Testing the Empirical Performance of Stochastic Volatility Models of the Short-Term Interest Rate
提出两因子离散时间随机波动率模型，比较现有与替代模型在预测短期利率水平与波动率上的表现，发现新模型优于扩散和GARCH模型。
I introduce two-factor discrete time stochastic volatility models of the short-term interest rate to compare the relative performance of existing and alternative empiricial specificattions. I develop a nonlinear asymmmetric framework that allows for comparisons of non-nested models featuring conditional heteoskedasticity and sensitivity of the volatility process to interest rate levels. A new class of stochastic volatility models with asymmetric GARCH models. The existing models are rejected in favor of the newly proposed models because of the asymmetric drift of the short rate, and the presence of nonlinearity, asymmetry, GARCH, and level effects in its volatility. I test the predictive power of nested and non-nested models in capturing the stochastic behavior of the risk-free rate. Empirical evidence on three-, six-, and 12-month U.S. Treasury bills indicates but that two-factor stochastic volatility models are better than diffusion and GARCH models in forecasting the future level and volatility of interest rate changes.