Applying CAPM to Decentralized Finance Assets for a Practical Guide

Introduction

Decentralized finance (DeFi) has introduced a wide array of new asset classes—stablecoins, liquidity provider tokens, synthetic derivatives, and governance tokens. While these instruments offer novel risk‑return profiles, investors still need a principled way to compare them against traditional benchmarks.
The Capital Asset Pricing Model (CAPM) provides a framework to estimate expected returns based on systematic risk. In this guide we show how to adapt CAPM to DeFi assets, walk through a concrete example, and highlight practical tools and caveats.
Decentralized finance (DeFi)

CAPM Recap

CAPM expresses the expected return (E(R_i)) of an asset (i) as:

[ E(R_i)=R_f+\beta_i\left(E(R_m)-R_f\right) ]

where (\beta_i) captures the asset’s sensitivity to systematic market movements.
expected return

Challenges in Applying CAPM to DeFi

• Risk‑Free Rate Selection – Conventional risk‑free rates come from government bonds. In a crypto environment, the closest analog is often a stablecoin pegged to fiat or a highly liquid DeFi collateral.
  The stablecoin is usually pegged to fiat.
• Debt‑to‑Equity Ratio – Many DeFi protocols operate on borrowed capital, so the capital structure becomes a key consideration in assessing systematic risk.
• Liquidity Dynamics – The availability of capital and the cost of borrowing can create deviations from traditional CAPM assumptions.

Estimating Beta for a DeFi Asset

Beta is the key coefficient that measures how much an asset’s returns move with the overall market.
β (beta)

Choosing an Appropriate Risk‑Free Rate

Stablecoin Yield (e.g., USDC on Compound)
Risk‑free rate proxies – Many DeFi protocols use stablecoin yields or treasury proxies as a de‑facto risk‑free rate, which is a core topic in interest‑rate modeling.

Tools and Libraries

Example Code Snippet (Python)

import pandas as pd
import statsmodels.api as sm

# Load price data
price = pd.read_csv('price_data.csv', parse_dates=['date'], index_col='date')

# Calculate returns
returns = pd.DataFrame()
returns['asset'] = price['AAVE'].pct_change().apply(np.log).dropna()
returns['market'] = price['DeFi20'].pct_change().apply(np.log).dropna()

# Regression
X = sm.add_constant(returns['market'])
model = sm.OLS(returns['asset'], X).fit()
beta = model.params['market']