Realised quantile-based estimation of the integrated variance

2010-09-15

AI Summary

This study addresses the challenges posed by jumps, outliers, and market microstructure noise in high-frequency financial data when estimating realized variance. The authors propose a robust quantile-based estimation method that constructs a quantile-type variance estimator asymptotically immune to finite-activity jumps and outliers, and extend it to noisy high-dimensional settings. Theoretical analysis demonstrates that the proposed estimator consistently recovers the integrated variance at the optimal convergence rate and exhibits favorable asymptotic efficiency. Monte Carlo simulations confirm its pronounced robustness in finite samples, and empirical applications to equity data further validate the practical effectiveness of the approach.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements

Problem

• realised variance
• integrated variance
• jump robustness
• market microstructure noise
• high-frequency data

Innovation

• quantile-based
• realised variance
• jump robust
• market microstructure noise
• integrated variance