Optimal vs. Naive Diversification in the Cryptocurrencies Market: The Role of Time-Varying Moments and Transaction Costs

ArXiv ID: 2501.12841 “View on arXiv”

Authors: Unknown

Abstract

This study investigates three central questions in portfolio optimization. First, whether time-varying moment estimators outperform conventional sample estimators in practical portfolio construction. Second, whether incorporating a turnover penalty into the optimization objective can improve out-of-sample performance. Third, what type of optimal portfolio strategies can consistently outperform the naive 1/N benchmark. Using empirical evidence from the cryptocurrencies market, this paper provides comprehensive answers to these questions. In the process, several additional findings are uncovered, offering further insights into the dynamics of portfolio construction in highly volatile asset classes.

Keywords: portfolio optimization, time-varying moments, turnover penalty, cryptocurrencies, naive 1/N benchmark, Cryptocurrencies

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced statistical models like DCC-EGARCH and deep-learning forecasting, indicating high mathematical complexity. It also demonstrates strong empirical rigor through extensive backtesting on cryptocurrency data, considering transaction costs and multiple frequencies.

  flowchart TD
    A["Research Goal: Determine optimal portfolio strategies<br/>in crypto markets with time-varying moments & costs"] --> B["Data: Crypto Asset Returns<br/>Historical Price Data"]
    B --> C{"Key Methodology Tests"}
    C --> D["Estimators Comparison<br/>Time-Varying vs. Sample Moments"]
    C --> E["Objective Optimization<br/>Standard vs. Turnover Penalty"]
    C --> F["Benchmarking<br/>Naive 1/N vs. Optimal Strategies"]
    
    D & E & F --> G["Computational Process<br/>Rolling Window Backtesting<br/>Transaction Cost Modeling"]
    
    G --> H["Key Findings"]
    
    subgraph H ["Outcomes"]
        H1["Time-varying moments generally outperform sample estimators"]
        H2["Turnover penalty improves risk-adjusted returns by reducing trading frequency"]
        H3["Optimal strategies with low-frequency rebalancing beat naive 1/N consistently"]
    end