Energy Security in Danger? A Comparative Analysis of Oil and Copper Supply

This study aims to examine energy security in terms of crude oil and copper supply. While oil remains the leading energy commodity globally, copper is crucial for many new technologies, foremost for RES. Therefore, both oil and copper are extremely important for current and future energy security. This article contains a bivariate methodological approach to a comparative analysis of oil and copper supply: determining supply security with an Index of security of supply, and examines price stability with generalized autoregressive conditional heteroscedasticity (GARCH) models. This research provides evidence that there are many differences but also significant similarities between these two completely different commodities in terms of both supply security and price stability. Facing the future for RES, significant demand may cause a threat to energy security on a previously unknown scale. Therefore this instability, both supply- and price-related, appears to be the main threat to future energy security.

How Does COVID-19 Affect the Volatility Spillover Between the Exchange Rate and the Export-oriented Businesses in Iran?

This study concentrates on examining the volatility spillover effects between the exchange rate (IRR to USD) and the leading export-oriented industries (i.e., petrochemical, basic metals and minerals) in Tehran Stock Exchange before and after the COVID-19 pandemic. Using DCC- and asymmetric DCC-GARCH approaches, the data sample (from 15 December 2018 to 24 April 2021) has been partitioned into two sub-samples: before and after the official announcement of COVID-19 outbreak. The results demonstrate that from the pre- to post-COVID-19 periods, first, the average returns of all industries have sharply fallen; second, the volatility of all variables has been significantly augmented in different horizons; third, for all industries, not only has the fractal market hypothesis approved in both separated periods, but also analysing the values of the fractional difference parameter, together with the outcomes of GARCH models, supports in the higher-risk post-COVID-19 period, wherein the effects of exogenous shocks last longer than their impacts in the alternative lower-risk period. Furthermore, our investigations demonstrate that the asymmetric spillover (based on the ADCC-GARCH models) in both pre- and post-COVID-19 periods are confirmed in all three industries, except for minerals after the novel coronavirus.Ultimately, the results not only corroborate the increase in the volatility spillover effects right after the COVID-19 but also substantiate that the exchange rate contributes most of the spillover effects into the petrochemical and minerals industries, which have been almost twice as much as those of the basic metals.

GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (GARCH) MODELS AND OPTIMAL FOR NIGERIAN STOCK EXCHANGE

This paper focused on comparative performance of GARCH models, ascertaining the best model fit, estimating the parameters and making prediction from optimal model. The study used UBA daily stock exchange prices sourced from the official websites of www.investing.com,on the daily basis of the Nigeria stock exchange rate over a period of ten years from 06/06/2012 – 04/06/2021. Five GARCH models (SGARCH, GJRGARCH or TGARCH, EGARCH, APGARCH and IGARCH) were fitted to the secondary data set of the Nigerian Stock exchange market for the period of June 2012- June 2021 and the results of the findings were obtained. The AIC results were SGARCH (1,1) (-6.1784), GJRGARCH (1,1) (-6.1778), EGARCH (1,1) (-6.1714) , APGARCH (1,1) (-6.1245) and IGARCH(1,1) with the value of AIC -6.1793. The EGARCH (1, 1) was found to be the optimal model with AIC value of -6.1714. The further findings indicated volatility clustering and leverage effect. The result of the analysis equally showed parameter estimates of the EGARCH (1,1) model and all the parameters were significant including mean and alpha. Prediction using the optimal model was made with an initial out of sample of 200 and n ahead of 200 with predicted values within the 95% confidence interval resulting there is no sign of volatility and clustering. Based on the findings of the study, other time series packages should be compared with GARCH models, data should be making available for easy access and investors should be encouraged to invest in United Bank for Africa (UBA, Nigeria).

THE SPILLOVER, RISK AND LEVERAGE EFFECTS OF SMART BETA MANAGEMENT EXCHANGE-TRADED FUND (ETF)

This research uses two different GARCH models to measure spillover, risk, and leverage effects of active, passive, and smart beta management Exchange-traded Funds (ETFs). The increase in popularity of ETFs and new categories within them, specifically the growth of smart beta management, means asset managers and investors have new metrics to account for when determining portfolio exposure following the Adaptive Investment Approach (AIA). The results show significant relationships among all groups regarding the spillover. A trend of positive multi-lateral spillover of returns among the three management types including passive, active and small beta is observed with smart beta showing the highest percentage of a bi-lateral positive effect. The strongest spillover of volatility effects is among the actively managed ETFs. The testing of risk results is insignificant, but the leverage effect results are consistent with the past studies showing the significant negative bi-lateral effect.

Does the short squeeze lead to market abnormality and antileverage effect? Evidence from the Gamestop case

PurposeThis study examines the Gamestop (GME) short squeeze in early 2021. Using intraday data for the period 4/1/2021–5/2/2021, the author provides empirical evidence that the GME stock price exhibited abnormal behavior.Design/methodology/approachThe author uses the popular Runs test to show that the GME returns were not randomly distributed, which is an indication of a violation of the Efficient Market Hypothesis (EMH). The main objective of the paper is to provide new quantitative evidence that stock returns are abnormal when short squeeze conditions emerge. The author employs the asymmetry Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models (the Exponential GARCH (EGARCH) and the Threshold GARCH (TGARCH)) and provides evidence that an exceptional time series feature emerged during the examined period: the antileverage effect.FindingsThe results show that the GME returns were not randomly distributed during the examined period and the asymmetry GARCH models indicate that, in contrast to what the time series normally show, volatility increased when the GME prices increased.Research limitations/implicationsThis paper presents a new/alternative approach for the study of EMH and abnormal returns in financial markets. Further studies on market performance during similar short squeeze conditions should be carried out in order to obtain empirical evidence for the antileverage effect abnormality.Practical implicationsThis paper could be useful for scholars who examine the EMH in financial markets because it suggests an additional method for testing abnormalities. It also presents a useful tool that allows practitioners to monitor for indications of abnormality in the stock market during a short squeeze, since the emergence of the antileverage abnormality could function as such an indication. Additionally, the outcome of this analysis could be useful for regulators because coordination among investors is easier than ever in the Internet era and such events may happen again in the future; even under normal (not short squeeze) conditions and lead to market instability.Originality/valueThis research differs from other studies that examine the GME case because it presents a new way to quantitatively present the abnormal performance of the stock markets for reasons that could be linked with the emergence of short squeeze conditions.