One of the most important hyper-parameters in duration-dependent Markov-switching (DDMS) models is the duration of the hidden states. Because there is currently no procedure for estimating this duration or testing whether a given duration is appropriate for a given data set, an ad hoc duration choice must be heuristically justified. In this paper, we propose and examine a methodology that mitigates the choice of duration in DDMS models when forecasting is the goal. Two Monte Carlo simulations, based on classical applications of DDMS models, are employed to evaluate the methodology. In addition, an empirical investigation is carried out to forecast the volatility of the S\&P 500, which showcases the capabilities of the proposed model.

翻译：持续时间依赖马尔可夫转换（DDMS）模型中最关键的超参数之一是隐状态的持续时间。由于目前既不存在估算该持续时间的方法，也不存在检验特定持续时间是否适用于给定数据集的程序，因此必须通过启发式方法对临时选择的持续时间进行论证。本文提出并研究了一种在预测目标下缓解DDMS模型持续时间选择问题的方法。基于DDMS模型的经典应用场景，我们采用两组蒙特卡洛模拟对该方法进行评估。此外，我们通过对标普500指数波动率的实证预测研究，展示了所提模型的应用能力。