Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
By modelling dynamism in the global oil market by three essential market-centric observables (viz., Market Expansion, Market Regime, and Market Liquidity), we study forecasting potential of the future oil markets within a memory-driven interdependence setting. We combine spot prices with our derived market proxies to produce representative global market proxies. The latter are used to quantify the extent of static and dynamic persistence within the system. Our extensive empirical investigation exploits the rich features of fractionally cointegrated vector autoregression, where the rate of disequilibrium error correction within the system is modelled to be slow, approximating real life system dynamics. An advantage is that it explains why we often experience a slow response of a policy intervention. We present robust evidence of both system-wide long-memory and a long-memory in the market-centric observables. We introduce a memory of memory estimation to discern the magnitude of the relative rate of acceleration/deceleration of shocks within each observable, which reflects on the overall stability of the system. Our results show significant degree of non-linear error dissipation and high degree of informational inefficiency. Rigorous out-of-sample forecasting exercise produces robust predictions and demonstrate superiority of our approach.