貴金屬指數型基金 (ETF) 評價: 以槓桿效應、外溢效果、動態波動和預測之檢測為例

Abstract

This dissertation investigates the leverage effect, spillover effect, volatility dynamic and forecasting for the innovation investment instrument, exchange-traded funds (ETFs), especially precious metal ETFs. The research is implemented based on actively precious metal ETFs through three essays with the inception date from 2005. The first essay provides evidence on long memory properties in volatilities of precious metal (base metal) ETFs by applying the Autoregressive Fractional Integrated Moving Average-Fractional Integrated Asymmetric Power Autoregressive Conditional Heteroskedasticity (ARFIMA-FIAPARCH). The strong evidence of long memory has been performed for both precious metal and base metal ETFs. The appearance of significant volatility asymmetry represents that positive news has stronger effect comparing to negative news. Moreover, this essay employs Generalized Autoregressive Conditional Heteroskedasticity-in-Mean-Autoregressive Moving Average (GARCH-M-ARMA) and the Exponentially Generalized Autoregressive Conditional Heteroskedasticity-in-Mean-Autoregressive Moving Average (EGARCH-M-ARMA) models to exploit the unilateral and bilateral positive relationship between precious metal (base metal) ETFs and precious metal (base metal) price indexes. Results indicate that the significant positive effects of lagged precious metal (base metal) price returns on current precious metal (base metal) ETF returns and vice versa. The second essay uses three Multivariate General Autogressive Conditional Heterokedasticity (MGARCH) models to model conditional correlations and analyzes the robust check the volatility spillovers between precious metal (base metal) ETFs and futures indexes. Baba, Engle, Kraft and Kroner (BEKK) model is recognized to fit data the best and represented the long-run persistence; the shocks on volatility of precious (base) metal ETFs might have impact on their futures contracts through range of a long time. The significance results exploit that the lagged covariances and lagged cross-products of the shocks are presented. Thus, the volatilities of precious metal (base metal) ETF returns have influenced on their futures price returns. The third essay applies chaos effect, grey relational analysis (GRA) and artificial neural network (ANN) to forecast the return volatility of precious metals and precious metal ETFs. The chaotic behavior is found in these data sets while using Brock Dechert Scheinkman (BDS) test, the rescaled range (R/S) analysis and correlation dimension analysis. The results showed that all the data series performed deterministic chaos. Precious metal and precious metal ETFs have represented random process and nonlinear properties. The West Texas Intermediate (WTI) index shows the greatest influence on forecasting precious metals and precious metal ETFs, followed by stock index, exchange rate, commodity research bureau (CRB) index, volatility index, interest rate and put-call (P/C) ratio. Moreover, the backpropagation network (BPN) model is the most powerful model among four ANN models like BPN, radial basic function (RBP), recurrent neural network (RNN) and time-delay recurrent neural network (TDRNN) in forecasting precious metals and precious metal ETFs. “All variables” group has stronger influence than high- or low-grey relational grade (GRG) variables for predicting precious metals. Whereas, some precious metal ETFs have better forecasting results by using “all variables” group and some others by utilizing high-GRG variables. Therefore, investors and traders can get profit through linkage seven determinants in forecasting precious metals and precious metal ETFs.