In recent years, cryptocurrencies have gained significant attention as a new asset class, sparking interest in their potential for portfolio diversification alongside traditional assets such as gold, stocks, and bonds. Unlike these tangible assets, cryptocurrencies are highly volatile, speculative, and influenced by unique market dynamics, raising questions about their role and effectiveness in diversified investment portfolios. This study aims to analyze the impact of cryptocurrencies on portfolio risk and diversification by applying two well-established risk assessment models: the Markowitz Mean-Variance (MV) model and the Value at Risk (VaR) Conditional Model. The Markowitz Mean-Variance model traditionally evaluates portfolio diversification by balancing expected returns with overall variance. In contrast, the Value at Risk model, including Conditional Value at Risk (CVaR), assesses the potential for extreme losses, providing a more tail-sensitive risk measure that may better capture the high volatility of cryptocurrency assets. By comparing these models, this research explores how cryptocurrency inclusion alters portfolio risk levels and diversification benefits compared to traditional assets.The findings are expected to offer insights into the viability of cryptocurrencies as diversification assets, highlighting whether these assets can contribute to portfolio resilience or amplify risk. This comparative analysis of MV and VaR models may guide investors in adapting their risk management strategies to incorporate cryptocurrencies effectively, considering both potential gains and the specific risk profile associated with these digital assets.