Price Leadership and Volatility Linkages between Oil and Renewable Energy Firms during the COVID-19 Pandemic

The COVID-19 pandemic is having a strong influence in all areas of society, like wealth, economy, travel, lifestyle habits, and, amongst many others, financial and energy markets. The influence in standard energies, like crude oil, and renewable energies markets has been twofold: from one side, the predictability of volatility has strongly decreased; secondly, the linkages of the price time series have been modified. In this paper, by using DCC-GARCH and Price Leadership Share methodology, we can investigate the changes in the influences between standard energies and renewable energies markets by analyzing one-minute time series of West Texas Intermediate crude oil futures contract (WTI), the Brent crude oil futures contract (BRENT), the STOXX Europe 600 oil & gas index (SXEV), and the European renewable energy index (ERIX). Our results confirm volatility spillover between the time series. However, when assessing the accuracy of the predictability of the DCC-GARCH model, the results show that the model fails its prediction in the period of higher instability. Besides, we found that price leadership has been strongly influenced by the virus spreading stages. These results have been obtained by dividing the period between September 2019 and January 2021 into 6 subperiods according to the pandemic stages.

Commodity Transaction Tax (CTT)

This study aims to examine the time-varying correlations and volatility linkages between commodity and equity markets before and after the implementation of the commodity transaction tax (CTT) in India in 2013. The study utilizes symmetric and asymmetric DCC-EGARCH model to estimate correlation dynamics. Evidence suggests that the volatility and dynamic correlation linkages between commodities and equity markets are significantly affected by the triggering events. The time-varying correlations of Comdex-Nifty 50 show an unintended steep decline in the post-CTT period. It is an indication of a “flight to quality” phenomenon, where investors move capital from non-agricultural commodity futures to other cross markets and international markets. However, DCC of Comdex-Dhaanya pair is highly volatile in the post-CTT period and also noticed an increased correlation and volatility between the Dhaanya-Nifty 50 pair. Moreover, the correlation dynamics reveal a certain degree of interdependence between the cross markets, which are lower especially during the triggering episodes.

Optimum investor portfolio allocation in new age digital assets

Purpose Bitcoin and Ethereum, although the most prominent cryptocurrencies, carry a high ticker price. Many investors carry an inherent bias against high price ticker securities and prefer only low prices securities. This paper aims to help market players generate adequate risk-adjusted returns by investing in only lower-priced cryptocurrencies. Design/methodology/approach The pairwise bivariate BEKK-GARCH (1,1) model is deployed to capture the short- and long-term volatility linkages between Litecoin, Stellar and Ripple from August 2015 to June 2020. Findings Litecoin is the most influential volatility sender in the basket of these three cryptocurrencies. The portfolio weights indicate that investors can create an optimized two asset portfolio with the lowest exposure to Stellar with Litecoin and Ripple. Market players with a long position in Ripple can have the cheapest hedge by shorting Stellar. Originality/value This study adds to the scant literature on the association between emerging cryptocurrencies and finding optimum portfolio weight and hedge ratios.

Information and volatility linkages across energy and financial markets

This study examines information and volatility linkages across energy and financial markets. In a world economy so connected, the impacts of climate change are likely to be transmitted through interlinked global markets. Hence, uncovering and understanding the interaction across these markets is a fundamental concern during the energy transition as it helps to understand how to strengthen incentives to facilitate energy investments. Based on the relation between information flows and volatility, this study employs a simple correlation approach based on implied volatility measures and the trading model of Fleming et al. to measure the common information linkages, as gauged by the correlation of return volatilities. The results suggest that volatility linkages across these markets are strong due to common information sharing and cross-market hedging. JEL Classification: G12, G14

Price and Volatility Linkages Between Indian Stocks and Their European GDRs

This article tests for price and volatility linkages between Indian global depositary receipts (GDRs) traded in Luxembourg/London and their underlying shares traded in Mumbai. The relationship is studied between the GDR price and the domestic share price along with the appropriate exchange rates, the foreign stock index and the domestic stock index using the vector autoregression (VAR) and dynamic conditional correlation (DCC) specification of multivariate generalised autoregressive conditional heteroscedasticity (GARCH) models. VAR results indicate a similarity between the two prices of scrips: one trading in Mumbai and the other trading in Luxembourg (London). Further, DCC-GARCH model outcomes point to, by and large, a high-dynamic correlation between Indian GDRs traded in Luxembourg/London and their underlying stocks listed in Mumbai. Thus, the price and volatility linkages between the Indian stock and its European counterpart are invariant with respect to the choice of the foreign stock exchange. Such a similarity in findings, notwithstanding the difference in degree of information disclosure as well as listing requirements at London and Luxembourg, is perhaps indicative of the stock-exchange-invariant nature of law of one price. JEL Classification: G15, C22