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Time-varying higher moments in Bitcoin.

Leonardo Ieracitano Vieira1, Márcio Poletti Laurini1

  • 1Department of Economics, FEARP, University of São Paulo, Av. dos Bandeirantes 3900, Ribeirão Preto, 14040-950 Brazil.

Digital Finance
|December 28, 2022
PubMed
Summary
This summary is machine-generated.

This study analyzes Bitcoin returns using advanced statistical models to understand its extreme price movements. Findings help in better predicting cryptocurrency investment risks.

Keywords:
BitcoinGeneralized autoregressive scoreHigher-order momentsRisk management

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Area of Science:

  • Quantitative Finance
  • Econometrics
  • Computational Finance

Background:

  • Cryptocurrencies, like Bitcoin, are novel investments with significant volatility.
  • Traditional financial models struggle with the asymmetric and extreme price changes characteristic of cryptocurrencies.

Purpose of the Study:

  • To model time-varying higher-order moments (scale, skewness, kurtosis) of Bitcoin returns.
  • To investigate the predictive performance of Generalized Autoregressive Score (GAS) models with non-traditional innovations distributions for Bitcoin.

Main Methods:

  • Utilized a modeling framework with time-varying higher-order moments.
  • Estimated a series of Generalized Autoregressive Score (GAS) models.
  • Employed non-traditional innovations distributions to capture cryptocurrency return dynamics.
  • Compared model predictive performance using a Value at Risk (VaR) loss function.

Main Results:

  • The study successfully modeled the complex, time-varying nature of Bitcoin's return distribution.
  • GAS models with specific innovations distributions demonstrated superior predictive accuracy for Value at Risk.

Conclusions:

  • Advanced econometric models are crucial for understanding and managing cryptocurrency investment risks.
  • The findings provide a more robust framework for assessing Bitcoin's volatility and potential future price movements.