/MSGARCH_comp

Comparison of Markov-Switching GARCH models, namely symmetric GARCH, EGARCH, GJR-GARCH, performances in Value-at-Risk forecasting.

Primary LanguageRMIT LicenseMIT

MSGARCH_comp

Comparison of Value-at-Risk forecasting performance of Markov-Switching GARCH models, namely symmetric GARCH, Exponential GARCH, and GJR-GARCH, based on stock markets universe.

The data considered here are 5,000 daily percentage log returns of each stock indices: DAX, S&P500, and Nikkei.
The first 3,000 daily log returns are fit univariately to 2-state-Markov-switching GARCH-type models as mentioned above, each with 2 innovation assumptions: Normal and Student's t distributions.

Model estimation is done by MCMC, using a robust adaptive random-walk Metropolis algorithm proposed by Vihola (2012). The forecasting horizons used here are 1, 3, 10 and 22, and the VaR is calculated at 1% shortfall probability.

For a fixed rolling-window length of 3,000, the experiment is repeated 2,000 times, providing the 2,000 out-of-sample data, which later on used for evaluating the forecasting performance of each model setting.

The goodness of in-sample fit is evaulated based on:

  1. The Akaike information criterion (AIC) and
  2. The Bayesian information criterion (BIC).

The forecasting performance is evaluated based on

  1. Backtesting:
    • Unconditional coverage test by Kupiec (1995),
    • Independence test by Christoffersen (1998),
    • Conditional coverage test by Christoffersen (1998),
    • Dynamic quantile test of Engle and Mangenelli (2004), and
  2. Model confidence set by Hansen et al. (2011), with the VaR-based loss function defined by González-Rivera et al (2004).

Require R packages: