Modeling exchange rate dynamics: Non-linear dependence and thick tails
提出用学生t自回归动态异方差模型（STAR）刻画汇率数据的非线性依赖和厚尾特征，该模型能更简洁准确地描述汇率概率信息，波动率预测优于传统ARCH类模型。
This paper illustrates a new approach to the statistical modeling of non-linear dependence and leptokurtosis in exchange rate data. The student's t autoregressive model withdynamic heteroskedasticity (STAR) of spanos (1992) is shown to provide a parsimonious and statistically adequate representation of the probabilistic information in exchange rate data. For the STAR model, volatility predictions are formed via a sequentially updated weighting scheme which uses all the past history of the series. The estimated STAR models are shown to statistically dominate alternative ARCH-type formulations and suggest that volatility predictions are not necessarily as large or as variable as other models indicate.