Energy Exchange Traded Funds Research Articles

An Econometric Analysis of ETF and ETF Futures in Financial and Energy Markets Using Generated Regressors

It is well known that there is an intrinsic link between the financial and energy sectors, which can be analysed through their spillover effects, which are measures of how the shocks to returns in different assets affect each other’s subsequent volatility in both spot and futures markets. Financial derivatives, which are not only highly representative of the underlying indices, but can also be traded on both the spot and futures markets, include Exchange Traded Funds (ETFs), a tradable spot index whose aim is to replicate the return of an underlying benchmark index. When ETF futures are not available to examine spillover effects, “generated regressors” are useful for constructing both financial ETF futures and energy ETF futures. The purpose of the paper is to investigate the co-volatility spillovers within and across the U.S. energy and financial sectors in both spot and futures markets, by using “generated regressors” and a multivariate conditional volatility model, namely diagonal BEKK. The daily data used are from 23 December 1998–22 April 2016. The dataset is analysed in its entirety and is also subdivided into three distinct subsets. The empirical results show there is a significant relationship between the financial ETF and energy ETF in the spot and futures markets. Therefore, financial and energy ETFs are suitable for constructing a financial portfolio from an optimal risk management perspective and also for dynamic hedging purposes.

Tracking Performance of Leveraged Energy Exchange-Traded Funds

The population of exchange-traded funds (ETFs) has exploded in recent years, with ETFs in a vast array of portfolios tied to specific asset classes, industries, active and passive trading strategies, and so on. In some cases, there are multiple leveraged long and short versions based on the same underlying factor. In this article, Tang and Xu examine nine leveraged ETFs tied to oil. Five are based on portfolios of oil stocks, and the other four use commodity futures to track the price of oil itself. Two main questions are: How correlated are the ETFs with the underlying energy risk factors they are designed to hedge? And, do the leveraged versions succeed in producing their target multiples of the exposure to the oil market? The authors find stock-based ETFs are much more correlated with the stock market than with oil prices, whereas the reverse is true of the crude oil ETFs. But all of the ETFs tend to fall short of their intended multiples, especially over longer holding periods, for several reasons. Tang and Xu decompose the shortfall to see how much is due to the product’s inherent convexity, which causes compounding to produce underperformance; how much comes from discrepancy between the ETF’s market price and its net asset value; and how much is the result of management’s inability to hit their target return even on a single day. All three factors are important and only the “inefficiency” difference between ETF price and NAV (net asset value) does not cumulate over longer holding periods.

Tracking Performanceof Leveraged Energy Exchange-Traded Funds

Tracking Performanceof Leveraged Energy Exchange-Traded Funds

The Study of the Long Memory in Volatility of Renewable Energy Exchange-Traded Funds (ETFs)

This research applied original price return and adjustment price return for both renewable and unrenewable energy ETFs. Comparing the long memory in volatility and asymmetric volatility of renewable and unrenewable energy ETFs, this study used three models, fractional autoregressive integrated moving average (ARFIMA), a combination of ARFIMA and fractionally integrated exponentially generalized autoregressive conditional heteroscedasticity (ARFIMA-FIEGARCH) and ARFIMA with hyperbolic generalized autoregressive conditional heteroscedasticity (ARFIMA-HYGARCH) models. The results show that by using the adjustment price return data samples, then the results are similar with original price return ETFs. Both unrenewable and renewable energy ETFs have a long memory in volatility and negative asymmetric volatility. ARFIMA-FIEGARCH model perform better to investigate long memory in volatility and asymmetric volatility for both energy ETFs among others.

TESTING PRICE PRESSURE, INFORMATION, FEEDBACK TRADING, AND SMOOTHING EFFECTS FOR ENERGY EXCHANGE TRADED FUNDS

This paper examines the relationships between flows and returns for five exchange traded funds (ETF) in the U.S. energy sector. Four alternative hypotheses are tested, including the price pressure hypothesis, information (or price release) hypothesis, feedback trading hypothesis, and smoothing hypothesis. The five ETF are the Energy Select Sector SPDR Fund (XLE), iShares U.S. Energy ETF (IYE), iShares Global Energy ETF (IXC), Vanguard Energy ETF (VDE), and PowerShares Dynamic Energy Exploration & Production Portfolio (PXE). A vector autoregressive (VAR) model is used to analyze the relationships between energy flows and returns. The empirical results show that energy ETF flows and subsequent returns have a negative relationship, thereby supporting the smoothing hypothesis. Moreover, the smoothing effect exists for XLE and IYE during the global financial crisis. Regardless of whether the whole sample period or the sub-samples before, during and after the global financial crisis are used, no evidence is found in support of the price pressure hypothesis, information hypothesis, or feedback trading hypothesis.