Institutional Backing and Crypto Volatility: A Hybrid Framework for DeFi Stabilization

ArXiv ID: 2512.19251 “View on arXiv”

Authors: Ihlas Sovbetov

Abstract

Decentralized finance (DeFi) lacks centralized oversight, often resulting in heightened volatility. In contrast, centralized finance (CeFi) offers a more stable environment with institutional safeguards. Institutional backing can play a stabilizing role in a hybrid structure (HyFi), enhancing transparency, governance, and market discipline. This study investigates whether HyFi-like cryptocurrencies, those backed by institutions, exhibit lower price risk than fully decentralized counterparts. Using daily data for 18 major cryptocurrencies from January 2020 to November 2024, we estimate panel EGLS models with fixed, random, and dynamic specifications. Results show that HyFi-like assets consistently experience lower price risk, with this effect intensifying during periods of elevated market volatility. The negative interaction between HyFi status and market-wide volatility confirms their stabilizing role. Conversely, greater decentralization is strongly associated with increased volatility, particularly during periods of market stress. Robustness checks using quantile regressions and pre-/post-Terra Luna subsamples reinforce these findings, with stronger effects observed in high-volatility quantiles and post-crisis conditions. These results highlight the importance of institutional architecture in enhancing the resilience of digital asset markets.

Keywords: Systemic Risk, Price Volatility, DeFi vs CeFi, HyFi, Panel Data Analysis, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper uses standard econometric models (panel EGLS, quantile regressions) with real financial data over a significant period and includes robustness checks, indicating solid empirical work. However, the mathematical complexity is relatively low, relying on established statistical methods rather than advanced theoretical derivations or novel algorithms.

  flowchart TD
    A["Research Goal<br>Determine if institutional backing<br>reduces crypto volatility?"] --> B["Data & Methodology"]
    
    subgraph B ["Data & Methodology"]
        direction LR
        B1["Dataset<br>18 Cryptocurrencies<br>Jan 2020 - Nov 2024"] --> B2["Analysis<br>Panel EGLS Models<br>Fixed/Random/Dynamic Specs"]
    end
    
    B2 --> C{"Key Variables"}
    
    subgraph C ["Key Variables"]
        direction LR
        C1["HyFi Status<br>Institutional Backing"]
        C2["Volatility Measures<br>Price Risk"]
        C3["Market Conditions<br>VOL / Crisis Periods"]
    end
    
    C --> D["Computational Process"]
    
    subgraph D ["Computational Process"]
        D1["Main Analysis<br>Panel Regressions"]
        D2["Robustness Checks<br>Quantile Regressions<br>Pre/Post Terra-Luna"]
    end
    
    D --> E{"Key Findings"}
    
    subgraph E ["Key Findings"]
        E1["HyFi Assets<br>Lower Price Risk"]
        E2["Volatility Interaction<br>Stabilizing Effect Intensifies<br>during High Market Volatility"]
        E3["Decentralization<br>Increased Volatility<br>Amplified in Market Stress"]
        E4["Post-Crisis<br>Stronger HyFi Effects<br>after Terra-Luna Collapse"]
    end