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Observing Cryptocurrencies through Robust Anomaly Scores.

Geumil Bae1, Jang Ho Kim2,3

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|November 24, 2022
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Summary
This summary is machine-generated.

Understanding cryptocurrency volatility is key for investors. This study introduces anomaly scores, using robust Mahalanobis distances, as a vital tool alongside volatility measures for better market analysis and investment decisions.

Keywords:
Mahalanobis distanceanomaly scorecryptocurrencyminimum covariance determinantshrinkage estimators

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Area of Science:

  • Quantitative Finance
  • Computational Economics
  • Financial Econometrics

Background:

  • Cryptocurrency markets exhibit higher volatility compared to traditional assets.
  • Accurate modeling of this volatility is crucial for informed investment decisions.
  • Market anomalies, indicated by deviations and return correlations, require robust measurement techniques.

Purpose of the Study:

  • To introduce and demonstrate the utility of anomaly scores for analyzing cryptocurrency markets.
  • To apply robust Mahalanobis distances using shrinkage estimators and minimum covariance determinant for calculating these scores.
  • To show how anomaly scores complement traditional volatility measures in understanding market behavior.

Main Methods:

  • Utilized robust Mahalanobis distances.
  • Employed shrinkage estimators for parameter estimation.
  • Applied the minimum covariance determinant (MCD) method for robust covariance matrix estimation.
  • Calculated anomaly scores for cryptocurrencies.

Main Results:

  • Anomaly scores provide critical insights that supplement standard volatility measures.
  • The proposed method effectively identifies market anomalies in cryptocurrency data.
  • Demonstrated the practical application of anomaly scores in portfolio optimization and scenario analysis.

Conclusions:

  • Anomaly scores are an essential tool for a comprehensive understanding of cryptocurrency market dynamics.
  • The integration of anomaly scores enhances investment decision-making processes.
  • Robust statistical methods are effective in quantifying market anomalies in volatile financial markets.