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Area of Science:

  • Quantitative Finance
  • Econometrics
  • Time Series Analysis

Background:

  • Stock market indices exhibit complex return dynamics influenced by interdependencies.
  • Existing models may not fully capture the joint effects of multiple time series with volatility clustering.

Purpose of the Study:

  • To propose and validate a novel Network Autoregressive GARCH (NAR-GARCH) model.
  • To effectively depict the return dynamics of stock market indices, considering both autoregressive and GARCH effects.
  • To capture joint effects from nonsynchronous multiple time series.

Main Methods:

  • A GARCH filter is applied to remove individual index GARCH effects.
  • A Network Autoregressive (NAR) model incorporating Granger causality and Pearson's correlation tests is used.
  • The NAR-GARCH model is applied to daily returns of 20 global stock indices (2006-2020).

Main Results:

  • The NAR-GARCH model demonstrates satisfactory performance in fitting and prediction across 20 global stock indices.
  • The model is particularly effective in capturing dynamics during periods of strong upward or downward price movements.
  • Empirical investigation confirms the model's ability to depict joint effects in nonsynchronous time series.

Conclusions:

  • The NAR-GARCH model provides an easy-to-implement and effective approach for analyzing stock market index return dynamics.
  • The model successfully integrates autoregressive dependencies and GARCH effects for improved financial time series analysis.
  • The findings highlight the model's utility in understanding market interdependencies and predicting index behavior, especially during volatility.