Working Paper: CEPR ID: DP9504
Authors: Liam Brunt; Edmund Cannon
Abstract: Cointegration analysis has been used widely to quantify market integration through price arbitrage. We show that total price variability can be decomposed into: (i) magnitude of price shocks; (ii) correlation of price shocks; (iii) between-period arbitrage. All three measures depend upon data frequency, but between-period arbitrage is most affected. We measure variation of these components across time and space using English weekly wheat price data, 1770-1820. We show that conclusions about arbitrage are sensitive to the precise form of cointegration model used; different components behave differently; and different factors ? in terms of transport and information ? explain behaviour of different components. Previous analyses should be interpreted with caution.
Keywords: domestic trade; economic integration; England and Wales; grain markets; time series; cointegration; transport
JEL Codes: N73; Q11; R41
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Improved transport (L91) | Increased market efficiency (G14) |
| Canals (L95) | Reduced price variance (L11) |
| Newspapers (Y90) | Increased correlation of price shocks (E39) |
| Transport improvements (R42) | Reduced price variance (L11) |
| Market efficiency (G14) | Improved half-life of price adjustments (E31) |
| Transport and communication variables (L91) | Half-lives of price adjustments (E39) |