Hermann Remsperger: Statistics for financial stability purposes Speech by Professor Hermann Remsperger, Member of the Executive Board of the Deutsche Bundesbank, at the Second ECB conference on statistics, Frankfurt, 22 April 2004. * * * Ladies and Gentlemen Sound statistics are needed for both fields of central banking - monetary policy and safe-guarding of financial stability. Statistics for monetary policy purposes mirror the two pillars of the ESCB’s strategy: first, monetary statistics, such as monetary aggregates and their counterparts which are compiled by the ESCB on the basis of banks’ balance sheet data and second, a wide range of real economic indicators such as GDP growth, wage developments and price indices. Most of these indicators are collected and compiled by the European Statistical System. Statistics on financial developments are needed not only because of the key role that financial institutions and financial markets play for monetary policy. They are also needed because monetary stability consists of both, price stability and financial stability. Central bank policy is more than monetary policy. It is against this background, as Eugenio Domingo Solans pointed out in Berlin last year, that the ECB has added the development of a statistical framework for financial stability to its medium-term agenda. In this context I would like to raise two questions today. First, does financial stability require different statistics from those which are already available for monetary policy? And second, in which areas of financial stability can deficiencies concerning the availability of financial stability indicators still be identified? I Measurement without definition? Let’s approach these two questions by emphasising that financial stability is a very complex subject. According to Tommaso Padoa-Schioppa, financial stability means “a condition whereby the financial system is able to withstand shocks without giving way to cumulative processes, which impair the allocation of savings to investment opportunities and the processing of payments in the economy”.1 This definition shows that financial stability is not confined to banking stability. Furthermore, recent research points out that financial instability can occur even in an environment of price stability. This is the underlying situation that makes statistics for financial stability purposes more diverse and less well established than statistics for monetary policy. Moreover, and unlike statistics for monetary policy, financial stability indicators do not only focus on the euro area as a whole. They also have to cover financial stability at the national level. Against this background the message I’d like to convey today is very simple: Although a comprehensive set of harmonised and consistent series of euro area indicators for monetary policy is now available, there is scope for collecting and compiling new data which could be used for financial stability purposes.2 What we need to assess the stability of the financial system is a very complex statistical framework. Take the IMF initiative on Financial Soundness Indicators as an outstanding example. Its “monitoring grid” clearly shows what Andrew Crockett pointed out already a few years ago: There is not only a micro but also a macro-dimension to financial stability analysis.3 T Padoa-Schioppa, Central banks and financial stability, speech delivered in Jakarta on 7 July 2003. Even though most statistical indicators, such as balance sheet statistics, interest rates and exchange rates, can serve both monetary policy and financial stability purposes, there are also a number of indicators which relate exclusively to one area or the other. Andrew Crockett, Marrying the micro- and macro-prudential dimensions of financial stability, speech delivered at the Eleventh International Conference of Banking Supervisors, Basel, 21 September 2000. It is certainly true that financial stability indicators concentrate more on the distribution within peer groups (ie categories of individual banks) which are at risk rather than the “average” bank. However, at least some degree of aggregation is normally necessary as the causes of financial instability are often common to all banks. It is unlikely to be the occasional failure of one bank that triggers a crisis but rather a shock to the financial system as a whole, caused, for example, by an earlier asset price boom and/or lending boom. While the analysis of shocks to the economy and the surveillance of macroeconomic conditions are also essential parts of monetary policy analysis, the question of how these shocks affect the financial sector is typically relevant to financial stability purposes only. II Infinite variety of financial stability indicators I suggest concentrating now on three areas. First, financial and real estate markets; second, financial institutions, and third, non-financial companies and private households. With this setup in mind, I will first focus on the situation in Germany before drawing your attention briefly to the euro area as a whole. As far as data for the first area, namely surveillance of financial markets are concerned, these are more or less readily available with long time series and adequate frequency. Examples are stock price indices, bond yields and emerging market spreads, obtained through commercial data providers. However, the availability of indicators for other asset markets is less satisfactory. One example are price indicators for the real estate markets, where we at the Bundesbank currently calculate indicators for terraced houses and flats based on data from a commercial data provider at annual frequency only. We are now investigating a way to move to a quarterly frequency for these data. The second group of financial stability indicators relates to financial institutions, in particular to the banking system, which forms the backbone of the financial system. It is precisely this area that is stressed in the IMF project on Financial Soundness Indicators, namely setting up indicators of the vulnerability of the financial system (such as credit, liquidity and market risk indicators) and for the capacity to absorb shocks. In Germany, there are two different data sources in this area. First, data collected for monetary policy, such as balance sheet statistics and all kinds of interest rate statistics. Second, data used for supervisory purposes, such as data on profitability, risk and capital. These supervisory data cover the total business of individual German institutions, thus also branches abroad. Sometimes even the whole bank group is covered, ie including domestic and foreign bank subsidiaries or even other financial subsidiaries. We are currently working closely with the Federal Financial Supervision Agency and the Ministry of Finance to investigate how the data sets could be combined and better displayed. The aim is to improve the analysis under the “financial stability umbrella”. While a near-comprehensive set of raw data is more or less readily available, further work may be necessary in respect of the methodology and frequency of those data. From the perspective of financial stability it would be helpful to have the statistics on banks’ profits and losses available on a quarterly basis. However, for the monitoring of financial stability it is not sufficient to look only at banks. You also have to look at other financial intermediaries, insurance companies and pension funds. A very important issue here is to gain more insight into the credit risk transfer from banks to other financial institutions through credit derivatives or the securitisation of loans. Although some fundamental data do exist in this regard in Germany, data availability in this field should be improved to get more information on links between different sub-sectors within the financial industry. Let us now turn to the third area of financial stability indicators. What we also need are data on the financial conditions of non-financial enterprises and households. Economic shocks might also be transmitted to the financial industry from these sectors - for example, via a deterioration of the quality of banks’ assets. It is precisely in this data segment, namely the quantification of the overall liabilities of non-financial corporations and households, that we at the Bundesbank are currently improving data availability. We have established a data pool related to non-financial enterprises in Germany which is fed by various sources (our own information, associations of banks and credit insurers, public information services, etc). In addition to the banks’ balance sheet data which allow us to quantify the liabilities of non-financial corporations and households vis-à-vis banks, supplementary information from the data pool on corporations may shed more light on the overall indebtedness of the private sector. Here, a possible indicator may be the ratio of non-financial corporations’ total debt to equity. A second set is composed of profitability indicators, such as return on equity. As regards households’ assets, the Bundesbank has reasonably comprehensive information on their securities held in safe custody with banks in Germany. Admittedly, these data are supplied on an annual basis only. However, we are going to increase the frequency of the safe custody statistics to a quarterly basis. That is why we may be in a position in due course to monitor the specific developments of securities holdings of households, as well as of other economic sectors such as non-financial corporations, in a more timely manner and with greater precision. Turning now to the analysis of financial stability for the euro area as a whole, at least two additional issues arise from a statistical point of view. First, the degree of cross-country comparability of the data and, second, the availability of indicators at euro-area level. We should aim to achieve a sufficiently high degree of coverage and harmonisation for each key indicator for the analysis of financial stability in the euro area. But this is a very ambitious target, even for the medium term. Harmonisation and coverage issues may arise in all the three areas, I adressed a minute ago. Let’s take the banking industry as an example. The ESCB already collects and compiles a comprehensive and harmonised set of statistics for monetary policy purposes. This allows the derivation of high-quality indicators referring to the business of bank head offices and bank branches located in the countries of the Eurozone. However, bank branches/subsidiaries outside the euro area are not covered by these data, and other financial subsidiaries are excluded in general. At the same time, there are indicators covering the entire bank or bank group. These indicators are derived from national supervisory data sources and thus are not harmonised. To make a long story short: There is one data set which is harmonised but lacks coverage. And there is another one which reflects high coverage but lacks harmonisation. III Feasibility constraints I think we have to tackle these and other harmonisation gaps. Furthermore, there is no doubt that the availability of reliable statistical data for financial stability purposes is not yet complete. We are faced with data gaps. There are, however, practical difficulties with regard to gathering additional, and more detailed, statistical data. One possibility to reduce these difficulties is to consider whether more data from commercial data providers could be purchased. However, the scope may not be sufficient as the focus of private providers is on financial market data. In addition, this option could turn out to be quite costly. Another option might be to consider extending the statistics produced under the stewardship of the ESCB in the field of money and banking statistics. However, a good sense of proportion is necessary when deciding on new harmonised statistical requirements. There is a common understanding that producing statistics is a costly matter, in particular from the viewpoint of the data suppliers. Any new statistical reporting will place an increased burden on the reporters and strain their resources. And let’s not forget that central bank statisticians and data reporters anyway have to cope with a whole bunch of new challenges. Examples are the enlargement of the European Union, any potential enlargement of the European Monetary Union, the change in accounting rules, and, last but not least, the requirement of calculating new indices and indicators for banking supervision purposes under the Basle II regime. Given all these tasks, it is absolutely essential to set priorities. The highest priority should be attributed to closing data gaps with regard to essential indicators for financial stability such as statistics on the securitisation of bank loans. In order to narrow the data gap in this area we should use existing data with sufficient coverage from other sources. An obvious example is the biannual information by the BIS on derivative instruments which is collected from a small number of leading market players, which covers still 80% of the overall market volume. At the same time, we have to check whether existing statistical surveys could be discontinued in exchange for any new data requirements. For example, regionally disaggregated balance sheet data of banks in Germany no longer have analytical relevance because of euro-area membership. In order to keep the costs within strict limits, statisticians may find it helpful to determine the extent to which one and the same data source might serve different analytical purposes. We also have to check whether new data requirements can be met from existing statistics if we estimate parts of the data cells and accept - as far as tolerable - estimation errors. This could apply to some specific sectoral breakdowns within the credit aggregates of MFIs which are not actually reported but estimated from specific benchmarks. In order to reduce the reporting burden, statistics could be compiled, whenever possible, on a sample basis, in particular in those cases where prices or indices are to be calculated. The MFI interest rate statistics could be taken here as an example. At the very least, statistics, or surveys, could be reported on a voluntary basis. This would be feasible in cases where a very small number of institutions cover the bulk of the business in question. Statistics on derivatives and the bank lending survey are valid examples. IV Conclusion Let me conclude by emphasising three points. First, there is a need for financial stability statistics beyond those that are already available for monetary policy purposes. Second, although progress has been made, a more extensive set of indicators is still missing. Third, clear-cut mandates are necessary for the formulation of reporting requirements and for the cost assessments; all the parties involved have to acknowledge that the production of statistical data is costly and burdensome and that budgets for statistical reporting are in no case “unlimited”.