Hopefully I have not spoilt your morning by delving too deeply into volatility. But, let me stress this, monetary policy divergence will continue to be an underlying source of volatility in the global and regional financial markets. However, issues in financial markets are, by no means, disconnected from the real economy. Indeed, volatility arises from slow recovery, and timid growth outlook for the global economy, the so-called “new normal”. Looking ahead, the global economy is expected to grow only at a subdued pace as some major advanced economies are still grappling with the impacts of the global financial crisis and Emerging Market economies are faced with sluggish exports. Many economists have begun to refer to these conditions as a “new normal” for the global economy where global growth is weak, global merchandise trade shrinks and commodity prices remain low. An important factor behind this global dynamics may be attributed to China’s attempt to rebalance its economy, with more emphasis on domestic consumption, and less on investment and exports. While China is undergoing its economic transition, a slowdown in its growth rate is inevitable. The repercussions of China’s transition have been quite evident on the collapse of commodity prices, leaving many Emerging Market commodity exporters with current account deficits and massive sharp depreciations. Another crucial driver is the trend of “on-shoring” among major economies – leading to a structural change in the global supply chain – where countries like the US and China seek to source inputs domestically, rather than import intermediate goods from other countries.

F. Mishkin has expounded in a recent speech on “Monetary policy, risk management and financial disruptions” (Mishkin, 2008). Over the last few decades, research in macroeconomics has focused on two main types of uncertainty: -  uncertainty about the current state of the economy, commonly called data uncertainty,  -  uncertainty about the structure of the economy, which includes: 1/ uncertainty about some key parameters of the economy (e.g., the slope of the Phillips curve) which may be seen as a benign form of model uncertainty, with some parameters varying in the neighbourhood of an unknown true value; and 2/ a more radical uncertainty about the true model in a given model space (e.g. what is the true probability distribution of shocks, is it Gaussian or not, what is the magnitude of the “tail risks”?). Issues like: “What are the non-linear dynamics that financial disruption may trigger?” are here of the essence. Regarding data uncertainty, a general principle of conduct is certainty equivalence: a policy rule that is optimized under the assumption of an absence of data uncertainty is still optimal in the presence of such uncertainty. Regarding the consequences of “simple” parameter uncertainty, a key reference is the Brainard (1967) principle of caution, which suggests that the monetary policymaker should react more cautiously to shocks to variables associated with uncertain parameters in the monetary policy transmission process. However, what central banks, and in particular the US Fed, face today may be seen as a radical kind of model uncertainty.