台灣地區金融市場波動性實證研究

Abstract

預測金融市場的波動性(volatility)非常重要，因為能否準確的預測波動對於風險管理的成敗扮演著關鍵性的角色。波動性是無法由金融市場所提供的資訊得知，然而我們可使用頻繁交易的資料來表示此一無法觀測的值，這也開啟了預測未來波動性的新視野。我們利用以日內報酬所得到的realized volatility當作真實波動以與時間序列的方法做出的模型做比較。本論文主要的目的是──以台灣的資料找出能有效預測波動性之模型、計算真實的波動性以用來代表無法直接觀測到的波動性、以三種不同的方式分析出最可靠的波動預測模型、並測試出何時GARCH(1,1)模型需要重新估計參數。為此，本文利用一些統計中的方法和時間序列模型去預測金融市場的日波動。

Forecasting volatility plays an important role in financial market, because volatility predictions are crucial for the successful implementation of risk management. The use of high frequency data approximately renders volatility from a latent to an observable quantity, and opens the new field of visions to forecast future volatilities. Use of realized volatility constructed from high-frequency intraday returns, in contrast, permits the use of traditional time-series methods for modeling and forecasting. Main goals of this thesis are to find general and powerful forecasting procedures for volatilities based on Taiwan high frequency data, to evaluate the predictive potential of volatility forecasts for the true latent volatility, to analyze the impact of more reliable volatility predictions on the quality of three widely used risk measures, and to test for parameter changes in the GARCH(1,1) models. For that purpose, this thesis explores some statistical models for predicting the daily volatility of financial time series.