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The financial cycle - the impact of booms and busts in the EA and they relationship with business cycle phases Keywords: Financial cycle, business cycle, econometrics, statistics, Euro Area, European Union 1. INTRODUCTION The financial cycle is the new black. Almost ten years after the burst of the housing bubble in the Unites States and the ripple effects that have touched the other world economies, the attention of research has shifted its focus on understanding the financial cycle and its interaction with the business cycle. Macroeconomic models need to incorporate the financial aspect in order to better understand its movements. This paper aims to 1) compare the different definitions given of the financial cycle in the literature; 2) model the financial cycle for the euro area as an aggregate and for its member states; 3) investigate its relationship with the business cycle; and finally 4) explore the feasibility of an extension of financial components to the business cycle clock. 1.1. Describing the financial cycle While the business cycle has been thoroughly investigated in the past, no definition exists yet for the financial cycle that has gathered the consensus of the scientific community. The first chapter explores and summarises the different definitions of financial cycle, which can be divided in three broad categories: the first, more parsimonious one, that chooses a very limited number of variables (e.g. Borio 2014 investigates the financial cycle in terms of credit and property prices), the second chooses a battery of financial indicators (Stremmel 2015), and finally through the construction of a synthetic measure (Claessens et al. 2011). One of the main issues faced to assess the optimal choice of variable is linked to the length of its series: although consensus lack on its definition, there is convergence in the assessment of the frequency of the financial cycle that can go vary 8 to 30 years, and thus limits the use of several, most recent and sophisticated variables. 2. METHODS 2.1. The choice of the dataset The paragraph gives an overview of the main datasets analysed in order to choose the most appropriate number to describe efficiently the phenomenon. This is a cardinal aspect, as the lack of a common definition of the financial cycle complicates the choice of the variables to use, and it is an element that by itself can change the outcomes of the approach. Moreover, an analysis of the euro area is further 1 complicated by the lack of significantly long time series, most of which begin in the late Nineties, and can hamper the reliability of the results. Below is a list of the main dataset that are currenty explored: Money aggregates (M1 M2 M3) Aggregated balance sheet statistics for MFI for the euro area Loans to households (including breakdown for consumer credit and loans for house purchase) and non-financial corporations (maturities up to 1, 5, and over 5 years). Deposits by sector (HH, financial and non- corporations, also by maturity) Balance sheets of euro area financial vehicle corporations Harmonised long-term interest rates Securities exchanges EU structural financial indicators Total credit to private non-financial sector and HH House price index (also deflated) Gross non-performing debt instruments at EU and country level and total accumulated impairment Overnight interest rates for the euro area Stock indexes and prices 2.2. Modelling the cycle The difficulty of defining the financial cycles impacts on its statistical measurement and analysis. The most recent research has focussed on a definition of the financial cycle based on levels, as in the traditional business cycle analysis, rather than a deviation from a trend, which can lead to different modelling and interpretation. The Markov-Switching model (Mazzi and Savio 2007, Mazzi 2016) is here proposed analyses the financial cycle and then investigate the impact of modelling the two cycles, business and financial, together. The preferred techniques to model the financial cycle are linear or non-parametric, e.g. weighted aggregations such as the conference board, or parametric, namely dynamic factor models, VAR, or their combination FVAR. The final choice will depend on the performance of the indicators, taking also into account computational complexity. Regarding model construction for a real-time detection or forecasting of turning points, it is preferred to use non-linear techniques (MS univariate models). Excessive volatility should be addressed, either by techniques to reduce before the modelling phase, or by using directly techniques that incorporate volatility (GARCH combined with VAR or MS). 2 3. RESULTS This section will illustrate how the movement of the financial cycle in the euro area and in a selection of its member states, in particular with respect to the business cycle. The EA financial cycle will be compared with that of the business cycle in order to high light potential common patters (the study will also carry out a comparison of the financial and business cycles of the EA as an aggregate with that of its member states). 4. CONCLUSIONS In the light of the results, a potential implementation of a financial cycle clock, linked to the business cycle one, could be proposed. Further strands of research, could go in the direction of developing similar tools for other countries or economic areas. REFERENCES Borio, The financial cycle and macroeconomics: What have we learnt? Journal of Banking & Finance, 2014 – Elsevier, pp. 182-198 Stremmel, H. (2015) Capturing the financial cycle in Europe. European Central Bank, Working Paper Series No. 1811. Claessens, S., Kose, M. and Terrones, M. (2011) Financial Cycles: What? How? When?. IMF Working Paper, No. WP/11/76. Mazzi, Savio, Growth and cycle in the Eurozone, Palgrave, 2007 Mazzi, Complementing scoreboards with composite indicators: the new business cycle clock, Eurona, pp. 75-99 3