Tree-Structured Multiple Regimes in Interest Rates
提出一个广义树状结构模型，用于描述短期利率的区制依赖均值回复和波动率聚集，能内生估计最优区制数，并利用期限结构等宏观信息，在预测条件一阶和二阶矩上优于传统模型。
This article develops a generalized tree-structured (GTS) model of the short-term interest rate that accommodates regime-dependent mean reversion and regime-dependent volatility clustering and level effects in the conditional variance. The model is constructed using the idea of multivariate tree-structured thresholds and nests the popular generalized autoregressive conditional heteroscedasticity and square root processes as simple special cases. It allows us to estimate the optimal number of regimes endogenously from the data and to exploit possible additional information in the term structure and in other macroeconomic variables. We provide empirical evidence of the strong potential of the GTS model in forecasting conditional first and second moments, also in comparison with alternative models of the short rate.