Component-Driven Regime-Switching Volatility
提出一类新的机制转换波动率模型，通过将机制分解为多个独立的两状态成分链，实现高维状态空间和简约转移矩阵，在标普500指数和IBM股票数据上，该模型在预测已实现方差方面优于GARCH等传统模型。
We develop a new class of regime-switching volatility models that are characterized by high-dimensional state spaces, parsimonious transition matrices, and ARMA dynamics for the log volatility process. This combination of features is achieved by assuming that we can decompose the Markov chain that describes regime dynamics into a number of two-state component chains that evolve independently through time. Using daily data for S&P 500 index and IBM shares, we show that our component-driven regime-switching (CDRS) models are capable of outperforming GARCH, component GARCH, regime-switching GARCH, and Markov-switching multifractal models in forecasting realized variances out of sample. Interestingly, we find that CDRS models with simple AR(1) dynamics perform well across the board.