Discrete time affine term structure models with squared Gaussian shocks (DTATSM-SGS)
在离散时间仿射期限结构模型中引入平方高斯冲击，保证因子非负且无需Feller条件，通过二阶Esscher变换灵活调整条件协方差，实证表明比自回归伽马模型更好地预测收益率条件波动、无条件矩和期限溢价。
The tractability of discrete time affine term structure models (DTATSM) is fully preserved when adding squared Gaussian shocks (SGS) to factor processes. SGS guarantee non-negative factors under parameter restrictions that do not affect market prices of risk. Feller conditions are not needed. Changes of measure can alter the conditional covariance of factors and yields through the flexible second-order Esscher transform. Non-negative factors can be conditionally correlated under the real measure even if they are not under the risk-neutral measure. The empirical evidence from US Treasury yields shows that SGS models tend to predict yields conditional volatility, yields unconditional moments and term premia better than the corresponding autoregressive gamma (AG) models.