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

Chang, Chia‐Lin; McAleer, Michael; Wang, Chien-Hsun

doi:10.20944/preprints201709.0035.v1

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Tokat and Tokat (2010) highlight the interdependence of the volatility of futures and spot markets in the Turkish stock exchange. Chang et al (2018) discuss the integration between the futures markets of exchange traded funds (ETFs) and the energy and financial markets.…”

Section: Review Of Literaturementioning

confidence: 99%

Volatility Integration in Spot, Futures and Options Markets: A Regulatory Perspective

Rastogi

Athaley

2019

JRFM

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The aim of this paper is to study the integration of volatility in the three markets, viz. spot, futures and options, in order to provide input for hedging purposes and the formulation of policies for derivatives. The generalized method of moments (GMM) is used to capture the simultaneous equation modelling of volatility in the three markets. The integration of the volatility in the three markets is also tested for structural breaks. The main finding of the paper is that the volatility in the options market is not associated with volatility in spot and futures market. However, volatility in spot and futures markets are associated with each other. As a consequence, investors can use options for hedging purposes and policy makers do not need to be concerned about the imminent impact of options markets on spot markets. To the best of the authors’ knowledge, there is no other study which discusses the integration of volatility in the three markets. Moreover, the finding of this paper that the options market behaves differently compared to the futures market has also not been discussed in earlier studies.

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Section: Review Of Literaturementioning

confidence: 99%

Volatility Integration in Spot, Futures and Options Markets: A Regulatory Perspective

Rastogi

Athaley

2019

JRFM

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“…Alexopoulos (2018) reviewed energy ETF performance and found that all ETFs outperform. Chang et al (2018) used generated regressors and a multivariate conditional volatility model to examine the spillover effects of the US energy and financial sectors in the spot and futures markets. The empirical evidence indicates a positive impact between the financial and Energy ETF.…”

Section: Literature Reviewmentioning

confidence: 99%

Jump Dynamics and Leverage Effect: Evidences from Energy Exchange Traded Fund (ETFs)

Chen¹,

Hussain²

2022

Journal of Applied Finance &Amp; Banking

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This paper is concerned with the behavior of energy ETF prices. It applies three models: autoregressive moving average (ARMA) and generalized autoregressive conditional heteroskedasticity (GARCH), along with their revised forms, ARMA–Exponential-GARCH, Glosten-Jagannathan-Runkle (GJR), and GARCH diffusion process with jump models. This study looks at the volatility behavior and jumps dynamics of Energy and Master Limited Partnership's (MLP) ETFs. The results show that ARMA-GARCH is appropriate for modeling energy and MLP ETFs. Both ETFs offer positive leverage and asymmetric volatility. The results show that the jump model with a GARCH volatility specification has an actual amount of jump presence and time variation in the jump size distribution. The conclusion of the ARMA - EGARCH model gives evidence of the reverse leverage effect. The leverage term positively influences the conditional variance, while the asymmetry coefficient for the GJR model is positive and significant. These results reveal that both Energy and MLPs ETF have high volatility. JEL classification numbers: F3. Keywords: Energy ETFs, MLPs, ARMA-GARCH model, Volatility Asymmetry, Leverage and Jump Effect.

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Is Technical Analysis Profitable on Renewable Energy Stocks? Evidence from Trend-Reinforcing, Mean-Reverting and Hybrid Fractal Trading Systems

Nor

Zawawi

Wickremasinghe

et al. 2023

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Demand for power sources is gradually shifting from ozone-depleting-substances towards renewable and sustainable energy resources. The growth prospects of the renewable energy industry coupled with improved cost efficiency means that renewable energy companies offer potential returns for traders in stock markets. Nonetheless, there have been no studies investigating technical trading rules in renewable energy stocks by amalgamating fractal geometry with technical indicators that focus on different market phases. In this paper, we explore the profitability of technical analysis using a portfolio of 20 component stocks from the NASDAQ OMX Renewable Energy Generation Index using fractal dimension together with trend-reinforcing and mean-reverting (contrarian) indicators. Using daily prices for the period 1 July 2012 to 30 June 2022, we apply several tests to measure trading performance and risk-return dynamics of each form of technical trading system—both in isolation and simultaneously. Overall, trend (contrarian) trading system outperforms (underperforms) the naïve buy-and-hold policy on a risk-adjusted basis, while the outcome is further enhanced (reduced) by the fractal-reinforced strategy. Simultaneous use of both trend-reinforcing and mean-reverting indicators strengthened by fractal geometry generates the best risk-return trade-off, significantly outperforming the benchmark. Our findings suggest that renewable energy stock prices do not fully capture historical price patterns, allowing traders to earn significant profits from the weak form market inefficiency.

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

Cited by 3 publications

References 11 publications

Volatility Integration in Spot, Futures and Options Markets: A Regulatory Perspective

Volatility Integration in Spot, Futures and Options Markets: A Regulatory Perspective

Jump Dynamics and Leverage Effect: Evidences from Energy Exchange Traded Fund (ETFs)

Is Technical Analysis Profitable on Renewable Energy Stocks? Evidence from Trend-Reinforcing, Mean-Reverting and Hybrid Fractal Trading Systems

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