這篇論文的主要目的是檢測風險與報酬之間的關係。 傳統的資本資產定價模型將風險分成兩部分:系統性風險與非系統性風險。此外其隱含了期望報酬率與系統性風險應為正相關，而非系統性風險對期望報酬率則無解釋能力。然而許多理論及實證研究卻認為非系統性風險應對期望報酬有些許的解釋能力，故在這篇論文中我們將利用OLS(最小平方迴歸分析法)及Quantile Regression Model(分量迴歸法)來檢測期望報酬率、系統性風險與非系統性風險之間的關係。
若使用最小平方迴歸分析法，我們發現系統性風險及非系統性風險對期望報酬皆沒有解釋能力，而若使用分量迴歸法，當期望報酬為中間值時，系統性風險與其有顯著且正向的相關性，而當期望報酬為極端值時，非系統性風險對其有相當的解釋能力，並且我們發現當期望報酬很高的時候，非系統性風險與其為正相關，當期望報酬非常差的時候，非系統性風險與其則為負相關。這表示股票表現得很好的時候，越高的非系統性風險可帶來較高的報酬，但相反地，當股價大幅下跌時，越高的非系統性風險將可能使情況變得更糟。

The purpose of this paper is to examine the relationship between risk and rreturn. The traditional Capital Asset Pricing Model divides risk into two parts: systematic and nonsystematic. In addition, it implies that expected return and systematic risk should have positive relationship, although idiosyncratic risk has no explanatory power with regard to expected return. Therefore, various theoretical and several empirical studies suggest that nonsystematic risk should also have some explanatory power with regard to expected return. In this paper we will use an OLS test and Quantile Regression Model (QR model) to detect these relationships.
When using an OLS test, we find that neither systematic nor and nonsystematic risk have any explanatory power with regard to expected return. In contrast, with a Quantile Regression Model, at the extreme value of expected return, nonsystematic risk has considerable explanatory power. Moreover, systematic risk is significantly and positively related to expected return at the middle value of expected return. Moreover, we find that when the value of expected return is highly positive, nonsystematic risk has a positive relationship to it; but when the value of expected return is highly negative, nonsystematic risk has a negative relationship to it. This means that a greater nonsystematic risk could
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