Modeling and forecasting aggregate stock market volatility in unstable environments using mixture innovation regressions

Nima Nonejad*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

1 Citation (Scopus)

Abstract

We perform Bayesian model averaging across different regressions selected from a set of predictors that includes lags of realized volatility, financial and macroeconomic variables. In our model average, we entertain different channels of instability by either incorporating breaks in the regression coefficients of each individual model within our model average, breaks in the conditional error variance, or both. Changes in these parameters are driven by mixture distributions for state innovations (MIA) of linear Gaussian state-space models. This framework allows us to compare models that assume small and frequent as well as models that assume large but rare changes in the conditional mean and variance parameters. Results using S&P 500 monthly and quarterly realized volatility data from 1960 to 2014 suggest that Bayesian model averaging in combination with breaks in the regression coefficients and the error variance through MIA dynamics generates statistically significantly more accurate forecasts than the benchmark autoregressive model. However, compared to a MIA autoregression with breaks in the regression coefficients and the error variance, we fail to provide any drastic improvements.

Original languageEnglish
JournalJournal of Forecasting
Volume36
Issue number6
Pages (from-to)718-740
Number of pages23
ISSN0169-2070
DOIs
Publication statusPublished - Sept 2017

Keywords

  • Mixture innovation regressions
  • Model uncertainty
  • Parameter instability
  • Stock market volatility

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