Estimating Spot Betas from High-Frequency Candlesticks

Summary

This paper develops estimators and inference procedures for spot regressions, with particular attention to beta as a measure of systematic risk. Instead of relying only on open-to-close returns, the method uses high-frequency candlestick data, incorporating each interval’s high and low prices. The estimators are constructed by minimizing quadratic risk under a fixed-k asymptotic framework, and the paper proposes a feasible hypothesis test for spot beta.

Simulation results indicate lower estimation risk than return-based estimators, especially in small samples, while the proposed test has higher power. An application uses one-minute data for IBIT and SPY to examine whether Bitcoin is market neutral; the reported estimates depart from neutrality, especially during high-volatility periods. These findings concern a specific application and framework. The document gives no further details on sample design, robustness checks, or trading performance, so the beta result alone does not establish a profitable strategy or a general conclusion about Bitcoin’s market exposure.

Key ideas

• Candlestick highs and lows can supplement open-to-close returns in spot regression estimation.
• The framework estimates regression parameters, including spot beta, by minimizing quadratic risk.
• The proposed feasible test is designed to have correct asymptotic size.
• Simulations report lower estimation risk, particularly in small samples, and higher test power.
• The IBIT-SPY application finds departures from Bitcoin market neutrality, especially in high-volatility periods.

Tags

# Spot Regressions with Candlesticks


# Spot Regressions with Candlesticks









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.