實證結果發現，利用平滑公式配適隱含波動率所得到的係數，會隨著時間變動而變化，具有隨狀態時間改變的性質。利用二階段的預測方式，可以增加橫斷面模型對隱含波動率曲面的配適效果，隱含波動率曲面具有可預測性；然而隨著預測的期間增加，預測曲面的效果會迅速降低，甚至產生對係數過度配適的問題。 One key stylized fact in the empirical option pricing literature is the existence of an implied volatility surface (IVS). The usual approach consists of fitting a linear model linking the implied volatility to the time to maturity and the moneyness, for each cross section of options data. However, recent empirical evidence suggests that the parameters characterizing the IVS change over time. In this paper we study whether the resulting predictability patterns in the IVS coefficients may be exploited in practice. We propose a two-stage approach to modeling and forecasting the TAIEX option IVS. In the first stage we model the surface along the cross-sectional moneyness and time-to-maturity dimensions, similarly to Dumas et al. (1998). In the second-stage we model the dynamics of the cross-sectional first-stage implied volatility surface coefficients by means of vector autoregression models. We find that not only the TAIEX implied volatility surface can be success fully modeled, but also that its movements over time are predictable in a statistical sense. However, when the fitted implied volatileity surface one week later, the VAR-type model’s prediction errors grow larger than another. The time passing is an important cause of overfitting at the movements of IVS.