Robust Estimation of Realized Correlation: New Insight about Intraday Fluctuations in Market Betas

ArXiv ID: 2310.19992 “View on arXiv”

Authors: Unknown

Abstract

Time-varying volatility is an inherent feature of most economic time-series, which causes standard correlation estimators to be inconsistent. The quadrant correlation estimator is consistent but very inefficient. We propose a novel subsampled quadrant estimator that improves efficiency while preserving consistency and robustness. This estimator is particularly well-suited for high-frequency financial data and we apply it to a large panel of US stocks. Our empirical analysis sheds new light on intra-day fluctuations in market betas by decomposing them into time-varying correlations and relative volatility changes. Our results show that intraday variation in betas is primarily driven by intraday variation in correlations.

Keywords: Quadrant Correlation Estimator, High-Frequency Data, Time-Varying Volatility, Market Beta Decomposition, Subsampling, Equities

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts including robust estimators, subsampling techniques, and consistency proofs, indicating high math complexity. It also demonstrates strong empirical rigor through application to high-frequency financial data, simulation studies, and decomposition of market betas, making it backtest-ready.

  flowchart TD
    A["Research Goal<br>Estimate Realized Correlation<br>with Time-Varying Volatility"] --> B["Methodology<br>Subsampled Quadrant Estimator"]
    
    B --> C["Input: High-Frequency<br>US Equity Data"]
    C --> D["Computational Process<br>1. Calculate Quadrant Covariance<br>2. Apply Subsampling<br>3. Decompose Beta"]
    
    D --> E{"Key Outcomes"}
    E --> F["Robust & Efficient<br>Correlation Estimator"]
    E --> G["Beta Fluctuations driven by<br>Correlation, not Volatility"]
    E --> H["New Insight on<br>Intraday Market Dynamics"]