Paper: arXiv 2510.12911

Authors: Yasin Simsek

Abstract

Betas from spot regressions are central to asset pricing and risk management, as measures of systematic risk. This paper develops a new estimation and inference framework for spot regressions by leveraging high-frequency candlesticks, extending conventional (open-to-close) returns with intra-period high/low prices. Specifically, I construct candlestick-based estimators of regression parameters, including spot beta, by minimizing a quadratic risk under a fixed-k asymptotic framework. I then develop a feasible hypothesis testing procedure for spot betas with correct asymptotic size. Simulation results show that the proposed estimator reduces estimation risk relative to return-based estimators, especially in small samples, and the test achieves notably higher power. I apply the framework to assess the market neutrality of Bitcoin using 1-minute data on IBIT and SPY, finding deviations from neutrality, particularly in high-volatility periods.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces a novel decision-theoretic framework for multivariate spot regressions using high-frequency candlesticks under continuous-time Itô semimartingales. It combines rigorous fixed-k asymptotic theory with a practical empirical evaluation of Bitcoin market neutrality using IBIT and SPY high-frequency data.

Research Flowchart

  flowchart TD
    A[Goal: Enhance Spot Beta Estimation & Testing via Candlesticks] --> B[Input: High-Frequency Candlestick Data<br/><i>Open, High, Low, Close</i>]
    B --> C[Methodology: Minimize Quadratic Risk under Fixed-k Asymptotics]
    C --> D[Compute Candlestick-Based Spot Beta Estimators]
    D --> E[Feasible Hypothesis Testing for Spot Betas]
    E --> F[Validation: Simulations Show Lower Risk & Higher Power]
    E --> G[Empirical Test: Market Neutrality of BTC/IBIT vs SPY]
    G --> H[Outcome: BTC Rejects Neutrality in High-Volatility Periods]

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