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Changes in the Risk Landscape of DeFi

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Post time 10-5-2024 21:03:53 | Show all posts |Read mode
Decentralized Finance (DeFi) is experiencing a new wave of development. The vibrancy of the new ecosystem and high yields resemble the famous DeFi summer of 2021. The diversity of innovative protocols makes it challenging for investors to keep up, while the impressive growth raises concerns about the accumulation of risks within the DeFi ecosystem.

You may have heard of doomsday analysis comparing this wave's most successful protocols (such as Ethena or Eigen Layer LRT) with risk management disasters (like Terra), but no credible similar evidence has been provided. In fact, the next generation of rapidly evolving DeFi protocols is more mature and has devoted significant effort to risk management. However, significant risks still exist.

The biggest risk in the current DeFi market is not based on systemic failures leading to Terra's collapse, but rather on three key factors: scale, complexity, and interconnectivity.

Protocols in this DeFi wave have grown considerably in just a few months, supporting more complex financial primitives, and their interconnectivity is incredible. The combination of complexity, scale, and interconnectivity has far exceeded the current DeFi market's risk model's capabilities. In simple terms, there are significant risk situations in the current DeFi market, and we lack a reliable risk model. Moreover, this gap seems to be widening rather than narrowing.

Four major risks of modern DeFi

From the start, risk has always been part of the DeFi narrative, and it's easy to discuss it in broad, general terms. This new era of DeFi brings novel innovations and significant growth rates. Therefore, the connotation of risk is different from before. In this DeFi era, analyzing risk from first principles highlights four fundamental factors: scale, speed, complexity, and interconnectivity.

To illustrate these factors, consider the difference in quantifying risk between a basic AMM with hundreds of millions in TVL and an AMM that uses rehypothecated assets, introduces its tokens and points systems, and grows to billions in TVL. The former's risk model can be addressed with basic statistics or machine learning approaches. The latter delves into more advanced branches of mathematics and economics, such as complexity or chaos theory, which have yet to be applied in DeFi.

Let's take a closer look at each factor.

1) Scale

The relationship between risk and scale in DeFi is straightforward. Modeling risk for smaller scales (e.g., a few billion) is significantly different from modeling risk for scales in the tens of billions. In larger scales, some risk situations occur that do not exist in smaller ones. This principle certainly applies to DeFi as a parallel financial system with many interconnected primitives.

Ethena, one of the most innovative projects in the current DeFi wave, has attracted billions in TVL in just a few months. Ethena's biggest challenge in the current market is adjusting its risk and insurance models to accommodate this scale in a scenario where interest rates are negative in the long term.

2) Speed

The relationship between risk and speed is the traditional friction between growing too big too fast. As a risk condition, speed accelerates scale. Protocols that go from millions to billions in TVL in just a few months may not have time to adjust their risk models to the new scale before unforeseen risk situations arise.

EigenLayer's rapid rise has triggered a movement in the entire rail sector, with some rails growing to billions in TVL in just a few months, but still lacking basic functionalities like withdrawals. The combination of speed and scale may exacerbate simple disengagement conditions, becoming influential risk factors in some protocols.

3) Complexity

The birth of the entire complexity theory field was to study systems that escape the laws of predictable models. Economic risk has been at the heart of complexity theory since its inception because the post-World War II world economy grew rapidly, and the requirements of risk models could not be met. Modeling risk in simple economic systems is straightforward.

In the new wave of DeFi, we have protocols like Pendle or Gearbox, which abstract quite complex primitives like yield derivatives and leverage. The risk models of these protocols are fundamentally more challenging than those of the previous generation of DeFi protocols.

4) Interconnectivity

From a risk perspective, a widely interconnected economic system can be a nightmare, as any situation can lead to numerous chain reactions. However, interconnectivity is a natural step in the evolution of economic systems.

The current DeFi ecosystem is more interconnected than its predecessors. We rehypothecate derivatives in EigenLayer, tokenize and trade in pools in Pendle, or use leverage in Gearbox. The result is that risk conditions in one protocol can quickly permeate into different key building blocks of the DeFi ecosystem, making it exceptionally challenging to construct risk models.

From technical risk to economic risk

For the past few years, hacking and exploits have been the main risk themes in DeFi, but this situation may be starting to change. The new generation of DeFi protocols is not only more innovative but also more robust from a technical security perspective. Audit firms are becoming smarter, and protocols are paying more attention to security.



As a continuously evolving financial system, DeFi's risks seem to be shifting from technical risks to economic risks. The sheer scale, rapid growth, high complexity, and deep interconnectedness are pushing DeFi into unpredictable territories from a risk perspective. With only a few companies dedicated to DeFi risk, the challenge now is how to catch up.
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Post time 12-5-2024 07:07:37 | Show all posts
Actually, it's worth taking a look at each method to understand them better.
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Post time 12-5-2024 12:30:41 | Show all posts
You can go check out various changes to understand them better.
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