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Intraday lottery demands in cryptocurrency market

Author

Listed:
  • Manisha Yadav

Abstract

Purpose - Using the high-frequency intraday data of the top 100 most liquid cryptocurrencies, this study aims to examine the presence of the MAX effect in the cross-sectional pricing of cryptocurrencies. Additionally, it delves into the pricing implications of idiosyncratic volatility (IVOL) and skewness, both idiosyncratic and systematic, across the cross-section of cryptocurrency returns and their interaction with the MAX measure. Design/methodology/approach - Driven by the growing influence of high-frequency trading (HFT) in the cryptocurrency market, the study modifies Baliet al.’s (2011) MAX measure by incorporating an hourly forecast horizon and 5-min log returns from the preceding hour. The relationship between MAX, IVOL and skewness over the past hour and expected returns is examined using portfolio-level analysis and Fama and Macbeth’s (1973) cross-sectional regressions. Findings - The findings indicate that an increase of one standard deviation in MAX corresponds to a 0.043% decline in subsequent returns for cryptocurrencies, suggesting overvaluation due to increased demand. These results are robust to other traditional price determinants. Untangling the MAX from other proxies of the lottery, the study reveals that MAX is the true effect in the cryptocurrency market. The results are robust to several sensitivity checks, such as varying MAX measures and holding periods. Originality/value - The study pioneers the investigation of lottery-like demand within cryptocurrency markets at the intraday frequency. To the best of the author’s knowledge, this is the first paper untangling the association between MAX and IVOL in the cryptocurrency market and thus offers valuable insights into investor behavior in these emerging markets.

Suggested Citation

  • Manisha Yadav, 2025. "Intraday lottery demands in cryptocurrency market," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 42(4), pages 799-835, March.
  • Handle: RePEc:eme:sefpps:sef-07-2024-0461
    DOI: 10.1108/SEF-07-2024-0461
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    More about this item

    Keywords

    MAX effect; Idiosyncratic volatility; Lottery-like preferences; Cryptocurrency; Skewness preferences; Cross-section of returns; C31; G11; G12; G41;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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