Markov Switching Component ARCH Model: Stability and Forecasting

TL;DR

This paper proposes a Markov switching component ARCH model with dynamic weights for better volatility forecasting, demonstrating improved accuracy over traditional models through simulation and empirical analysis.

Contribution

It introduces a novel Markov switching GARCH extension with time-varying weights, enhancing volatility modeling and forecasting capabilities.

Findings

The model captures shock variants effectively.

It provides superior volatility forecasts.

The Bayesian estimation method is effective.

Abstract

This paper introduces an extension of the Markov switching GARCH model where the volatility in each state is a convex combination of two different GARCH components with time varying weights. This model has the dynamic behavior to capture the variants of shocks. The asymptotic behavior of the second moment is investigated and an appropriate upper bound for it is evaluated. The estimation of the parameters by using the Bayesian method via Gibbs sampling algorithm is studied. Finally we illustrate the efficiency of the model by simulation and empirical analysis. We show that this model provides a much better forecast of the volatility than the Markov switching GARCH model.