Download NTTS 2017 abstract

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
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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).
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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
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