The popular media suggest that the Federal Reserve is 'all powerful' in determining things like interest rates and inflation, but this is not true. The Fed controls currency and reserves - imperfectly - but the money supply is determined by the numerous decisions of borrowers and lenders to expand credit. Thus, the Fed can try to pump reserves into the financial system, but it will not result in an expansion of any monetary aggregates. Monetary policy can also lead to price bubbles. When people think of money and inflation, they usually think about inflation in goods prices. This stems from the quantity theory of money that treated the number of transactions taking place as being proportional to goods and services produced. That may have been appropriate for a society in which most transactions were indeed for goods and services, but today, the transactions for real estate and securities dwarf the transactions for goods and services. This means that the quantity theory of money needs to be rethought, and this rethinking illuminates how excess money supply growth can cause price bubbles in securities, real estate and commodities as well as traditional inflation.
Applying cointegration analysis to security price movements illustrates how securities move together in the long-term. This can be augmented with an error-correction model to show how the long-run relationship is approached when the security prices are out of line with their cointegrated relationship. Cointegration and error-correction modelling promises to be useful in statistical arbitrage applications: not only does it show what relative prices of securities should be, but it also illuminates the short-run dynamics of how equilibrium should be restored along with how long it will take. Cointegration, coupled with error-correction modelling, promises to be a profitable way of implementing statistical arbitrage strategies.1 Bondarenko (2003) and Hogan et al. (2004) defined statistical arbitrage as an attempt to exploit the long-horizon trading opportunities revealed by cointegration relationships. Alexander and Dimitriu (2005) showed how cointegration is a better way of implementing a statistical arbitrage strategy than other conventional ways, like the use of tracking error variance minimization. These previous studies, however, did not add error-correction modelling to the trading strategies. This article seeks to fill that gap, by presenting how to implement a statistical arbitrage strategy based on cointegration and error-correction modelling.
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