Smart Contract Adoption in Derivative Markets under Bounded Risk: An Optimization Approach

Paper: arXiv 2510.07006

Authors: Jinho Cha, Long Pham, Thi Le Hoa Vo, Jaeyoung Cho, Jaejin Lee

Abstract

This study develops and analyzes an optimization model of smart contract adoption under bounded risk, linking structural theory with simulation and real-world validation. We examine how adoption intensity alpha is structurally pinned at a boundary solution, invariant to variance and heterogeneity, while profitability and service outcomes are variance-fragile, eroding under volatility and heavy-tailed demand. A sharp threshold in the fixed cost parameter A3 triggers discontinuous adoption collapse (H1), variance shocks reduce profits monotonically but not adoption (H2), and additional results on readiness heterogeneity (H3), profit-service co-benefits (H4), and distributional robustness (H5) confirm the duality between stable adoption and fragile payoffs. External validity checks further establish convergence of sample average approximation at the canonical O(1/sqrt(N)) rate (H6). Empirical validation using S&P 500 returns and the MovieLens100K dataset corroborates the theoretical structure: bounded and heavy-tailed distributions fit better than Gaussian models, and profits diverge across volatility regimes even as adoption remains stable. Taken together, the results demonstrate that adoption choices are robust to uncertainty, but their financial consequences are highly fragile. For operations and finance, this duality underscores the need for risk-adjusted performance evaluation, option-theoretic modeling, and distributional stress testing in strategic investment and supply chain design.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a structured optimization model combining lognormal risk specifications with contract theory to evaluate smart contract adoption. While it relies partially on synthetic simulation and proxy datasets (S&P 500 and MovieLens) for empirical validation, its analytical formulation of boundary conditions and threshold effects is mathematically sound and well-articulated.

Research Flowchart

  flowchart TD
    A["Research Goal: Analyze Smart Contract Adoption & Financial Outcomes Under Risk"] --> B["Data Inputs: S&P 500 Returns & MovieLens100K Dataset"]
    B --> C["Optimization Methodology: Bounded Risk Model & Sample Average Approximation"]
    C --> D["Computational Processes: Variance Stress Testing & Empirical Heavy-Tail Fitting"]
    D --> E["Finding 1: Stable Adoption Boundary Solutions Invariant to Risk Shock"]
    D --> F["Finding 2: Fragile Payoffs with Discontinuous Collapse at Fixed Cost Threshold A3"]
    E & F --> G["Core Outcome: Stable Adoption vs. Fragile Payoff Duality"]

Cited References (45)

1. Contract Theory (2005)
2. Supply chain coordination with contracts (2003) — link
3. Some simple economics of the blockchain (2016) — link
4. Blockchain disruption and smart contracts (2019) — link
5. Decentralized finance: On blockchain- and smart contract-based financial markets (2021) — link
6. Blockchain Revolution: How the Technology Behind Bitcoin and Other Cryptocurrencies is Changing the World (2017)
7. Modelling Extremal Events for Insurance and Finance (1997) — link
8. Continuous Univariate Distributions, Volume 2 (1995)

0 of 45 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.