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Developing a crisis model based on higher-order moments.

Vera Ivanyuk1,2

  • 1Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, 125993, Moscow, Russia.

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Summary
This summary is machine-generated.

This study introduces a novel crisis detection model for financial time series. Higher-order moments effectively identify deviations and extreme values, forming the basis of a new crisis indicator.

Keywords:
Higher-order momentsPoint estimationStatistical momentTime series

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Area of Science:

  • Quantitative Finance
  • Econometrics
  • Time Series Analysis

Background:

  • Financial time series data are prone to sudden deviations and extreme values.
  • Detecting crisis situations in financial markets is crucial for risk management.
  • Existing models may not fully capture the nuances of crisis events.

Purpose of the Study:

  • To develop a model for detecting crisis situations in financial time series.
  • To analyze the utility of higher-order moments in identifying crisis indicators.
  • To propose a novel crisis indicator based on statistical moments.

Main Methods:

  • Analysis of fixed and cumulative central and raw moments.
  • Application of higher-order moment analysis to financial time series.
  • Development of a crisis indicator derived from moment analysis.

Main Results:

  • Higher-order moments effectively record deviations and extreme values in financial time series.
  • The proposed crisis indicator demonstrates the ability to signal crisis events.
  • Statistical moments provide valuable insights into the distributional properties during crises.

Conclusions:

  • Higher-order moments are significant in detecting anomalies within financial time series.
  • The developed crisis indicator offers a promising tool for financial crisis detection.
  • Moment analysis provides a robust framework for understanding crisis dynamics.