Vladimir Kovšca, Zrinka Lacković Vincek, Suzana Keglević Kozjak
Prior research on cryptoasset valuation has largely adapted discounted cash flow (DCF) models by treating staking rewards and transaction fees as productive cash flows, while insufficiently accounting for monetary characteristics and strategic flexibility inherent to decentralized platforms. This study investigates whether such cashflow based approaches systematically undervalue Ethereum. The central hypothesis is that Ethereum’s intrinsic value cannot be adequately explained by DCF valuation alone, and that monetary premium and technological optionality constitute economically significant components of value. To examine this hypothesis, a multi-layer valuation framework is applied using network and market data from 2022–2025, combining a DCF model, a monetary premium benchmarked against gold based on relative scarcity and adoption, and a real option uplift reflecting future expansion potential. Monte Carlo simulation is employed to test the robustness of the results. The findings indicate that while DCF-based valuations remain relatively stable, total intrinsic value is highly sensitive to assumptions regarding monetary adoption and strategic optionality. These results underline the importance of layered valuation frameworks for decentralized platforms.
Este artigo apresenta um esboço estruturado sobre “Eficiência de Capital em AMMs de Faixa Concentrada (Concentrated Liquidity).”. O objetivo é analisar os fundamentos técnicos e econômicos da liquidez concentrada, tomando Uniswap v3 e outros <i>concentrated liquidity market makers</i> (CLMMs) como referência, e discutir suas implicações para o ecossistema Web3. A metodologia baseia‑se em revisão bibliográfica e análise de casos práticos, com foco na comparação entre AMMs de faixa infinita (como Uniswap v2) e AMMs com faixas de preço configuráveis pelos provedores de liquidez. Documentação oficial e materiais de lançamento do Uniswap v3 destacam que a ideia definidora do protocolo é permitir que LPs aloquem liquidez em faixas de preço customizadas, alcançando até cerca de 4.000× mais eficiência de capital em relação ao modelo v2 quando a liquidez é concentrada em uma faixa de 0,10%, com capacidade técnica de ranges tão granulares quanto 0,02%, o que elevaria a eficiência para até 20.000×, ainda que com custos maiores de gas por swap. Análises de segurança e guias educacionais sintetizam que, ao invés de espalhar capital em toda a curva de preços teórica, a liquidez concentrada permite que o capital atue apenas nos intervalos em que as negociações são mais prováveis, aproximando o comportamento do AMM a um <i>order book</i> tradicional e oferecendo melhor <i>price impact</i> com o mesmo capital. Ao mesmo tempo, estudos acadêmicos sobre CLMMs mostram que, embora a eficiência de capital aumente, também se intensificam riscos como <i>impermanent loss</i> (uma vez que a posição pode sair da faixa e ficar 100% em um único ativo) e estratégias adversariais como <i>just‑in‑time liquidity</i>, nas quais LPs estratégicos entram e saem em micro‑janelas para capturar taxas de forma desproporcional. Pesquisas mais amplas em design de AMMs sugerem ainda que abordagens multi‑token e mecanismos de compartilhamento de reservas podem aumentar a liquidez efetivamente ativa em CLMMs em 2,6–5,9×, mitigando efeitos de fragmentação de faixa. Conclui‑se que AMMs de faixa concentrada são um avanço significativo em eficiência de capital e qualidade de execução, mas exigem modelos de risco mais sofisticados, tanto para LPs quanto para protocolos, em comparação com AMMs de faixa infinita.<br>
Multi-chain deployment has become a mainstream strategy for U.S.-based DAOs, yet treasury management faces three core bottlenecks: cross-chain liquidity fragmentation, inadequate compliance with U.S. regulations (including OFAC sanctions screening and SEC transparency requirements), and inefficient revenue distribution. Leveraging the incubation practices of over 12 U.S. DAOs (via daos.world) and expertise in multi-chain smart contract development, this study proposes a three-dimensional risk and compliance optimization framework (cross-chain risk hedging + real-time regulatory screening + hierarchical revenue distribution). Empirical testing on 8 U.S. DAOs (operating on Base/Ethereum/Solana, covering AI-focused, meme coin-focused, and investment-focused types) over a 6-month period (September 2025 - February 2026) demonstrates that the framework reduces cross-chain compliance risks by 82.3% (OFAC violation rate drops from 18.0% to 3.2%), increases the annualized treasury return rate by 17.6% (from 4.2% to 5.04%), lowers cross-chain transaction costs by 28.5% (average Gas fee decreases from $12.8 to $9.1), and shortens liquidity adjustment response time from 48 hours to 6 hours. Integrating U.S. regulatory requirements with cross-chain technical logic, this research addresses the theoretical gap in multi-chain DAO treasury management, provides a replicable paradigm for U.S. DAOs to balance compliance, security, and profitability, aligns with the standardization strategy of the U.S. Web3 ecosystem, and is expected to unlock $15-20 billion in potential investment value.
The tokenization of Real-World Assets (RWAs) via Decentralized Finance (DeFi) protocols promises fractional ownership and continuous liquidity for traditionally illiquid asset classes, yet the market microstructure governing on-chain RWA pricing efficiency and pool liquidity remains under-theorised and empirically unresolved. This paper develops a quantitative market-microstructure framework to evaluate pricing errors, slippage dynamics and liquidity-pool efficiency in RWA tokenization relative to traditional Real Estate Investment Trusts (REITs). We combine an oracle-adjusted Constant Product Automated Market Maker (CPAMM) with a GARCH(1,1)-X specification and calibrate the model to published on-chain statistics from RealT, Ondo Finance and Centrifuge, benchmarked against the Vanguard Real Estate ETF (VNQ). Simulation-based evidence indicates that (i) RWA tokenization lowers the implied cost of capital by 115-140 basis points; (ii) asset-level idiosyncratic volatility induces nonlinear slippage in constant-product pools during stress regimes; and (iii) oracle latency dominates the persistence of pricing deviation (PEₜ). We propose an oracle-conditioned hybrid liquidity architecture that significantly mitigates pricing deviation and enhances market efficiency.
Enabled by blockchain progress, ICOs emerged as an alternative to traditional equity financing, with global investor reach, decentralized governance, and reduced transaction costs. However, the performance of ICOs under competitive product market conditions remains underexplored. This paper develops a game theoretic Cournot competition model to compare firms' financing and operational strategies under two benchmark scenarios, depending on whether product value is insensitive or sensitive to managerial effort. The analysis examines how cost structure, market volatility, product substitutability, and managerial risk attitude jointly affect financing choices and production decisions. To ensure research rigor, we further analyze a generalized model and conduct robustness checks by varying key parameters, confirming the stability of the equilibrium outcomes beyond the benchmark settings. The results show that optimal financing choices depend critically on market structure. Greater product substitutability intensifies competition and widens the utility gap between financing modes, strengthening the dominance of the more suitable strategy. Equity financing is more favorable for risk averse firms or those operating in low innovation, high substitutability industries such as manufacturing and utilities, due to its risk sharing and operational flexibility. In contrast, ICOs are better suited for innovation driven ventures such as DeFi and NFTs, which benefit from incentive alignment and reduced equity dilution. This study provides managerial insights into the strategic selection of financing mechanisms under competition and contributes to a deeper understanding of token financing in modern capital markets.
This paper challenges the conventional divide between productive and non-productive assets by proposing that scarcity, rather than internal cash flow generation, is the fundamental source of value across all asset classes. Interim payments such as dividends, rents, or coupons, represent one modality of monetizing scarcity, but terminal resale and other mechanisms serve equivalent roles. We develop a valuation framework in which scarcity is modeled as a latent, time-varying state variable shaped by economic pressures on demand and supply. A class of monetization functions, characterized by monotonicity and curvature, maps scarcity states into observable or forecast cash flows. This formulation allows discounted cash flow (DCF) logic to be reinterpreted as a general pricing mechanism for intertemporal scarcity. The framework accommodates both terminal-value assets, such as Bitcoin or gold, and income-generating assets, such as equities or bonds. We formally demonstrate the equivalence between terminal and periodic payoff structures and introduce a classification of assets according to their scarcity mechanism, whether physical, contractual, algorithmic, or reputational. By embedding scarcity at the core of valuation, this approach dissolves artificial distinctions in asset classification and establishes a unified foundation for pricing financial claims across diverse contexts.
The rapid expansion of blockchain-based applications and decentralized fi nance (DeFi) has led to a substantial increase in demand for block space, resulting in pronounced volatility in transaction costs, commonly referred to as gas fees. Such volatility introduces significant budgetary and operational risks for a wide range of market participants, including protocol developers, arbitrageurs, mar ket makers, and institutional users. In this paper, we develop a comprehensive theoretical and computational framework for the valuation of derivatives written on Ethereum gas fees. We focus in particular on swaps based on average gas costs and European-style options on gas fee levels. Our approach yields tractable (semi-)analytical pricing formulas for these instruments, allowing for explicit de composition into hedgeable and non-hedgeable risk components. The proposed framework not only provides practical tools for managing transaction cost risk but also lays the foundation for the development of a new class of derivatives markets centered on blockchain base fees.
The transition of Micro, Small, and Medium Enterprises (MSMEs) toward decentralized rooftop solar is critical for sustainable industrial growth in emerging economies, yet commercial adoption remains sluggish despite grid parity. This study empirically investigates MSME preferences for solar financing architectures using a Choice-Based Conjoint (CBC) experiment grounded in Random Utility Theory. Primary data were collected from 100 MSMEs in India’s National Capital Region, generating 1,000 discrete choice observations under strictly controlled load conditions (50–60 kW). A Conditional Logit Model was employed to estimate part-worth utilities across capital structures, tariff mechanisms, and performance risk allocation. Contradicting standard market assumptions, the aggregate choices revealed a 77.6% rejection rate of standard solar offerings. The econometric results demonstrate severe utility penalties for upfront capital and fixed repayment obligations . Crucially, the requirement for firm-assumed maintenance risk generated perfect separation , acting as an absolute barrier to adoption. However, market simulations isolating an optimized financing package—combining zero-upfront OPEX, pay-per-unit tariffs, and developer-assumed risk—resulted in the adoption rate increasing to 76.3%. The findings indicate that the current stagnation in commercial solar diffusion is driven primarily by suboptimal risk allocation and product mismatch, rather than a lack of underlying economic viability. To accelerate deployment, policymakers and financial institutions must pivot from capital-subsidy models toward standardizing and de-risking third-party "Energy-as-a-Service" frameworks.
This paper proposes a hybrid IoT-blockchain architecture designed to ensure the security and value enhancement of flue gas desulfurization (FGD) gypsum throughout its entire lifecycle. At the edge, sensor data is encrypted using AES-256, with RSA-2048 handling key exchange, achieving a hybrid encryption overhead of 1.84 milliseconds per kilobyte. A permissioned Proof-of-Authority consensus mechanism delivers$\text{1, 7 0 0}$transactions per second with a confirmation time of just 0.59 seconds. An immutable ledger records purity, moisture, volume, and origin data; smart contracts automatically execute compliance checks and quality balance reconciliations. During a$\text{1 2}$-month field deployment at a 1.2-million-ton coal-fired power plant, the system reduced unauthorized access attempts by 94.7%, lowered transportation quality disputes by 80%, and improved downstream price stability by 18%. Scalable to 145,000 daily records, the system supports sub-second queries for$\text{8 5 {\%}}$of calls and achieves post-quantum security through zero-knowledge proof integration. This framework transforms industrial byproduct tracking into a verifiable, real-time asset valuation tool.
This study asks whether Ethereum’s proof-of-stake (PoS) incentives not only make economic sense on paper but also feel attractive to real validators who may be loss-averse and sensitive to risk. We take a canonical Eth2 slot-level model of rewards, penalties, costs, and proposer-conditional maximal extractable value (MEV) and overlay a prospect-theoretic valuation that captures reference dependence, loss aversion, diminishing sensitivity, and probability weighting. This Prospect-Theoretic Incentive Mechanism (PT-IM) separates the “money edge” (expected accounting return) from the “felt edge” (behavioral value) by mapping monetary outcomes through a prospect value function and comparing the two across parameter ranges. The mechanism is parametric and modular, allowing different MEV, cost, and penalty profiles to plug in without altering the base PoS model. Using stylized numerical examples, we identify regions where cooperation that pays in expectation can remain unattractive under plausible loss-averse preferences, especially when penalties are salient or MEV is volatile. We discuss how these distortions may affect validator participation, economic security, and the tuning of rewards and penalties in Ethereum’s PoS. Integrating behavioral valuation into crypto-economic design thus provides a practical diagnostic for adjusting protocol parameters when economics and perception diverge.
The aim of this thesis is to examine the pricing and efficiency of Bitcoin options. It reviews theories of market efficiency and considers how effectively these frameworks apply to cryptocurrency markets. The thesis examines multiple option pricing models by comparing their performance for pricing Bitcoin options. Bitcoin’s high volatility and the relatively young age of its market development highlight the need to analyze how these characteristics influence both option pricing and overall market efficiency. In addition, the thesis examines the characteristics of Bitcoin options. The study provides guidelines for future research and market development, helping to build trust and support the integration of cryptocurrency derivatives into the broader financial system. Tämän opinnäytetyön tavoitteena on tarkastella Bitcoin-optioiden hinnoittelua ja markkinoiden tehokkuutta. Työssä käydään läpi markkinatehokkuuden teorioita ja arvioidaan, kuinka hyvin nämä viitekehykset soveltuvat kryptovaluuttamarkkinoihin. Opinnäytetyössä tarkastellaan useita optioiden hinnoittelumalleja vertailemalla niiden toimi- vuutta Bitcoin-optioiden hinnoittelussa. Bitcoinin korkea volatiliteetti ja sen markkinoiden suhteellisen varhaisessa kehitysvaiheessa oleva tila korostavat tarvetta analysoida, miten nämä ominaisuudet vaikuttavat sekä optioiden hinnoitteluun että markkinoiden yleiseen tehokkuuteen. Lisäksi työssä tarkastellaan Bitcoin-optioiden erityispiirteitä. Tutkimus tarjoaa suuntaviivoja tu- levalle tutkimukselle ja markkinoiden kehittämiselle, ja sen tavoitteena on lisätä luottamusta sekä tukea kryptovaluuttajohdannaisten integroitumista laajempaan finanssijärjestelmään.
Bitcoin treasury companies have taken stock markets by storm amassing billions of dollars worth of tokens in hundreds of entities. The paper discusses, how leverage - whether created through corporate debt or investors using stock as loan collateral - fuels this trend. The extension of the binary-choice Kelly criterion to incorporate uncertainty in the form of the Kullback-Leibler divergence or more generally Bregman divergence is also briefly discussed.
Hardhik Mohanty, Giovanni Zaarour, Bhaskar Krishnamachari
Everlasting options, a relatively new class of perpetual financial derivatives, have emerged to tackle the challenges of rolling contracts and liquidity fragmentation in decentralized finance markets. This paper offers an in-depth analysis of markets for everlasting options, modeled using a dynamic proactive market maker. We examine the behavior of funding fees and transaction costs across varying liquidity conditions. Using simulations and modeling, we demonstrate that liquidity providers can aim to achieve a net positive PnL by employing effective hedging strategies, even in challenging environments characterized by low liquidity and high transaction costs. Additionally, we provide insights into the incentives that drive liquidity providers to support the growth of everlasting option markets and highlight the significant benefits these instruments offer to traders as a reliable and efficient financial tool.
This paper introduces a novel multi-objective optimization framework for sustainable portfolio rebalancing under uncertainty. The model simultaneously targets return maximization, downside risk control, and liquidity preservation, addressing the complex trade-offs faced by investors in volatile markets. Unlike traditional static approaches, the framework allows for dynamic asset reallocation and explicitly incorporates nonlinear transaction costs, offering a more realistic representation of trading frictions. Key financial parameters—including expected returns, volatility, and liquidity—are modeled using interval arithmetic, enabling a flexible, distribution-free depiction of uncertainty. Risk is measured through semi-absolute deviation, providing a more intuitive and robust assessment of downside exposure compared to classical variance. A core innovation lies in the behavioral modeling of investor preferences, operationalized through three strategic configurations, pessimistic, optimistic, and mixed, implemented via convex combinations of interval bounds. The framework is empirically validated using a diversified cryptocurrency portfolio consisting of Bitcoin, Ethereum, Solana, and Binance Coin, observed over a six-month period. The simulation results confirm the model’s adaptability to shifting market conditions and investor sentiment, consistently generating stable and diversified allocations. Beyond its technical rigor, the proposed framework aligns with sustainability principles by enhancing portfolio resilience, minimizing systemic concentration risks, and supporting long-term decision-making in uncertain financial environments. Its integrated design makes it particularly suitable for modern asset management contexts that require flexibility, robustness, and alignment with responsible investment practices.
This paper presents a robust multi-period portfolio optimization framework that integrates interval analysis, entropy-based diversification, and downside risk control. In contrast to classical models relying on precise probabilistic assumptions, our approach captures uncertainty through interval-valued parameters for asset returns, risk, and liquidity—particularly suitable for volatile markets such as cryptocurrencies. The model seeks to maximize terminal portfolio wealth over a finite investment horizon while ensuring compliance with return, risk, liquidity, and diversification constraints at each rebalancing stage. Risk is modeled using semi-absolute deviation, which better reflects investor sensitivity to downside outcomes than variance-based measures, and diversification is promoted through Shannon entropy to prevent excessive concentration. A nonlinear multi-objective formulation ensures computational tractability while preserving decision realism. To illustrate the practical applicability of the proposed framework, a simulated case study is conducted on four major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB). The model evaluates three strategic profiles based on investor risk attitude: pessimistic (lower return bounds and upper risk bounds), optimistic (upper return bounds and lower risk bounds), and mixed (average values). The resulting final terminal wealth intervals are [1085.32, 1163.77] for the pessimistic strategy, [1123.89, 1245.16] for the mixed strategy, and [1167.42, 1323.55] for the optimistic strategy. These results demonstrate the model’s adaptability to different investor preferences and its empirical relevance in managing uncertainty under real-world volatility conditions.
Options are fundamental to blockchain-based financial services, offering essential tools for risk management and price speculation, which enhance liquidity, flexibility, and market efficiency in decentralized finance (DeFi). Despite the growing interest in options for blockchain-resident assets, such as cryptocurrencies, current option mechanisms face significant challenges, including a high reliance on trusted third parties, limited asset support, high trading delays, and the requirement for option holders to provide upfront collateral. In this paper, we present a protocol that addresses the aforementioned issues. Our protocol is the first to eliminate the need for holders to post collateral when establishing options in trustless service environments (i.e. without a cross-chain bridge), which is achieved by introducing a guarantee from the option writer. Its universality allows for cross-chain options involving nearly \textit{any} assets on \textit{any} two different blockchains, provided the chains' programming languages can enforce and execute the necessary contract logic. Another key innovation is reducing option position transfer latency, which uses Double-Authentication-Preventing Signatures (DAPS). Our evaluation demonstrates that the proposed scheme reduces option transfer latency to less than half of that in existing methods. Rigorous security analysis proves that our protocol achieves secure option trading, even when facing adversarial behaviors.
This paper aims to leverage Bayesian nonlinear expectations to construct Bayesian lower and upper estimates for prices of Ether options, that is, options written on Ethereum, with conditional heteroscedasticity and model uncertainty. Specifically, a discrete-time generalized conditional autoregressive heteroscedastic (GARCH) model is used to incorporate conditional heteroscedasticity in the logarithmic returns of Ethereum, and Bayesian nonlinear expectations are adopted to introduce model uncertainty, or ambiguity, about the conditional mean and volatility of the logarithmic returns of Ethereum. Extended Girsanov’s principle is employed to change probability measures for introducing a family of alternative GARCH models and their risk-neutral counterparts. The Bayesian credible intervals for “uncertain” drift and volatility parameters obtained from conjugate priors and residuals obtained from the estimated GARCH model are used to construct Bayesian superlinear and sublinear expectations giving the Bayesian lower and upper estimates for the price of an Ether option, respectively. Empirical and simulation studies are provided using real data on Ethereum in AUD. Comparisons with a model incorporating conditional heteroscedasticity only and a model capturing ambiguity only are presented.
not-yet-known not-yet-known not-yet-known unknown This paper investigates the current landscape of option trading platforms for cryptocurrencies, encompassing both centralized and decentralized exchanges. Option contracts in cryptocurrency markets offer functionalities akin to traditional markets, providing investors with tools to mitigate risks, particularly those arising from price volatility, while also allowing them to capitalize on future volatility trends. The paper discusses these applications of option contracts in the context of decentralized finance (DeFi), emphasizing their utility in managing market uncertainties. Despite a recent surge in the trading volume of option contracts on cryptocurrencies, decentralized platforms account for less than 1% of this total volume. Hence, this paper takes a closer look by examining the design choices of these platforms to understand the challenges hindering their growth and adoption. It identifies technical, financial, and adoption-related challenges that decentralized exchanges face and provides commentary on existing platform responses. Subsequently, it introduces a zero-loss liquidity provision strategy on altcoins that utilizes options with automated market makers. These opportunities result in a positive return with no significant risk. It then investigates opportunities in the past using historical on-chain data to emphasize the number of risk-free opportunities that DeFi is missing due to the lack of a functional options exchange on arbitrary ERC20 token pairs on Ethereum. The experiments show 1015 profitable instances in the past three years on 14 token pairs.
Srisht Fateh Singh, Panagiotis Michalopoulos, Andreas Veneris
This paper investigates the current state of option trading platforms for cryptocurrencies, encompassing both centralized and decentralized exchanges. Option contracts in cryptocurrency markets offer functionalities akin to traditional markets, providing investors with tools to mitigate risks, particularly those arising from price volatility. The paper discusses these applications of option contracts in the context of decentralized finance, emphasizing their utility in managing market uncertainties. Despite a recent surge in the trading volume of option contracts on cryptocurrencies, decentralized platforms account for less than $1 \%$ of this total volume. Hence, this paper takes a closer look by examining the design choices of these platforms to understand the challenges hindering their growth and adoption. It identifies technical, financial, and adoption-related challenges faced by decentralized exchanges. Subsequently, the paper provides commentary on existing platform responses.