Overview:
The landscape of yield generation in crypto markets has been varied and innovative, with opportunities ranging from liquidity provisioning on Automated Market Makers (AMMs) to token farming. However, structured products, even with their significant potential for generating yield and nearly $1.5 trillion market share in traditional finance, have not experienced a similar rate of uptake. These products are extensively used by investors to either generate yield or hedge their existing portfolio while allowing a considerable amount of customization when working with a bank.
Cega is pioneering efforts to bridge this gap in the market, striving to make complex financial instruments, once the exclusive domain of high-net-worth individuals at top-tier investment banks, accessible to a broader audience. This report outlines our method for pricing such structured products, specifically focusing on the volatility skew—a critical input for pricing these products. We will detail our approach to bootstrapping the volatility surface for SOL, utilizing the liquid volatility market data of BTC and ETH as a foundation, due to the absence of SOL options on major exchanges like Deribit.
Introduction to Structured Products in Crypto:
In the realm of traditional finance, structured products are a staple, yet their utilization in the cryptocurrency sphere is notably limited. Cega is addressing this gap by introducing a diverse range of structured products tailored for the broader market. This report will concentrate on the complexities of valuing these financial instruments, with a special emphasis on the “Gotta Go Fast” product—a fixed coupon note (FCN) adapted for the crypto environment.
As the cryptocurrency market prepares for the next bull run, the timing of our analysis of this FCN is particularly unique. This product is ideal for speculative strategies on bullish trends, primarily aiming at yield accumulation. The FCN presents two key scenarios, hinging on an embedded knock-in feature that activates only if assets in the underlying basket depreciate by over 50%.
Scenario 1: Provided no single asset depreciates beyond 50%, investors are assured of receiving their complete principal and the stipulated yield upon maturity.
Scenario 2: Should any asset in the basket plummet by more than 50% the calculation of the investor’s return will pivot to the worst performing asset. In such instances, while the yield is still disbursed, the principal repayment is recalibrated based on the asset with the poorest performance.
For investors focused on yield, the decision to invest in this FCN fundamentally boils down to their assessment of whether any asset in the basket will lose over 50% of its value during the product’s maturity period, which, in the case of “Gotta Go Fast”, spans 27 days.
Apart from the inherent risks tied to DeFi projects, like smart-contract vulnerabilities and credit risk, the primary risk in this scenario is a market downturn where asset prices fall by 50% from the initiation of the trade. As outlined in Cega’s documentation, breaching the knock-in barrier can result in a principal loss, determined by the weakest performing asset at maturity. Nonetheless, the yield earned from the FCN partially mitigates these losses, rendering the FCN’s downside risk more favorable compared to direct ownership of the underlying assets.
Methodology for Bootstrapping the SOL Volatility Surface:
Pricing non-linear derivatives such as FCNs necessitates a deep understanding of the implied volatility linked with the underlying asset. It is noteworthy that unlike vanilla derivatives, which often have straightforward closed-form pricing models like the Black-Scholes formula, exotic derivatives demand more intricate methodologies. Typically, this involves comprehensive Monte Carlo simulations to forecast diverse price trajectories, rendering the pricing process substantially more complex.
For the “Gotta Go Fast” product, we have the advantage of accessible market data on the implied volatility of BTC and ETH from Deribit. However, we encounter a distinct challenge since SOL options which were previously listed on Deribit no longer trade on the platform, necessitating an alternative approach in our valuation framework. Our approach circumvents this obstacle by constructing a bespoke SOL volatility surface. The naïve method would employ a flat volatility model based on SOL’s realized volatility. This method is inherently flawed, as it neglects the nuanced dynamics of volatility skew, potentially leading to significant mispricings.
Instead, we propose a more nuanced method that adapts the existing BTC and ETH volatility surfaces. Preliminary analysis of the correlation between SOL and these major cryptocurrencies indicates a closer affinity between SOL and ETH, suggesting the latter as the preferable basis for our model.
To bootstrap the SOL volatility surface, we first normalize the ETH volatility surface against a scaled version of at-the-money (ATM) ETH IV, adjusting for the ratio between ETH’s IV and realized volatility to anchor our proxy to a reasonable baseline. We then approximate ATM SOL IV using SOL’s realized volatility. The resultant volatility surface for SOL is derived by scaling the normalized ETH surface with the inferred ATM SOL volatility. The graph herein illustrates a scaled SOL volatility surface as of October 19, 2023 19:00 UTC, with a constant maturity of 30 days to expiration (DTE).
Analysis of Pricing Results:
Comparative analysis of the “Gotta Go Fast” product against historical pricing data has unveiled significant trends. Since the summer of 2023, utilizing a flat volatility (ie: realized volatility) model for pricing the FCN seemed adequate, as it closely aligned with its historical trading prices. However, the approach shifted with the volatility surge in mid-August, where a bootstrapped volatility surface model demonstrated superior accuracy in reflecting historical prices.
This analysis benefits from examining volatility patterns over recent months, revealing two distinct volatility regimes: a phase of diminishing volatility and a period of relatively stable volatility ranges. Interestingly, these regimes correlate with the optimal periods for employing either the flat volatility or the scaled volatility surface models. For instance, during the period of decreasing volatility, the flat volatility model’s prices were in close agreement with market prices. Conversely, in the phase of choppy volatility, the bootstrapped volatility surface approach yielded more precise alignments with market prices.
A notable limitation of this analysis is the reliance on comparing model outputs with historical market prices. In more liquid markets, this comparison is valid. However, the limited liquidity in the “Gotta Go Fast” FCN pool, currently under $1 million USD, means that curve-fitting might not produce the most accurate results. Additionally, the constrained size of the dataset limits our capacity to draw definitive conclusions, underscoring the need for more comprehensive data to bolster confidence in our findings.
Despite these limitations, our analysis serves as a valuable heuristic. The flat and bootstrapped volatility models outline a broad spectrum of the FCN’s historical trading range. Although this range is wide, it offers critical insights that lay the groundwork for more precise pricing of these products in the future, especially as liquidity enhances in this area of the crypto market.
Conclusion:
As the crypto derivative market progresses towards maturity, we envision a landscape where structured products will benefit from enhanced liquidity and more efficient price discovery mechanisms. This evolution is contingent upon the entry of a new cohort of market participants: market-makers proficient in trading exotic derivatives, and sophisticated DeFi participants actively pursuing yield opportunities.
The integration of these skilled entities is crucial for fostering a robust market infrastructure. It will not only facilitate more fluid trading conditions but also contribute to the stabilization of this dynamic market segment. This influx of expertise is expected to bring a higher level of sophistication and understanding to the market, driving innovation and potentially leading to the development of more advanced financial instruments within the crypto sphere. Cega stands as a prime example of a team leading the way in financial engineering within the crypto domain. Many of their innovative products, showcasing their creativity and expertise, are accessible on their website.
In the interim, until this anticipated market state materializes, current market-makers and traders within the DeFi ecosystem must navigate this evolving landscape with prudent and well-informed estimates when it comes to pricing exotic products. Their approach to managing the inherent volatility of this nascent market will play a pivotal role in shaping its future trajectory and in laying the groundwork for a more mature, liquid, and transparent derivative trading environment in the crypto domain.