Introduction and Motivation
The rise of decentralized finance (DeFi) has revolutionized traditional financial services by eliminating intermediaries through blockchain-based protocols. DeFi applications—such as decentralized exchanges, lending platforms, and stablecoins—leverage smart contracts to create transparent, trustless systems. A notable innovation within this ecosystem is derivative tokens, which provide decentralized exposure to derivative markets. This study investigates spillover effects between major cryptocurrencies (Bitcoin and Ethereum) and derivative tokens, using a quantile vector autoregression (QVAR) approach to capture dynamics across market conditions.
Derivatives and Derivative Tokens: An Overview
Derivatives are financial contracts tied to underlying assets (e.g., stocks, commodities, cryptocurrencies), enabling hedging or speculation on future price movements. In blockchain ecosystems, derivative tokens represent a subclass of DeFi assets that replicate traditional derivatives' functionalities. Examples include:
- Synthetix (SNX): Facilitates synthetic asset trading.
- Injective (INJ): Powers decentralized derivative exchanges.
- Perpetual Protocol (PERP): Offers perpetual contracts without expiry dates.
These tokens democratize access to derivatives while introducing unique volatility patterns linked to their underlying protocols.
Data and Methodology
Data Sources
Daily price data for Bitcoin (BTC), Ethereum (ETH), and 10 high-cap derivative tokens (e.g., SNX, INJ, UMA) were analyzed from 24 December 2020 to 10 February 2023. This period captures the emergence of most derivative tokens post-2020.
Methodology: Quantile VAR Approach
Adapting Ando et al. (2022), we employ a QVAR model to measure spillovers across quantiles (5th, 50th, 95th), revealing asymmetries during market extremes. Key steps include:
- Vector Moving Average (MA) Representation: Infinite-order MA derived from QVAR(p).
- Generalized Forecast Error Variance Decomposition (GFEVD): Quantifies spillover contributions across assets.
👉 Explore how derivative tokens compare to traditional crypto assets
Key Findings
Static Spillovers
Median Quantile (50th): Total connectedness index of 67.74%, indicating strong interdependencies even in stable markets.
- BTC and ETH exhibit bidirectional spillovers.
- Derivative tokens show higher linkage to ETH than BTC.
Extreme Quantiles (5th/95th):
- Lower quantile (5th): Spillovers intensify during downturns, with ETH acting as a volatility conduit.
- Upper quantile (95th): Derivative tokens contribute disproportionately to bullish momentum.
Dynamic Spillovers
- Ethereum’s Central Role: ETH consistently transmits/receives more spillovers than BTC, reflecting its DeFi ecosystem’s depth.
- Market Resilience: Derivative tokens demonstrate lower systemic risk during crashes compared to BTC/ETH.
Conclusions and Implications
- Asymmetric Connectedness: Derivative tokens are more sensitive to ETH price movements across all market conditions.
- Portfolio Diversification: Investors may hedge crypto volatility by incorporating select derivative tokens.
- Policy Considerations: Regulatory frameworks should account for cross-market spillovers to mitigate systemic risks.
👉 Learn how DeFi innovations are reshaping finance
FAQs
Q1: How do derivative tokens differ from traditional cryptocurrencies?
A1: While cryptocurrencies like BTC serve as stores of value, derivative tokens derive their worth from underlying assets or protocols, enabling advanced financial strategies like leveraged trading.
Q2: Why does Ethereum exhibit stronger spillovers than Bitcoin?
A2: Ethereum’s smart contract capabilities and dominant role in DeFi make it a hub for derivative token activity, amplifying its price influence.
Q3: Are derivative tokens safer during market crashes?
A3: Our analysis shows they absorb shocks better than BTC/ETH in extreme downturns, though individual token risks vary.
Q4: What practical steps can investors take based on these findings?
A4: Diversify holdings across BTC, ETH, and select derivative tokens to balance risk exposure while capitalizing on intermarket trends.
This study was funded by Wenzhou-Kean University (IRSPC2023002).