Oil and gold are used as investment assets and so they are closely related to the evolution of stock market indices, given that any influence on decisions about investment portfolios can affect stock market returns. Consequently, it is important for investors to analyze the direction of influence between crude oil, gold and stock markets when designing and implementing their investment strategies.Thus, this paper studies the direction of the causality among the three markets for the US case. In doing so, we apply linear and non-linear Granger causality tests for daily data from the Great Moderation onwards. Evidence is provided as to the importance of considering the possibility of nonlinear relationships between the three markets, a feature which cannot be revealed using conventional linear causality tests which would therefore lead to an information loss about the true link.The results for the full sample indicate that the causality goes in all directions, which implies that changes in the stock market returns may be monitored by observing changes in the returns of the two commodity markets considered (and vice versa). This may help to design substitution investment strategies. The results also indicate that the direction of influence between markets does not exhibit material differences between various subsamples, with the exception of the causality relationship between the two commodity markets. This may in part help explain the contradictory results and mixed conclusions found in previous related literature.
In this paper we investigate the adaptive market efficiency of the agricultural commodity futures market, using a sample of eight futures contracts. Using a battery of nonlinear tests, we uncover the nonlinear serial dependence in the returns series. We run the Hinich portmanteau bicorrelation test to uncover the moments in which the nonlinear serial dependence, and therefore adaptive market efficiency, occurs for our sample
1 January 2019 in Afro-Asian J. of Finance and Accounting
The study empirically investigates the adaptive market hypothesis (AMH) in the Pakistan stock market over the period of 1992 to 2015. Daily data of returns (KSE-100) is divided into eight sub-samples of equal length of three years each and into different market conditions and are subjected to linear/nonlinear tests to elucidate how market-efficiency has behaved over time and whether a relationship exists between market conditions and levels of return predictability. The tests reveal that returns have gone through periods of dependence and independence over eight sub-samples thus Pakistan Stock Exchange is an adaptive market and consistent with the AMH. Furthermore, certain market conditions are more conducive to the predictability of returns as market conditions have also gone through episodes of significant dependence and independence of return predictability, which is also consistent with the AMH. Therefore, overall results of the study suggest that the AMH better elucidates the behaviour of stock returns than conventional efficient market hypothesis (EMH).
21 April 2017 in International Journal of Pure and Apllied Mathematics
The relationship between three different groups of COVID-19 news series and stock market volatility for several Latin American countries and the U.S. are analyzed. To confirm the relationship between these series, a maximal overlap discrete wavelet transform (MODWT) was applied to determine the specific periods wherein each pair of series is significantly correlated. To determine if the news series cause Latin American stock markets’ volatility, a one-sided Granger causality test based on transfer entropy (GC-TE) was applied. The results confirm that the U.S. and Latin American stock markets react differently to COVID-19 news. Some of the most statistically significant results were obtained from the reporting case index (RCI), A-COVID index, and uncertainty index, in that order, which are statistically significant for the majority of Latin American stock markets. Altogether, the results suggest these COVID-19 news indices could be used to forecast stock market volatility in the U.S. and Latin America.