這篇論文主要是應用stochastic conditional duration (SCD) 模型做為實證分析的依據。
首先，我們利用 autoregressive conditional duration (ACD) 模型，為此篇論文的原始模型，
然後，再運用 SCD 模型的技巧來改善 ACD 模型。
最後，我們從 NYSE 中選取出兩檔股票，分別把資料套用至 ACD 與 SCD 模型中，並比較那一個模型較為適用。
ACD 模型主要是以過去的交易時間距離來預期當期的交易時間距離，並透過交易時間距離變化，來判斷市場上訊息的流入時間點。
而 SCD 模型則是在 ACD 模型中的潛在方程式 (latent equation) 中加入隨機的因子，
來改善 ACD 模型的不足的地方。
然而這個所加入的隨機因子可以用來捕捉隨機的突發事件,
這樣的設定能讓我們對交易時間距離的預期更加準確,
進而能精準地判斷何時會有訊息流入。
如果我們只運用 ACD 模型來預估交易時間距離，而不把當期的突發事件考量進來，
是有可能會讓我們對交易時間距離的預期變得較不準確。
我們透過上述論點，把資料分別帶入 SCD 和 ACD 模型之中來做比較，
經由實證分析可知 ACD 模型中的 log-ACD 模型仍就明顯地優於 SCD 模型的配適交易時間距離的能力，
log-ACD 模型將較能準確地捕捉訊息流入的時間點。

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