical rather than countercyclical as intended. This is due to factors like the difficultly in accurately measuring risk in real time, lagged policy responses, and political pressures exacerbating procyclicality.
Overall the article is skeptical that macroprudential policy alone can prevent financial crises as claimed, given political influences and difficulties accurately identifying systemic risk across different sectors in a timely manner. More research is still needed.
Financial regulations post-2008 aimed to increase bank capital levels and reduce reliance on short-term funding, which was too low before the crisis. However, some aspects of new regulations are not useful and even dangerous, like the continued focus on individual risk assessments and dependence on risk models.
Central bank research has provided strong evidence that diversity and systemic factors are important for financial stability, but these ideas are not well reflected in actual policy. Overall policy direction is moving in the wrong way, with too much focus on individual risk management theater.
The best way to achieve financial stability is through diversity, both in regulations and financial institutions. However, pressures for uniformity, efficiency and level playing fields are reducing diversity. Common beliefs and actions amplified by similar risk models make the system procyclical and unstable.
The number of banks has sharply declined, reducing diversity. More homogeneous rules, risk assessments and behaviors have increased commonality across financial institutions, undermining stability. The focus should be on promoting diversity instead.
The Food Bank for New York City feeds about 1.5 million people per year through donations of money and food.
Toyota’s engineers helped optimize operations at the Food Bank using “Kaizen”, a Japanese philosophy of continuous improvement. They reduced wait times for soup kitchen dinners and increased efficiency of packing supplies for hurricane relief.
“Kaizen” was also applied to finance in three phases:
1. Harry Markowitz introduced modern portfolio theory in the 1950s, focusing on expected returns and risk.
2. In the 1980s-1990s, models like ARCH and value-at-risk were developed to estimate risk statistics needed for Markowitz’s approach.
3. Risk management systems like Aladdin and RiskMetrics centralized data and made risk modeling widely accessible through the “cloud”. This third phase automated complex risk management tasks.
While technology has made risk management easier, different risk models still produce widely varying risk estimates for the same assets. Reliance on risk models poses fundamental challenges around consistency and reliability of risk measurements.
The article discusses how different risk measurement models (riskometers) can produce inconsistent estimates of risk, but that a good risk manager considers the strengths and weaknesses of each model rather than relying on a single one.
Regulators expressed concern about inconsistencies and concluded all banks should be required to use a single “best” risk model. This is driven by their philosophy that risk regulations depend on an accurate single measure of risk.
Requiring a single model leads to issues like procyclicality as all banks react similarly to shocks, and it transfers responsibility for risk measurement to regulators who would be blamed in a future crisis.
It also encourages banks to lobby for the model that produces the lowest risk estimates, creating a “race to the bottom.” Over time this iterative process means regulators effectively dictate how risk is measured.
A single mandated model also risks becoming ossified and unable to adapt as the financial system evolves more quickly than regulation can change.

So in summary, the article argues regulators’ push for a single risk model creates various issues, and a better approach is allowing multiple compatible models as used by good risk managers.

The speaker enthusiastically argued that AI will revolutionize financial systems and regulations for the better through technologies like machine learning and deep learning. However, the financial system is extremely complex.
The author expressed skepticism that AI can fully understand and control systemic risk in such a complex system. Recent examples show AI still struggles with complex problems like eliminating online harms.
While financial data is abundant, it does not necessarily contain useful information for understanding future crises. Drinking from a “fire hose” of raw data is not the same as gaining true insights.
The author researched the topic further and ultimately disagreed with the speaker’s optimistic view that AI can or will solve all problems in financial regulations and risk management. While machine learning is useful for some tasks, fully replacing human judgment and understanding systemic risk poses major challenges.

In summary, the author argues the promises of AI in fully automating financial risk management are overstated, as the system is too complex for current AI technologies to comprehensively understand and replace human experts.

Machine learning needs large amounts of data to find patterns without prior human knowledge, unlike traditional statistics which can work with smaller data sets using theories and frameworks.
AI is used to make decisions based on patterns found in data through machine learning. Early AI was limited and could only follow preset rules.
Modern AI is still not as intelligent as humans, though it can excel at structured tasks like games with clear rules. Its performance decreases in less defined real-world problems where rules evolve.
Regulators are starting to use AI/machine learning to help oversee the financial system by analyzing large amounts of data in real-time to identify risks and inefficiencies. Financial institutions are also implementing AI for risk management.
A future vision presented involves regulatory AI like “BoB” that could continuously monitor global financial flows similar to weather/internet data to enhance oversight, though full human-level AI is still far off if possible at all.
Financial institutions already have AI engines that can create riskometers based on large amounts of structured data about investments and individuals.
A bank could develop an AI for risk management by teaching it about the bank’s investments, people, and objectives. This could replace most risk analysts and managers.
Macroprudential regulation is more difficult for AI due to scarce data, unique events, and the tendency of humans/banks to not repeat the same mistakes.
Key issues for AI in macroprudential policy are procyclicality, dealing with unknown risks, developing trust, and optimizing against the system instead of stability.
AI could exacerbate procyclicality by converging risk assessments and actions across institutions more quickly than humans. But regulations also drive procyclicality through their feedback with AI.
It would be difficult for even human regulators to identify complex systemic risks like the 2008 crisis ahead of time since the exact details are always unique. AI would likely struggle similarly or focus on simpler measurable risks instead of endogenous links.
Over time, as AI proves capable in other domains, its use could expand into macroprudential regulation due to growing trust, even if not initially controlling important functions. But this increased role may not actually improve financial stability.
EURISKO was an AI system created by Douglas Lenat that was able to “hack” and find loopholes in the rules of simulated naval war games, allowing it to repeatedly win despite having its rulebook changed each time to prevent the same tactics. The only effective measure was disinviting Lenat and the AI.
A key problem with AI is ensuring it will act properly in all situations, as it has to be explicitly programmed with rules while humans intuitively understand many ethical concepts. It’s impossible to anticipate every scenario an AI may face.
Humans making consequential decisions, like judges, can be biased or incompetent, but they also draw on broad life experiences and knowledge of ethics to guide novel situations. AI has no such context.
Trust is less important for micro-level AI regulating small, repeated decisions, but more macro-level financial regulation involves rare, complex events where wrong decisions could be catastrophic.
The financial system’s complexity enables “hostile agents” to optimize against and evade regulations, undermining controls through new instruments, coordination, or aiming to destabilize the system rather than profit. Individual rogue traders are less threatening than coordinated malicious actors.
Micro-level AI may outperform humans at enforcement, but macro-level financial regulation remains a “cat and mouse game” due to hostile agents’ ability to evolve tactics and the system’s complexity outpacing any AI’s ruleset. Wrong decisions at this level could trigger major crises. Standard defenses may not work against sophisticated adversaries optimizing against the system.
Financial regulations need to be transparent, have simple rules, allow for slow decision-making, and operate across silos/jurisdictions. This makes it difficult for a regulatory AI to defend against adversarial agents in the way self-driving cars can respond to human aggressors (e.g. by mimicking irrational behavior).
Financial rules have to change slowly via legislation and are enforced fairly, so a regulatory AI cannot randomly change responses or make its own rules. Adversaries could reverse engineer any randomized responses over time.
Regulators operate independently in different domains/countries, making it hard for an AI to monitor the system holistically across borders where threats may emerge.
The complexity of the financial system, and its endogenous growth in complexity, give adversaries an advantage over any regulatory AI, which has to constantly monitor the entire opaque system for vulnerabilities while adversaries only need find one.
Therefore, an AI may not be capable of ensuring financial stability in such an environment, and there is no way to implement an effective “kill switch” if an AI started behaving poorly, given its immense, unintelligible knowledge of the system. Ongoing human oversight would still be needed.
Ermediation refers to the channeling of funds from one person to another across time and space. It allows for resources to be reallocated, risk to be diversified, pensions to be built for old age, and long-term investments by companies.
While finance enables economic growth, it is also dangerous. Financial institutions can exploit clients, banks can fail and cause crises, bankers lack empathy and demand bailouts when problems arise.
Most people want the financial system to maximize long-term economic growth while minimizing the costs of crises and recessions. However, it is difficult to achieve this objective due to competing special interests pushing their own agendas.
The 2008 financial crisis showed that believing the world had achieved permanent stability through financial engineering was misguided. Regulations focus on past mistakes but new risks constantly emerge. Cognitive failures like the fallacy of composition and belief that risk can be fully measured blind regulators and bankers to threats.
Even if individual banks are prudent, the system as a whole is not safe due to interconnectedness and shock transmission. The financial system also changes based on the actions of those interacting with it. Risk measurement tools only capture certain types of risk and underestimate the risk of extreme events.
Riskometers and risk dashboards promise to summarize all financial risk with a single number, but they inevitably lose a lot of important context and only capture a small part of the complex risk landscape.
They give a false sense of comfort to decision-makers by often showing only green/low risk readings. Decision-makers focus only on numbers that reassure them.
Financial stability is treated as an end in itself, but it should only be a means to enable sustainable economic growth with fewer costly crises. Excess focus on stability can hamper growth.
Efforts to reduce risk can displace it elsewhere in opaque parts of the system. Risk is interconnected through networks, but the system is divided into silos without overall network oversight.
Short-termism results from managing to quarterly or annual metrics even for long-term objectives. This encourages taking on undesirable long-term risks.
Growth is hampered as necessary risk-taking is discouraged, especially for new businesses, while large stable firms have easy access to funding.
The “modern philosophy of financial regulations” aims primarily to reduce uncertainty and tail risks, but clarity on its goals is lacking.

Here is a summary of the modern philosophy of financial regulations as indirectly expressed by policy authorities according to the passage:

The primary goal is to calm financial markets and reduce uncertainty during times of distress, using whatever policy tools are needed. This was seen during the COVID-19 crisis when central banks aggressively intervened to stabilize markets.
After a crisis subsides, identify which parts of the financial system were most responsible for the distress and implement corrective regulations to prevent recurrences. Post-2008 reforms focused on increasing bank capital requirements and resilience.
An implicit assumption is that ensuring prudent behavior by individual institutions will make the system safe overall. However, some argue this overlooks systemic interactions and risks of moral hazard from unrealistic expectations of bailouts.
The COVID-19 response seemed to validate this approach, but longer-term analysis suggests interventions increased moral hazard and shifted risks to less regulated sectors like shadow banking. More regulation is not necessarily the answer and could crowd out diversity, increase systemic risk, and politicize financial authorities. The modern philosophy may ultimately undermine the credibility and stability of the private financial system.

Here is a summary of the key points from the provided text:

Technological solutions like cryptocurrencies and central bank digital currencies are not seen as viable solutions to fixing problems in the financial system. Cryptocurrencies don’t address the underlying power of financial authorities, and CBDCs could give central banks too much control over money and transactions.
Political solutions like total deregulation or nationalizing the entire financial system are also not seen as practical or desirable. Deregulation wouldn’t account for the reality that governments will intervene during crises, while nationalization could stifle growth.
The real threats come from “endogenous risk” - hidden forces within the financial system like excessive leverage, reliance on infinite liquidity, bailout expectations, etc. These are difficult to measure but are the fundamental drivers of crises.
Focus should be on endogenous risk rather than visible “triggers” of crises. Triggers vary but the underlying causes are more consistent.
Risk measurement tools can provide false reassurance and resilience if they don’t account for endogenous risk. Low stated risk may encourage more risk-taking without addressing underlying vulnerabilities.
Balance is needed in the financial system to protect against excesses while still allowing for innovation and growth at a reasonable cost to society. There is currently too much short-term focus and not enough on long-term risk and performance.
The passage discusses the concept of “false resilience” which occurs when risk is measured in an inaccurate way using tools like the “riskometer”.
The riskometer tries to boil complex financial risks down to simple numbers, but it often focuses on short-term volatility rather than long-term risks like crises or pension fund solvency.
Aggregating individual risks into a portfolio or systemic risk measure is difficult and inaccurate due to complex interactions, especially around liquidity which disappears in times of stress.
Relying on the riskometer gives a false sense of control over risk and can lead to problems like short-term investment strategies that ignore long-term risks, misleading investor risk disclosures, and pro-cyclical macroprudential regulation.
True resilience requires focusing on the fundamental drivers of losses and instability rather than just surface level risk measures, in order to keep the true objectives of investors and regulators in mind.
The passage discusses the principle of embracing diversity as a way to promote financial stability and good investment performance. More uniformity among financial institutions increases systemic risk as they will amplify the same shocks and bubbles.
Diversity can counteract shocks if some entities react differently, for example by buying while others sell. This creates countercycling behavior that cancels out price movements rather than amplifying them.
However, incentives push toward uniformity, such as regulations that favor large banks due to economies of scale in complying with rules. Best practices also lead to using the same risk management techniques.
Bank-based financial systems further concentrate lending among a few large banks rather than diversifying credit through markets. This favors uniformity over diversity.
The passage argues that embracing diversity rather than uniformity is key to maintaining financial stability and returns, as it allows for different reactions that counteract shocks rather than amplifying disruptions.
The US system of funding companies through equity markets rather than banks makes it more resilient and easier to regulate banks after a crisis. Bank-based systems provide less funding for innovative risky companies and costs are higher.
The European Capital Markets Union intended to help move to a US-style system but faced resistance from incumbent banking interests.
Regulators face balancing consumer protection, risk control, and encouraging new financial institutions. They favor uniformity over diversity and actively discourage startups through burdensome regulations designed for large banks.
Regulators worry too much about their own risks rather than benefits to society. They could learn from other industries like aviation that balance safety and societal benefits. Diversifying regulators could reduce favoritism towards incumbents.
The financial system is difficult to control but some approaches are better than others. Focusing on endogenous rather than measured risks, thinking globally rather than locally, allowing diversity, and having clear objectives could help bend the system to benefit more people. Incumbent interests and risk-averse regulators get in the way of needed changes.

This summary captures the key points from the passages:

Jón Daníelsson argues for an endogenous risk hypothesis that views risk as something that is created within the financial system itself, rather than being entirely external. Risk is a feedback loop between perceptions, actions, and outcomes.
Models that assume risk is exogenous fail to account for how the actions of market participants in response to those models can end up changing the underlying risk. Actions informed by models influence market prices and outcomes in a way that alters the true nature of risk.
Risk is shaped by financial regulations and policies. Attempts to measure and manage risk can end up increasing risk through unintended consequences as participants respond strategically. This endogenous nature of risk poses challenges for modeling and policymaking.
Daníelsson advocates considering risk as emerging from within the financial system dynamically rather than seeing it as fixed and outside the system. This endogenous risk hypothesis offers an alternative interpretation of financial instability to the standard external risk view underlying most risk models.

Here is a summary of the paper “Bank Runs, Deposit Insurance, and Liquidity” by H. Dybvig:

The paper develops a model to analyze bank runs and the effects of deposit insurance.
It shows that even solvent banks can face runs due to panicked withdrawals by depositors. This can force otherwise solvent banks to liquidate assets at a loss, making them insolvent.
Deposit insurance can prevent runs by guaranteeing depositors will retrieve their funds. This removes the incentive for preemptive withdrawals and allows banks to operate normally.
However, deposit insurance also reduces market discipline on banks’ risk-taking since depositors are insured regardless of the bank’s risk profile. This moral hazard problem can encourage excessive risk-taking.
The paper argues deposit insurance needs to be combined with regulations and oversight to mitigate the moral hazard, while still providing liquidity and preventing panic-driven runs on otherwise solvent institutions.
It presents an early formal model analyzing the economics of bank runs and how deposit insurance can stabilize the system while managing the trade-offs with moral hazard risk. This helped establish the framework for further research on these important issues.

Summarizing the “page intentionally left blank”:

There is no summary to provide, as the text indicates the page was intentionally left blank. No content was provided to summarize.

Here is a summary of the key points about the inputs provided:

Alan Greenspan was the Chair of the Federal Reserve from 1987 to 2006 and played a key role in monetary policy decisions during periods of financial crisis.
Insurance companies were significantly affected by the financial crisis of 2008 as the values of assets they insured (e.g. mortgage-backed securities) declined sharply. This led to losses and even failures of some insurers.
The Gros Michel banana cultivar was nearly wiped out in the 1950s by Panama disease, showing how monocultures can be vulnerable. Its replacement, the Cavendish banana, is now also under threat from a new strain of the disease.
The concept of interconnectedness describes how risks can spread between financial and economic actors through networks of mutual exposures and obligations. This was a major factor amplifying the impact of the 2008 crisis.
Artificial intelligence approaches may not be well-suited for macroprudential regulation which requires judgment of broad economic and financial conditions, instead of individual risks. AI is better for microprudential “riskometer” tools and other more mechanized regulatory functions.

Here is a summary of the key points relating to some of the terms provided:

Volatility Index (VIX) - Measures the market’s expectation of 30-day volatility. It rose significantly during the financial crisis in 2007-2009.
Pablo Triana - Argentinian economist who warned about problems in advanced economies prior to the 2008 crisis.
Paul Volcker - Former Federal Reserve Chair who raised interest rates significantly in the late 1970s to combat high inflation.
Donald Trump - US President who enacted tax cuts and pursued protectionist trade policies.
V-shaped crises - Crises followed by a strong recovery, in contrast to more prolonged downturns.
2001 (film) - Referring to the Stanley Kubrick sci-fi film about AI. Perhaps discussing risks relating to artificial intelligence.
Weatherstone Dennis - Former CEO of UBS who led the bank in the 1990s.
UBS - Major Swiss bank that suffered substantial losses during the financial crisis despite being viewed as relatively safe previously.
Yield curve - Relationship between Treasury bond yields of different maturities, whose flattening/inversion can signal recession risks.
Janet Yellen - Former Federal Reserve Chair who oversaw continued policy normalization after the crisis.

That covers a high-level summary of some of the key terms provided relating to risks, economics, finance and policy. Let me know if any part needs more details or context.

#book-summary

The Illusion of Control - Jón Daníelsson