2.0 THEORETICAL CONCEPTS AND MARKET INTEGRATION
2.3.0 Summary and Concluding Remarks
In sum, the above theoretical review shows that market integration is a broad concept and hence its definition can be vague. For instance tradability represented by physical trade flow is sufficient to imply MI but without price transmission such approach can be very biased since tradability can also imply information flow between markets without physically observing trade.
3 Since in practice, many market data have strong lag relations, reducing the frequency implies, throwing away about a third of already relatively short commodity market series. Given the relatively many regimes implied by the structure of the PBM as a regime switching model, results can easily be influenced by transient shocks, if they are not fairly distributed over time. Moreover, since segmentation implies a form of random walk process, lower frequency data may miss some crucial periods.
MI can again hold without price transmission when threshold effect persists. Outcome-based notions tend to be less informative in policy wise but do distinguish between the various interconnected economic concepts that define markets behaviours over time. The broadness of the concept usually results in situation where each of the divergent measurement approaches, tends to work well under one case but sometimes inconsistent in the other.
Since the past decade, the growing advances in market integration and price transmission measurements have generated popular view that the traditional models for commodity markets integration analysis within a linear setting are inconsistent under many real world situations in explaining observed movements in market phenomena. The diversion to recent non-linear and regime switching versions indicate a direction to the domains of models with implicit assumption of multiple equilibria, since MI and ME are intrinsically interlinked. As implied by ESTJ spatial equilibrium theory, and the fact that many commodity markets and trade regions are characterised by changing policy schemes, technological innovations on transactions cost and their associated uncertainties on market decisions, make it quite appealing to suspect that many commodity markets would relate differently in particular periods of large transactions cost, more liberalized market schemes, policy uncertainties and in strategic planning phases in time. In applied economics in general, presence of such features make it difficult to explain aggregate long-run behaviour using traditional linear models. Hence the important contribution made by the PBM and other regime switching tools in particular as they possess much flexibility to accommodate various theoretical views that underpin markets inter-relationships.
From the review, it is worth noting that while price formation structures dwell heavily on demand and supply interactions, it is only under (perfect) competitive market equilibrium that one can both assume long-run measure of no-arbitrage and efficiency. In effect, though market prices are outcomes of a process (interactions among many market variables) through demand and supply mechanisms and as such contain richness of market information, the many implied information from market prices are unobserved and dependent on the underlying market equilibrium condition. Price transmission analysis that do not accommodate all the possible underlying equilibrium conditions tend to address a particular form of MI and may be biased where other conditions hold.
Nevertheless, PTE readily reveal both theoretical and policy implications of market dynamics over time. In fact, the degree and speed of price adjustment processes to re-establishing equilibrium can, to some extent, help understand how markets function efficiently as well as how theoretical scenarios are gauged through empirical findings. In many cases where competitive equilibrium are expected, price transmission analysis -time series econometrics or partial equilibrium models- have played a major role in policy prescription and addressing distributional issues of welfare effects from market policy scenarios. But since in competitive equilibria analysis, comparative static long or short-run equilibrium implies a dynamic process, market efficiency is concerned with whether optimal amount of trade is occurring to ensure price differentials that result from demand and supply shocks are exhausted. Within cointegration and error correction frameworks insights about both short and long run inter-markets relations are provided under perfect competitive assumptions.
The distinction between MI and competitive ME is important for meaningful MI analysis, especially with limited information on tradability. Thus, though the two concepts are intrinsically intertwined they can imply different welfare outcomes and in effect policy concerns. That is;
“In order for markets to fulfil the promise they offer for risk management, efficient distribution of production according to comparative advantage, clear transmission of policy signals, and maintenance of micro-level incentives to innovate, there should be neither segmented competitive equilibria nor effective trade quotas ……….……… Given limited data, in particular a paucity of data on transactions costs and trade volumes, and the intrinsic limitations of existing empirical methods, economists still have only a fragile empirical foundation for reaching clear, strong judgements about spatial market integration as a guide for corporate or government policy”.(Barrett, 2005)
Depending on the nature of trade policy environment, distortions that characterise the markets and transactions costs involved in conducting trade, price series may behave in various ways of relationships. Thus, while MI can be evaluated via any of the above two major methodological frame under specific assumptions of the market, each has a potential weakness when a complete conceptual foundation of MI theory is to be inferred. Each measurement tool depends on the specificity of the market under consideration and as such fails to distinctively address the
relationship between the law of one price, competitive spatial market equilibrium and implied efficiency, nature of arbitrage dynamics; and market integration within each model frame.
In fact, it has been established that many macroeconomic variables are characterised by different nonlinear forms that require thoughtful or more robust econometric tools. In market analysis for example, while transactions cost may deter arbitrage to a certain threshold of price/rent variations between two market points, the behaviour of the long-run relationship between the two markets may indeed be far from constant or linearity due to economic uncertainties and policy changes among others. In practice while threshold (ECM) and PBM models are usually used in that order to address market behaviour in commodity markets assessments as noted above, in any respect, each specification may be unable to capture the system behaviour where the very driving assumptions of the other are of prime important and are also to be represented.
While TAR models are capable of characterising systems by their dynamic processes in magnitude and speed of price (rent) adjustments and hence to infer process of integration, the PBM as a static probabilistic model accounts directly for the nonlinear-discontinuities in long-run relationship to define arbitrage conditions (outcomes) which explicitly ignores any adjustment dynamics of the process and their implied time series effects.
Recent extensions of PTE into regime switching (MS-VECM) provide room for inferring arbitrage conditions (market outcomes). These models do not however, account directly for transactions cost effect on the adjustment processes and usually do not impose equilibrium conditions. Impliedly, it is conceivable to construct a more robust model for MI assessment by merging these two major methodological blocks, where transactions cost can be inferred from the price differentials in a regime switching fashion to accommodate arbitrage conditions when such data is not available.
In the next section we define a conceptual framework to highlight how markets inter-relationships over time fit in a wider non-linear dynamical system and demonstrate how the complexity of MI strongly suggests such modelling framework.