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CD-vine model for capturing complex dependence.

O Ozan Evkaya1, Ceylan Yozgatlıgil2, A Sevtap Selcuk-Kestel3

  • 1Research Center for ORSTAT, KU Leuven, Leuven, Belgium.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel CD-vine mixture model to analyze complex dependencies in time-series data. The model effectively captures variable relationships over time, outperforming traditional methods in financial market analysis.

Keywords:
C-vineCD-vine mixtureD-vineMixture modelstock market indices

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Area of Science:

  • Statistics
  • Financial Modeling
  • Time Series Analysis

Background:

  • Finite mixture models offer flexible dependence structures for random variables.
  • Limited research exists on finite mixture of vines, particularly for time-series data.
  • Classical mixture models do not fully capture complex temporal dependencies.

Purpose of the Study:

  • To develop a novel CD-vine (C-vine within a D-vine) mixture model.
  • To express and analyze dependencies between variables in temporal order.
  • To model both within-time-point and across-time-point dependencies.

Main Methods:

  • Incorporation of C-vines into a D-vine structure to create a CD-vine mixture model.
  • Probabilistic tying of cumulative distribution function values across time components using D-vine.
  • Utilized simulated data for model validation and applied to real-world stock market index data.

Main Results:

  • The CD-vine mixture model successfully captures complex dependence structures.
  • C-vines explain dependencies within each time point, while D-vine models dependencies across time points.
  • The model demonstrated robust performance on simulated data and practical applicability to financial markets.

Conclusions:

  • The proposed CD-vine mixture model provides a flexible and powerful tool for analyzing multivariate temporal dependencies.
  • This approach enhances understanding of financial market dynamics by modeling intricate relationships over time.
  • The study highlights the potential of vine mixture models in advanced statistical and financial applications.