外溢效果、結構改變和預測的研究:以消費性 ETF為例

Abstract

The aim of this research is to close the gap in the literature of the spillover, the long memory, volatility and forecasting for consumer exchange-traded funds (ETFs). This research is divided in to three parts. The first part focuses on spillover and leverage effects of Consumer ETFs (Consumer Discretionary and Consumer Staples) and Producer Related ETFs. This study used Generalized Autoregressive Conditional Heteroskedasticity-in-Mean Autoregressive Moving Average (GARCH-M-ARMA) and found a bilateral correlation between Consumer ETFs and Producer Related ETFs tracing fundamental indexes. With Exponentially Generalized Autoregressive Conditional Heteroskedasticity-in-Mean Autoregressive Moving Average (EGARCH-M-ARMA) models, this paper found that Producer Related ETFs have less spillover effects for compared with Consumer ETFs. There were strongly negative leverage effects of both consumer and Producer Related ETFs. The second part forecasts consumer exchange-traded funds (ETFs) which classified by country, such as the United States (US), excluding the United States (EX-US), Emerging Markets, Brazil, China, and India. The findings of Grey Relational Analysis (GRA) showed that there are top four ranking to influence Consumer ETFs, such as New York Stock Exchange Composite Index, Commodity Research Bureau, exchange USD/EUR and Put/call ratio. Artificial Neural Network (ANN) approach connected with all data and variables revealed that Back-Propagation Perception (BPN) can be much more effective for prediction. However, based on different sample this paper found that Time-Delay Recurrent Neural Network (TDRNN) and Radial Basis Function Neural Network (RBFNN) provide consistent results. ANN model also found that Brazil and China Consumer ETFs can be easier to predict, comparing with others countries. The methods used in the third part is known as Autoregressive Fractionally Integrated Moving Average which found that media, consumer service, food and beverage and consumer goods industries of Consumer ETFs returns can be a good prediction. Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity (ARFIMA-FIGARCH) model revealed that the long memory in volatility existed only for gaming and consumer goods industries. Moreover, there are multiple structural breaks for asymmetrical effects in Consumer ETFs by applying the Iterated Cumulative Sums Squares Test (ICSS). The outcome of this research will not only offer economic meaning for issuers, investors and fund managers to plan for their trading strategies, but also provide for academicians and researchers stepping stones in having empirical results and a new perceptive from Consumer ETFs.