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As digital assets continue to evolve and gain traction, more institutions are exploring opportunities to integrate crypto into traditional finance portfolios. According to the 2025 Institutional Investor Digital Assets Survey, 83% of investors plan to increase allocations to digit al assets over the year. Learn more in this week's blog:
Digital assets, like cryptocurrencies, have shown potential for higher returns than traditional asset classes. For example, Bitcoin prices surged by approximately 125% in 2024 compared to 23% of the S&P 500. Still, crypto assets remain more volatile, and consequently, riskier than traditional assets, requiring robust risk management.
Here are four risk management strategies for institutions considering crypto investing:
1. Counterparty Risk Evaluation
Digital asset allocation strategies must include a counterparty risk evaluation to protect against financial losses. In the crypto space, counterparties vary, but common ones include cryptocurrency exchanges and decentralized finance (DeFi) protocols. Monitoring and analyzing historical exchange performance — particularly during highly volatile periods — trading volumes, and order book depth are ways to evaluate an exchange's risk profile before trading on it.
2. Volatility Management Techniques
Volatility management is crucial to mitigate price swings common in crypto investing and avoid substantial drawdowns. It encompasses many of the same techniques used to manage traditional asset volatility, like diversification and position sizing. Stablecoin allocation can also reduce risk in extremely volatile periods.
3. Liquidity Risk Assessments
Due to the fragmented markets in digital assets, it can be challenging to obtain a clear view of available liquidity and accurately assess risk. A structured risk assessment is necessary to quantify liquidity risk levels and determine mitigation strategies. An assessment generally involves aggregating data from disparate markets, analyzing the data, and generating risk scores that quantify liquidity risk levels.
Order book analysis and bid-ask-spread analysis are techniques that yield insight into liquidity risk. Stress testing, which gauges how a portfolio would perform in extreme market conditions, is another useful technique for identifying liquidity vulnerabilities.
4. Drawdown Mitigation Strategies
Mitigating the impact of drawdowns is one of the keys to protecting capital, especially in a highly volatile market like crypto. Many drawdown mitigation strategies overlap with those used to manage volatility, like diversifying crypto assets and holding a portion of stablecoins in a portfolio. Another important technique is implementing stop-loss orders to automatically sell crypto assets when they dip to a specific price, preventing them from becoming significant drawdowns.
Why Trust Amberdata?
Amberdata closes the gap between traditional finance and crypto investing. We provide actionable market and on-chain data, as well as comprehensive tools to empower organizations in making decisions, mitigating risks, and confidently growing their crypto portfolios. Financial professionals use our digital asset data and analytics to implement familiar risk and portfolio management frameworks, making the transition from traditional to digital seamless.
Explore Amberdata's Risk and Portfolio Management Solution
Traditionally risk-averse institutions can grow their potential for greater returns and diversification with digital assets. The key lies in comprehensive risk and portfolio management driven by high-quality data and actionable analytics.
Our risk and portfolio management solution was designed to help organizations assess risk exposure and manage crypto portfolios. It provides comprehensive analytics, including liquidity risk monitoring and stress testing, fueled by granular market data. With Amberdata, you get a unified view of performance and transaction data across digital assets, simplifying risk management and supporting optimized portfolio management.
Request a demo today and discover how our solutions transform data complexity into actionable insights.