Understanding Systemic Risk in Financial Markets

Systemic risk in financial markets refers to the possibility that a large number of institutions or markets will fail simultaneously, causing a ripple effect throughout the entire economy.

This can happen when a single event or crisis triggers a chain reaction, leading to widespread failures. For example, the collapse of Lehman Brothers in 2008 triggered a global financial crisis.

Systemic risk is often linked to interconnectedness, where institutions have complex relationships with each other, making it difficult to identify and contain potential problems. This interconnectedness can also make it harder to regulate and monitor institutions.

The interconnectedness of financial institutions can lead to a "domino effect", where the failure of one institution causes a ripple effect, leading to failures in other institutions.

Systemic risk is a concept that refers to the potential for a company-level event to have a ripple effect on the entire economy. This can happen when a large company fails, causing a chain reaction of failures in other companies that were connected to it.

The federal government often intervenes in the economy to mitigate this risk, as seen in the case of companies considered "too big to fail." They believe that targeted regulations and actions can reduce or minimize the impact of a company-level event.

However, the government may choose not to intervene if the economy is experiencing a natural correction, which can be beneficial for the market in the long run. This is more often the exception than the rule, as it can sometimes destabilize the economy due to consumer sentiment.

Systemic risk can be caused by a variety of factors, but one key example is the interconnectedness of financial institutions. This was the case with Lehman Brothers and AIG, which were both major players in the financial system and had extensive connections to other institutions.

Their collapse caused a ripple effect throughout the economy, leading to a freeze in capital markets and making it difficult for businesses and consumers to get loans. The government's decision to bail out AIG, but not Lehman, highlights the complexity of systemic risk.

The Dodd-Frank Act of 2010 aimed to prevent another Great Recession by regulating key financial institutions and limiting systemic risk. However, its effectiveness has been debated, particularly in relation to its impact on small businesses.

The failure of Barings Bank in 1995 due to rogue trader Nick Leeson's actions is an example of a one-off crisis that did not have a knock-on effect on the whole system. In contrast, the collapse of Overend and Gurney in 1866 had a major impact on the British economy, leading to a severe recession.

Systemic risk can be triggered by a small event that is magnified by linkages within the financial system, known as amplification mechanisms. This was the case with the collapse of Overend and Gurney, which was sparked by a failed venture in high-risk lending.

Here are the four key elements of our research on systemic risk:

Systemic risk measurements have their limitations. Danielsson et al. express concerns that these measurements, such as SRISK and CoVaR, don't accurately reflect the actual systemic risk in the financial system.

Systemic financial crises happen relatively infrequently, occurring once every 43 years for a typical OECD country. This makes it challenging to model and measure systemic risk.

Classic valuation models are inadequate for valuing derivatives, debt, or equity under systemic risk. This is because they fail to account for the complex financial interconnectedness between firms.

Systemic risk measurements have their limitations. Danielsson et al. express concerns that these measurements are based on market outcomes that happen multiple times a year.

This discrepancy can lead to a misrepresentation of actual systemic risk in the financial system. Systemic financial crises happen once every 43 years for a typical OECD country.

The probability of systemic risk as measured does not correspond to the actual systemic risk.

Classic valuation models are often inadequate in the face of systemic risk, particularly when financial interconnectedness needs to be modeled. This is because such models can't handle closed valuation chains, where the share price of one firm influences the values of all other assets, including itself.

In a simple example with four firms A, B, C, and D, the share price of A can affect all other asset values. This creates a complex web of relationships that classic models struggle to capture.

Systemic risk can lead to non-trivial asset value equations, even with just two firms involved. For instance, if firm 1 owns 5% of firm 2's equity and 20% of its debt, and firm 2 owns 3% of firm 1's equity and 10% of its debt, the equilibrium price equations can become highly non-linear.

The Fischer (2014) model requires very strong conditions on derivatives to guarantee uniquely determined prices, but even then, it's unclear how weak conditions on derivatives can be chosen to apply risk-neutral pricing in financial networks with systemic risk.

The Merton (1974) model is a fundamental concept in finance that helps us understand how to price debt and equity in mature financial markets. It's based on the idea that the value of a company's assets is a key determinant of its debt and equity values.

In this model, two financial firms with limited liability own assets of value ai ≥ 0 at maturity T ≥ 0. They also owe a single amount of zero coupon debt di ≥ 0 due at time T. The model assumes that the business asset ai is not influenced by the firms in the considered financial system.

The classic single firm Merton model shows that equity and debt recovery value, si and ri, are uniquely and immediately determined by the value ai of the exogenous business assets. This means that if we know the value of the assets, we can easily calculate the debt and equity values.

Assuming the assets ai are defined by a Black-Scholes dynamic, risk-neutral no-arbitrage pricing of debt and equity is straightforward. However, the Merton model has its limitations, particularly when dealing with multiple firms with potentially correlated assets.

Financial interconnectedness is a complex phenomenon that can either enhance or destabilize financial stability. The Eisenberg and Noe (2001) model, for instance, showed that interconnectedness can lead to a higher risk of financial instability.

Acemoglu, Ozdaglar, and Tahbaz-Salehi (2015) developed a structural systemic risk model that incorporates both distress costs and debt claims with varying priorities. They found that up to a certain point, interconnectedness enhances financial stability, but once a critical threshold density of connectedness is exceeded, further increases in density propagate risk.

Network models have been proposed as a method for quantifying the impact of interconnectedness on systemic risk. This is a more useful systemic risk measure than a traditional "too big to fail" (TBTF) test.

The study by researchers used a dataset of observed network connections between supplier and customer firms to construct a network of the largest 1,000 firms by revenue. They found that a 1-percent shock to these firms resulted in significant losses to the network, with Amazon's loss being $1.42 billion and economy-wide losses reaching $77 billion.

Here's a list of some of the firms studied and their respective losses:

Diversification, a common risk-reduction strategy, can have ambiguous effects on systemic risk. In a certain range, financial interconnections can serve as a shock-absorber, but beyond a tipping point, they might serve as a shock-amplifier.

Systemic risk can be amplified when there are very close interlinkages between different elements of the financial system. These interlinkages form mechanisms that create vicious feedback loops.

Ignoring cross-holdings of debt or equity in credit risk modelling can lead to an under- or over-estimation of default probabilities. Modelling credit risk without considering these cross-holdings can be misleading.

Amplification mechanisms can turn relatively minor events into major crises.

Amplification mechanisms play a significant role in turning minor events into major crises in the financial system.

These mechanisms create vicious feedback loops that can amplify systemic risk. Vicious feedback loops are formed by very close interlinkages between different elements of the financial system.

Systemic risk becomes a problem when these interlinkages are present, leading to a snowball effect that can quickly get out of control.

Estimating default probabilities is a crucial aspect of risk management, and it's essential to get it right. Ignoring cross-holdings of debt or equity can lead to an underestimation of default probabilities.

Modelling credit risk without considering cross-holdings can be misleading. This can result in inaccurate assessments of a company's financial health.

Proper structural models of financial interconnectedness are necessary for accurate risk management. This is especially important in cases where companies have complex financial relationships.

Ignoring cross-holdings can also lead to an overestimation of default probabilities. This is a critical consideration for financial institutions and investors.

Accurate default probability estimation is vital for making informed investment decisions. It can also help prevent costly mistakes and losses.

Regulation can't be the sole protection against systemic risks, as seen in the banking sector, where regulations brought in to reduce risk simply shifted it to the insurance sector.

Regulations can create systemic risk by changing how private institutions behave and creating perverse incentives, or by impacting parts of the financial system not considered by that particular regulation.

The financial system needs to be considered as a whole when implementing financial regulations and crisis responses, to avoid creating new risks and mitigating the impact of systemic risk.

Regulation arbitrage, where commerce is transferred from a regulated sector to a less regulated or unregulated sector, can restore systemic risk and bring markets full circle.

Policymakers must act to mitigate the impact of systemic risk and prevent the next crisis, as the cost to society is too great to ignore.

Regulations themselves can be costly to society, and it's essential to consider the long-term effects of financial regulations and crisis responses.

Firms from diverse sectors, including petroleum, transportation, and insurance, represent meaningful sources of systemic risk.

The centrality of technology and telecommunications firms is especially important, given their high interconnectedness and the growing risks of cybersecurity.

Policymakers need to consider whether firms in these sectors have become too big and too interconnected, and if so, identify potential mitigations through exercises analogous to stress tests used for systemically important banks.

As we move forward, it's essential to consider the implications of our findings on the broader economy. Firms from diverse sectors, such as petroleum, transportation, and insurance, represent meaningful sources of systemic risk.

The centrality of technology and telecommunications firms is a pressing concern, given their interconnectedness and the potential for widespread disruption. The 2016 cyberattack on Dyn, a small firm providing a key domain name system, highlights the far-reaching consequences of such an event.

Policymakers must begin to address the systemic risk of firms across the broader economy, particularly in the tech and telecommunications sectors. This may involve identifying potential mitigations through exercises analogous to stress tests used for systemically important banks.

A renewed policy discussion about systemic risk is needed, especially in the tech and telecommunications sectors. Policymakers must consider whether firms in these sectors have become too big and too interconnected to fail.

Systemic risk is not limited to the financial sector, but can also arise from firms in diverse sectors like technology and telecommunications. In fact, the 2016 cyberattack on Dyn, a small firm providing a domain name system, caused significant damage to numerous large online firms.

The 2008 financial crisis highlighted the importance of interbank networks and systemic risk, but surprisingly little attention was paid to systemic risks in the broader economy. A closer look at the crisis showed that the emergency loans received by Chrysler, Ford, and General Motors were motivated by systemic risk.

Firms like Amazon and GoDaddy, with their widespread influence and essential services, pose significant systemic risk. A 1-percent shock to Amazon would result in estimated firm losses of $1.4 billion, but over $77 billion in total losses throughout the economy.

The following firms are among the 20 with the largest systemic impact following a shock:

The aggregate economic multiplier for the simulated 1-percent shock to Amazon is 54, indicating a significant ripple effect throughout the economy. This highlights the need for policymakers to consider the broader economic implications of systemic risk.