Search Results (4)

This study explores the international natural gas market integration using the Engle–Granger cointegration and error correction model. Previous studies have suggested that liquefied natural gas (LNG) and oil-linked pricing with a long-term contract have played key roles in gas market integration, especially between European and Asian markets. There is, however, little discussion of the role of the emergence of a swing supplier. A swing supplier, e.g., Qatar or Russia, is flexible to unexpected changes in supply and demand in both European and Asian markets and adapts the gas production/exports swiftly to meet the changes in the markets. Qatar has been a swing supplier since 2005 in the global natural gas market. In 2009, Qatar’s global LNG export share reached above 30% and has remained around 25% since then. Empirical results indirectly support that the emergence of a swing supplier may tighten market integration between Europe and Asia. The swing supplier may have accelerated the degree of market integration as well, particularly after 2009. Full article

This study applies machine learning methods to develop a sustainable pairs trading market-neutral investment strategy across multiple futures markets. Cointegrated pairs with similar price trends are identified, and a hedge ratio is determined using an Error Correction Model (ECM) framework and support vector machine algorithm based upon the two-step Engle–Granger method. The study shows that normal backwardation and contango do not consistently characterize futures markets, and an algorithmic pairs trading strategy is effective, given the unique predominant price trends of each futures market. Across multiple futures markets, the pairs trading strategy results in larger risk-adjusted returns and lower exposure to market risk, relative to an appropriate benchmark. Backtesting is employed and results show that the pairs trading strategy may hedge against unexpected negative systemic events, specifically the COVID-19 pandemic, remaining profitable over the period examined. Full article

This paper analyzes the effects of the real policy interest rate on the banking sector lending rate, the deposit rate, real stock prices, and the real exchange rate using the Engle Granger cointegration method (EG), the vector error-correction model (VECM), and the nonlinear vector error-correction model (NVECM) with monthly Turkish data over the period January 2002–April 2018. (1) EG results indicate bivariate cointegration relationships between the real interest rate, lending rates, and the deposit rate. The real interest rate increases all lending rates, mainly the housing rate. However, the long-run coefficient for the real exchange rate is not statistically significant. The pass-through is higher for the deposit rate than for lending rates. Moreoever, real stock prices shrink substantially where the finance sector has been affected the most. (2) VECM results indicate a cointegration relationship between all the variables except for the real exchange rate, which has a statistically non-significant pass-through coefficient. The real interest rate has a noteworthy long-run positive effect on the housing loans lending rate compared to others. The affirmative effect on real stock prices is the highest for the technology sector. The short-run effect of the real interest rate on lending rates, real stock prices and the real exchange rate are statistically non-significant except for the overall stock price index, and the vehicle loans lending rate which has a higher coefficient than the deposit rate. (3) NVECM results allow testing of eleven hypotheses and highlight the symmetric relationship and the valid pass-through effect, and reject the strong exogeneity assumption for all variables. Full article

The generalized method of moments (GMM) estimator of the reduced-rank regression model is derived under the assumption of conditional homoscedasticity. It is shown that this GMM estimator is algebraically identical to the maximum likelihood estimator under normality developed by Johansen (1988). This includes the vector error correction model (VECM) of Engle and Granger. It is also shown that GMM tests for reduced rank (cointegration) are algebraically similar to the Gaussian likelihood ratio tests. This shows that normality is not necessary to motivate these estimators and tests. Full article