Statistical Modeling of High Frequency Financial Data: Facts, Models and Challenges

ArXiv ID: ssrn-1748022 “View on arXiv”

Authors: Unknown

Abstract

The availability of high-frequency data on transactions, quotes and order flow in electronic order-driven markets has revolutionized data processing and statist

Keywords: High-Frequency Trading, Market Microstructure, Electronization, Algorithmic Trading, Time-Series Analysis, Equity / Quantitative Finance

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper involves advanced stochastic calculus and modeling of high-frequency data, indicating high mathematical complexity, while its focus on empirical high-frequency data and statistical methods suggests a strong, though not code-heavy, empirical backing.

  flowchart TD
    A["Research Goal: Model High-Frequency<br>Financial Data in Order-Driven Markets"] --> B["Data Collection:<br>Transactions, Quotes, Order Flow"]
    B --> C["Methodology:<br>Time-Series & Statistical Analysis"]
    C --> D["Computational Modeling:<br>Volatility Estimation & Microstructure"]
    D --> E["Key Finding 1:<br>Data Irregularities (Clock Effects)"]
    D --> F["Key Finding 2:<br>Microstructure Noise Bias"]
    D --> G["Key Finding 3:<br>Modeling Challenges & Solutions"]