Modeling Long Memory and Regime Switching with an MRS-FIEGARCH Model: A Simulation Study

Recent research suggests that long memory can be caused by regime switching and is easily confused with it. However, if the causes of confusion were properly controlled, they could distinguished. Motivated this idea, our study aims to distinguish between financial volatility. We firstly modeled volatility using Fractionally Integrated Exponential GARCH (FIEGARCH) Markov Regime-Switching EGARCH (MRS-EGARCH) frameworks, respectively, performed a simulation on their finite-sample properties when innovations followed non-normal distribution. Subsequently, we demonstrated FIEGARCH MRS-EGARCH processes simulations. A recent theoretically proved time-varying smoothing probability series induce presence significant in regime-switching process. To control for its effect, two-stage two-state MRS-FIEGARCH frameworks are proposed. The Monte Carlo studies showed both effectively pure processes. When model was further employed fit generated process, it outperformed ordinary model. Finally, an empirical NASDAQ index return conducted demonstrate provide potentially more reliable long-memory estimates, identify states outperform models.