M Sy

Shale Revolution and Shifting Crude Dynamics

Crude prices are subject to both demand and supply shocks. Major events and structural changes can induce large variations in intensities of the two types of shocks, as well as their magnitudes of impacts on crude price movements. This paper proposes a theoretical framework that allows us to extract the time variation in demand and supply shocks through a joint analysis of crude futures options and stock index options. Historical analysis shows that crude futures price movements are dominated by supply shocks in the earlier half of our sample from 2004 to 2008, but have become much more demand-driven since then. The large demand shock from the Great Recession, triggered by the 2008 financial crisis, contributes to the start of the dynamics shift. The shale revolution, on the other hand, has fundamentally altered the crude supply behavior. Since 2010, technology advances in horizontal drilling and hydraulic fracturing, together with other innovations, have enabled rapid increase of U.S. tight oil production from shale at increasingly competitive cost. The increasing tight oil production has undercut the price-setting power of the OPEC, and has lowered the OPECs incentive to self-regulate its production. As a result of the dynamics shift, investors have also been shifting from worrying about crude price hikes as a production cost gauge to crude price drops as an indication of weakening demand. The shifting dynamics have fundamental implications for optimal fuel cost hedging by heavy crude users such as the airline industry.

Comparative study of volatility forecasting models: the case of Malaysia, Indonesia, Hong Kong and Japan stock markets

This paper aims to investigate the effectiveness of four volatility forecasting models, i.e. Exponential Weighted Moving Average (EWMA), Autoregressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedastic (GARCH), in four stock markets Indonesia, Malaysia, Japan and Hong Kong. Using monthly closing stock index prices collected from 1st January 1998 to 31st December 2015 for the four selected countries, results obtained confirm that volatility in developed markets is not necessarily always lower than the volatility in emerging markets. Among all the three models, GARCH (1, 1) model is found to be the best forecasting model for stock markets in Malaysia, Indonesia, and Japan, while EWMA model is found to be the best forecasting model for Hong Kong stock market. The outperformance of GARCH (1, 1) found supports again what is found in Minkah (2007).