本論文探討利用總體因子預測台灣股市的月成交量，並討論其預測準確度。總體因子主要利用主成分分析法從大量的總體資料中抽出，台灣股市月成交量資料主要來自TEJ資料庫，並將其分為九類：水泥窯業、食品業、塑膠化工業、紡織業、機電業、造紙業、營建業、金融業和加權指數。 結果發現三個月後的預測值比一個月後的預測值準確，而從RMSE跟MAE的結果，發現食品業、塑膠化工業、紡織業、機電業、造紙業預測的準確度較高。 / This paper discusses forecasting monthly turnover by static principle components method, and testing accuracy of forecasting. The monthly turnover is from Taiwan stock market as nine turnover classification, Cement & Kiln industry, Food industry, Plastic & Chemical industry, Textile industry, Mechanical & Electrical industry, Paper-making industry, Construction industry, Financial industry and Value-Weighted Index. The principle components extracted from large macroeconomic datasets have the explanatory power to monthly turnover. In addition, for basic forecasting, the accuracy of three-month prediction is better than one-month prediction in both subsamples. To test accuracy, RMSE (PC) and MAE (PC) are outperformed the same in Food industry, Textile& Fibers industry. However, MAE (PC) in Plastic & Chemical industry, RMSE (PC) in Mechanical & Electrical industry and Paper-making industry still show the good prediction as well.

為了研究總體因子與產業違約率之間的關聯性, 本文以信用投資組合觀點模型(CPV) 做為開端, 建立在具評等基礎下的違約損失模型, 並以投機等級違約率估計出移轉係數矩陣, 進而模擬各產業條件移轉矩陣, 藉以反應在各種不同總體情境下, 產業內各評等的移轉機率及違約機率。此外, 本文亦建立不分評等的簡化違約損失模型, 並將兩模型做一比較。最後, 我們以台灣537 家上市櫃公司做為投資組合樣本, 分別模擬出兩模型的條件違約損失分配。進一步計算風險指標,以此做為未來規劃資本計提的基礎。最後結果顯示, 投資組合違約情況確實受總體因子影響, 且發現若投資組合中評等越差公司之曝險越小, 將有助於降低組合資產風險。