Lotso Insurance Implementation | ChubGame Whitepaper

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Data

To effectively test and validate the functionality of our futures trading insurance based on actual market data, we leverage a range of reliable data sources. These sources are crucial for gathering the essential information required to apply and refine the methodology described in this thesis. This approach ensures our analysis is grounded in real-world data, providing a robust and accurate assessment of our futures trading insurance function.

Table 1. Daily price data for 12 crypto assets

Asset Name	Min	Standard deviation	Max
ETH/USDT	518.68	1004.94	4807.98
ADA/USDT	0.14	0.659	2.97
MATIC/USDT	0.02	0.592	2.88
SOL/USDT	1.20	60.39	258.44
DOT/USDT	4.29	12.5	53.82
AVA/USDT	0.48	1.297	6.27
ATOM/USDT	4.40	9.425	44.27
NEAR/USDT	0.87	4.183	20.18
EOS/USDT	0.82	1.915	14.52
EGLD/USDT	8.81	85.12	490.99
XTZ/USDT	0.72	1.77	8.71

In the context of futures trading, the "Asset Name" column lists 12 cryptocurrencies, all of which are paired with Tether (USDT), a stablecoin pegged to the US dollar. These cryptocurrencies include Ethereum (ETH), Cardano (ADA), Polygon (MATIC), Solana (SOL), Polkadot (DOT), Avalanche (AVA), Cosmos (ATOM), Near (NEAR), EOS (EOS), Elrond (EGLD), and Tezos (XTZ).

Focusing on futures trading, the "Minimum (Min)" and "Maximum (Max)" columns represent the lowest and highest daily futures contract prices recorded for each cryptocurrency over the observed period. This data is critical in futures trading as it helps in understanding the price range and potential fluctuations that traders might experience.

The "Standard Deviation" column reflects the price volatility of each cryptocurrency's futures contracts during the studied period. This metric is crucial in futures trading, as it provides insights into the risk level associated with each cryptocurrency. It measures the extent of price variation from the average futures contract price. For instance, a lower standard deviation, such as that of ETH/USDT, indicates less volatility, making it a potentially more stable choice for futures trading among the listed portfolio assets.

Table 2. Market Capitalization and Annual Yield.

Asset Name	Market Capitalization ($B)	Annual Yield (%)
ETH	230	6
ADA	14.09	4
MATIC	10.18	4.8
SOL	9.28	7.15
DOT	7.82	15
AVA	5.91	9
ATOM	3.29	21.39
NEAR	1.93	10.07
EOS	1.33	1.26
MVRSX	1.03	9
XTZ	1.01	5.6

The "Asset Name" column enumerates the ticker symbols of the 12 cryptocurrencies featured in our analysis. The "Market Capitalization" column reflects the total market value of each cryptocurrency as of 11.04.2023, expressed in billions of dollars. For example, Ethereum (ETH) holds the highest market capitalization at $230 billion, whereas Tezos (XTZ) records the lowest at $1.01 billion.

The "Annual Yield" column, in the context of perpetual futures trading, represents the annualized return calculated as a percentage, based on the average funding rate paid or received for holding perpetual futures contracts over a 30-day period. This yield is indicative of the cost or gain from holding these contracts, which can be a critical factor in futures trading strategies.

In our dataset, Cosmos (ATOM) shows the highest annualized yield at 21.39%, attributed to its funding rate in perpetual futures contracts, while EOS has the lowest at 1.26%.

The final column, "Portfolio Weight," indicates the allocation percentage of each cryptocurrency within our investment portfolio. This portfolio is significantly tilted towards ETH, constituting 50% of the total, and minimally towards Tezos (XTZ) at only 0.7%.

Perpetual Futures Funding Rate Data: Understanding the funding rate for perpetual futures contracts is crucial in our analysis. The funding rate affects the cost of maintaining a position in these contracts and can significantly influence the overall profitability of a futures trading strategy. This rate, which can be positive or negative depending on market conditions, essentially represents the periodic payments made between buyers and sellers of the contracts and is a key component in determining the Annual Yield for each cryptocurrency in our perpetual futures portfolio.

Table 3. Annualized perpetual futures funding rate, %

Name	Min	Mean	Max
Perps annualized funding rate, %	-333.91	-6.11	10.95

Table 3 presents data on the costs involved in maintaining perpetual futures contracts, focusing on their role in futures trading strategies.

By meticulously gathering and organizing data from diverse sources, we ensure that the analysis in this thesis is rooted in accurate and extensive information, tailored specifically to futures trading in cryptocurrencies. This data-centric strategy enables us to construct a solid methodology, offering valuable insights into the optimal futures trading strategy for managing cryptocurrency portfolios within the digital asset ecosystem. This approach is crucial for understanding the intricacies of futures trading and for making informed decisions that align with market dynamics and investor objectives.

Estimation Result

The estimation results are systematically presented in line with the various stages of our methodology, offering insights into the crucial elements of the optimal futures trading strategy for a cryptocurrency portfolio. As an illustrative case, we will present the results for a specific futures trading period, 01.03.2024 to 01.04.2024. This will demonstrate the application of our methodology and its consequential impact on portfolio risk management.

The initial phase following data collection involves calculating the portfolio weights for futures trading.

Table 4. Portfolio Weights

Asset Name	Portfolio Weight (%)
ETH	50
ADA	9.7
MATIC	7.01
SOL	6.39
DOT	5.38
AVA	4.07
ATOM	2.27
NEAR	1.33
EOS	0.92
MVRSX	0.71
XTZ	0.7

The process initiates with the computation of portfolio weights, which are determined based on the market capitalizations of individual cryptocurrencies. This method ensures that cryptocurrencies with larger market sizes have a more substantial influence on the portfolio.

To accomplish this, we first aggregate the market capitalizations of all the cryptocurrencies under consideration. We then calculate the proportionate weight of each cryptocurrency by dividing its individual market capitalization by the total market cap. The resulting weights, each ranging between 0 and 1 and collectively summing up to 1, depict the fraction of the total portfolio investment allocated to each cryptocurrency. In our portfolio, Ethereum (ETH) carries the largest weight at 50%, reflecting its significance as the primary asset for futures trading, while Tezos (XTZ) holds the smallest weight at 0.7%.

Subsequently, we simulate the evolution of the portfolio value over the upcoming 30 days. This stage involves two critical steps: estimating the necessary parameters and simulating the progression of the portfolio's value. This simulation is crucial in futures trading, as it helps in forecasting potential changes in the portfolio's value based on the anticipated market movements of the cryptocurrencies involved.

In the context of futures trading, a key parameter for our simulation is the correlation matrix, which delineates the relationships between the different cryptocurrencies in the portfolio. This matrix is instrumental in understanding how the movements of one cryptocurrency might correlate with others in the portfolio, a factor that is particularly crucial in futures trading where the interplay of multiple assets can significantly impact the overall portfolio performance.

The correlation matrix is utilized to accurately capture the interactions among the various assets within the portfolio. It forms a foundation for the subsequent step: simulating the portfolio value over the forthcoming 30 days. This simulation employs a Monte Carlo methodology, based on a multivariate Jump-Diffusion model. The model is adept at representing both gradual shifts and abrupt fluctuations in cryptocurrency prices, reflecting the dynamic nature of the cryptocurrency market.

The model incorporates the correlations between assets, allowing for the generation of probable future price trajectories that take into account both diffusion and jump aspects, as inferred from historical price data. By simulating a range of possible future market scenarios, this approach provides a comprehensive view of potential portfolio value changes, thereby informing more strategic decision-making in futures trading.

Figure 4. Portfolio Value distribution after 30 days

The outcome of our simulation is a range of potential portfolio returns over a 30-day period, providing insights into the possible future performance of the portfolio in the context of futures trading.

In this scenario, the most likely projected portfolio return is +33%.

Following this, the next critical step is to determine the futures exposure of the portfolio. This value is crucial as it indicates how the portfolio value is impacted by price fluctuations of Ethereum (ETH), which, in this context, is the primary asset for futures trading.

To gauge this sensitivity, we conduct a regression analysis using the most probable portfolio returns derived from the simulated distribution as the dependent variable, and the individual returns of ETH as the independent variable.

The regression's coefficient for ETH reveals the portfolio's elasticity in relation to ETH price movements. This elasticity metric is subsequently utilized to compute the futures exposure. Essentially, it measures the expected variation in the portfolio value corresponding to a 1% change in the ETH price, a vital calculation for strategizing in futures trading and managing portfolio risk effectively.

Table 5. Portfolio price elasticity estimation results

Coefficients	Estimate	Std. Error	t value
Intercept	-0.00103	0.005054	-0.205
log_eth_returns	1.352481	0.215054	6.289

In the context of futures trading, the coefficient from the log_eth_returns regression analysis represents the sensitivity of the portfolio returns to variations in the returns of Ethereum (ETH). This is essentially the portfolio’s exposure to ETH futures. This coefficient is statistically significant, indicating a reliable measure. It suggests that for every 1% increase in ETH returns, there is an expected 1.35% increase in the portfolio's value.

The subsequent step involves determining the pricing of futures options on ETH as one method of managing the portfolio's exposure to ETH price movements. For this, we use the Jump-Diffusion model, tailored to futures options pricing. The input for this model is the most probable price path for ETH over the next 30 days, as deduced from the Monte Carlo simulation.

This figure represents the estimated price for the futures option, equating to the cost of managing the portfolio’s exposure to ETH through these financial instruments.

Next, we turn our attention to another strategy: the utilization of perpetual futures contracts. The cost of holding a position in perpetual futures is influenced by the funding rate. To forecast this rate, we develop a linear regression model based on the historical correlation between daily price changes in ETH and the funding rate. This model is instrumental in predicting future funding rates, thereby aiding in the estimation of the costs associated with perpetual futures contracts as a strategy for managing portfolio exposure.

Table 6. Funding rate regression estimation results

Coefficients	Estimate	Std. Error	Pr(>\|t\|)
Intercept	-5.505e-05	3.246e-05	0.0929
dailyEthReturns	6.115e-04	5.802e-04	0.2944

The Intercept in our analysis signifies the expected funding rate for perpetual futures when the daily return on ETH is zero. The associated p-value for the intercept is 0.0929, which, being just under 0.1, suggests borderline significance. Typically, a p-value less than 0.05 is preferred to deem a coefficient statistically significant.

The coefficient for dailyEthReturns, at 6.115e-04, indicates the variation in the funding rate corresponding to a unit change in daily ETH returns. With a p-value of 0.0944, this result also treads the borderline of significance, implying that while there might be an increase in the funding rate with rising daily returns, the evidence is not robust enough to conclusively affirm this relationship. This finding warrants further investigation and theoretical exploration.

Based on the forecasted funding rate, we then calculate the cost associated with employing perpetual futures as a risk management strategy.

Table 7. Cost of employing each futures strategy for a specified trading period:

Strategy	Cost of Hedging
Futures Options	7.4%
Perpetual Futures	1.33%

In this scenario, the cost of employing futures options (akin to put options in traditional markets) is 7.4% of the portfolio value, while using perpetual futures is substantially lower at 1.33%.

Futures options, which act as a safeguard against a decline in ETH value, incur an upfront cost (premium) of 7.4% of the initial portfolio value. However, the capital requirement for futures options is relatively lower than that for perpetual futures, as only the premium payment is required without the need for additional collateral.

Conversely, employing perpetual futures demands full collateralization, necessitating capital equal to the portfolio value. While the direct cost of this strategy is lower at 1.33%, the requirement to lock in substantial capital can limit other investment opportunities, such as reinvesting in the portfolio.

Furthermore, considering our portfolio where assets are staked to earn yield, the opportunity cost of using perpetual futures is accentuated. The capital allocated to these futures could otherwise be reinvested for staking, potentially yielding higher returns.

Therefore, although perpetual futures appear less costly in terms of direct expenses, the high capital commitment and the lost opportunity for staking rewards may render futures options a more favorable strategy under certain circumstances. The decision between these two methods should weigh not just the immediate costs but also the capital requirements, opportunity costs, risk tolerance, market conditions, and potential staking rewards.