Interactions between business cycles, financial cycles and monetary policy: stylised facts

Abstract

3However, the empirical link between asset prices and economic activity on the one hand, and the relationship between economic activity and interest rates or between stock prices and interest rates on the other, are not established facts. This study therefore sets out to identify a number of stylised facts that characterise this link, using a statistical analysis of these data (economic activity indicators, stock prices and interest rates). More specifically, we study the co-movements between stock market indices, real activity and interest rates over the business cycle. Assuming that there is no single definition of the business cycle, we adopt an agnostic approach in our methodology. The traditional approach characterises the cycle as a series of phases of expansion and contraction. Formally, expansion phases are defined as the periods of time separating a trough from a peak; conversely, contraction phases correspond to periods separating a peak from a trough. In this respect, it is vital to define and accurately identify peaks and troughs. Although this view of the cycle fell out of fashion after the 1970s, it has recently come back into focus thanks to a number of studies, in particular by Harding and Pagan (2002a,b), who proposed a simple method for analysing the concordance between macroeconomic variables. 4 By definition, the concordance index represents the average number of periods in which two variables (eg GDP and a stock market index) coincide at the same phase of the cycle. The traditional approach defines the business cycle directly by analysing changes in the level of a variable, eg GDP. The modern approach (mentioned above), using the appropriate statistical filtering techniques, enables us to split a variable into two components, one cyclical or short-term, and the other structural or permanent. As its name suggests, the cyclical component has no trend and can be associated with the business cycle. Consequently, we can calculate the correlations between the cyclical components of two variables in order to study the degree of their co-movement (ie the similarity of their profile). However, we show that the structural component of a variable is driven by a

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