Testing Leverage and Spillover Effects in Precious Metal ETFs

Jo-Hui Chen,Thi Van Trang Do

doi:10.4236/tel.2018.83015

Abstract

This research employed the 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 investigate the spillover and leverage effects in the returns and volatilities of precious metal (base metal) ETFs. Significant positive relationships were found between precious metal (base metal) ETFs and precious metal (base metal) price indices. Further, the positive relationship between risk and return was illustrated in daily precious metal (base metal) ETFs.

Highlights

Exchange-traded funds (ETFs) are known as financial innovations that have demonstrated tremendous international growth since ETFs were being initially introduced in 1993
The spillover and leverage effects on returns and volatilities are captured by utilizing generalize autoregressive conditional heterokadasticity in mean (GARCH-M)-autoregressive moving average (ARMA) and EGARCH-M-ARMA models between precious metal ETFs and precious metal prices
The evidence of positive unilateral relationships in returns is represented by the Silver Trust (SLV), Palladium Shares (PALL), and JJC, whereas other precious metal performed bilateral positive relationship with current price returns

Summary

Introduction

Exchange-traded funds (ETFs) are known as financial innovations that have demonstrated tremendous international growth since ETFs were being initially introduced in 1993. We present the empirical evidence that ETFs and their precious metal indices represent unilateral or bilateral influence through positive and negative effects on each other. The purpose of this study is to explore the spillover and leverage effects from the returns and volatilities of precious metal ETFs and precious metal indices and vice versa. This study utilized both the generalize autoregressive conditional heterokadasticity in mean (GARCH-M)-autoregressive moving average (ARMA) models and the exponentially generalized autoregressive conditional heterokadasticity in mean (EGARCH-M)-autoregressive moving average (ARMA) models.

Literature Review

Data and Methodology

Empirical Results

Conclusions