本文比較了台積電美國存託憑證(TSMC ADR)股票價格在不同密度預測方法下的準確性。我們主要採用兩種方法來預測隔天的股票價格。

在第一種方法中，受Fan，Taylor和Sandri（2017）的啟發，我們將5分鐘高頻報酬帶入實現波動率的異質自我迴歸模型(HAR-RV model)來獲得密度預測，並將密度預測從風險中立的機率測度轉換到真實世界的機率測度。 在第二種方法中，根據ŽIKEŠ和BARUNÍK（2016），我們將實現波動率的連續分量和跳躍分量帶入線性分位數迴歸模型(LQR model)來獲得密度預測。

在獲得兩個模型的密度預測之後，我們計算每個密度的對數似然分數，用來分辨哪個密度預測方法可以較準確地預測出隔天的股票價格。

我們的貢獻是為投資者、決策者和管理者提供良好的股價預測方法，讓他們在做出決策之前能有可以參考的依據。

The paper compares the accuracy of density forecasts for TSMC ADR stock prices. We majorly adopt two methods to forecast the next day stock price.

In the first method, inspired by Fan, Taylor and Sandri (2017), we consider HAR-RV model with 5-min high-frequency returns to obtain density forecast, and transform the density from risk-neutral measure to real-world measure. In the second method, following ŽIKEŠ and BARUNÍK (2016), we use LQR model with the continuous- and jump- components of realized volatility to obtain the density forecast.

After having the densities of the two models, we calculate the log-likelihood scores of each density in order to tell which density forecasting method is more accuracy in forecasting the next day stock price.

Our contribution is to provide a good forecasting method for investors, policy makers, and managers as a reference before they make decisions.

摘要-----------------------------------------------------I
ABSTRACT------------------------------------------------II
Table of Contents--------------------------------------III
List of Figures-----------------------------------------IV
List of Tables-------------------------------------------V
1 INTRODUCTION-------------------------------------------1
2 METHODOLOGY--------------------------------------------7
2.1 Applied high-frequency returns into HAR-RV model-----7
2.2 Lognormal densities of HAR-RV model------------------9
2.3 Nonparametric transformations-----------------------10
2.4 Diagnostic test for densities of HAR-RV model-------13
2.5 Theoretical framework of LQR model------------------14
2.6 Densities of LQR model------------------------------15
2.7 Ranking---------------------------------------------17
3 DATA--------------------------------------------------19
3.1 High-frequency stock prices-------------------------19
3.2 Futures prices--------------------------------------20
4 EMPIRICAL RESULTS-------------------------------------21
4.1 Density forecasts of HAR-RV model-------------------21
4.2 Density forecasts of LQR model----------------------26
4.3 Log-likelihood comparisons--------------------------32
5 CONCLUSIONS-------------------------------------------36
REFERENCES----------------------------------------------38

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