Data for macroprudential oversight

Data needs and demands for macroprudential oversight are set by a broad range of issues. First, the availability of data obviously restricts the types of inputs to tools and models used by policymakers. Second, the understanding of the financial system, its fragilities and instabilities and the general oversight process defines what a policymaker understands to demand. Third, the design of the tools and models used for the task at hand set their final nuance to the data needs.

Early-warning exercises commonly make use of a wide range of indicators, mea-suring various dimensions of risks, vulnerabilities and imbalances. In this thesis, macroprudential data are related to three different categories:

i) macroeconomic data, ii) banking system data, and iii) market-based data.

Generally, the key three sources of macroprudential data measure the behavior of three low-level entities: households, firms and assets. By grouping data for the entities, we may produce data on various levels of aggregation. While firm-level data may also be of interest in the case of systemically important financial insti-tutionss (SIFIs), the data for macroprudential analysis most commonly refer to high-levels or aggregations of three kinds (see, e.g., Woolford (2001)): macroeco-nomic, banking system, and financial market behavior. Hence, for macroprudential purposes, low-level entities may be aggregated as follows: from data on individual households’ actions to the macroeconomic, from data on banks to the banking sys-tem, and from data on individual assets to the financial market. For instance, an entity could be a country, which would be described by country-level aggregates of macroeconomic, banking system, and financial market behavior. It is still worth to note that a system-wide approach does not always necessitate aggregation, as an entire system may, for instance, be viewed from the perspective of a network of entities. Further, the category aggregating banks to the banking system may likewise be defined in broader terms (e.g. financial intermediaries in general) or some other type of financial intermediaries (e.g., insurers).

Yet, these three categories do not perfectly cover all types of data relevant for macroprudential oversight, especially not novel unexplored sources. These data relate, for instance, to texts and discussions (e.g., news articles, blogs or discussion forums) and tracking human behavior (e.g., search-terms used in Google and buying behavior). Whereas text has, for instance, been utilized for mapping bank inter-relations (see, e.g., R¨onnqvist and Sarlin (2013)), trends in Google searches have been used for nowcasting macroeconomic data with long publication lags (see, e.g., Carri`ere-Swallow and Labb´e (2013)). The focus herein is, however, on the above mentioned three categories of numerical data, and on an overview of their use as indicators. The below discussion is supported by a long, yet incomplete, list of indicators along all three categories in Table 4.1.

4.2.1 Macroeconomic data

Macroeconomic data can be transformed to measure risks and vulnerabilities of economic activity on a country level, and may hence function as leading indicators.

Rather than being narrow in definitions, many macroeconomic measures provide a broad picture of overall economic and financial activity, as well as general cir-cumstances, in the entire economy or a particular area of it, such as economic and production growth, current account balance and inflation. Trends, and devi-ations from them, indicate not only broad economic development in general, but also whether quantities and prices are consistent with prospects, such as in credit markets. For instance, vulnerabilities and risks to financial stability may be repre-sented through above-normal and sustained rates of growth or valuation of credit and investment.

The production of macroeconomic data involves a laborious and costly aggregation process to derive figures that represent all households in an economy. The data are obviously not only of interest for domestic analysis, but also for various cross-country comparisons. This has stimulated a wide range of attempts to harmonize macroeconomic measures. Explicitly aiming at standardizing macroeconomic data across countries, the United Nations have issued their System of National Accountss (SNAs) in 1953 and its revised versions in 1968, 1993 and 2008 (see United Nations (2008) for the latest version). Likewise, the International Monetary Fund (IMF) has issued a Balance of Payments Manual to provide an accounting standard for reporting of balance of payments statistics (see IMF (2008) for the latest version).

The title of the version in 2008, in contrast to the versions in 1948, 1950, 1961, 1977 and 1993, has been amended to Balance of Payments and International Investment Position Manual to reflect that it now covers both transactions and stocks of the related financial assets and liabilities alike.

Lately, multiple initiatives mainly run by the IMF have attempted and also prompted progress in data provision. In 1996, the IMF established the Special Data Dissemi-nation Standard (SDDS) (see (IMF, 2007c) for the latest version) to guide member countries in providing national economic and financial statistics to the public. The SDDS is the first of a two-tier data standards initiative with the general aim of improving access to comprehensive, timely and accurate data to facilitate macroe-conomic policies and the functioning of financial markets. To function as a devel-opment tool to prepare for SDDS subscription, the IMF established the second tier in 1997, called the General Data Dissemination System (GDDS) (see IMF (2007b) for the latest version). Likewise, the Data Quality Reference Site (DQRS) was established by the IMF in 2000 to foster a common understanding and importance of data quality.

The national accounts may further be complemented with balance-sheet exposures between aggregated entities, such as economies. These types of cross-border ex-posures represent crucial links in the global economy. Since 2001, the IMF has published data on bilateral portfolio investment positions among economies on an annual basis. The data have been collected through the annual Coordinated Portfo-lio Investment Survey. Likewise, the Coordinated Direct Investment Survey collects bilateral position data on direct investments among economies.

Table 4.1: Examples of macroprudential indicators.

(a) (b) (c)

Macroeconomic data Banking system data Market-based data

Macroeconomic indicators Banking system

indicators Market-based indicators

Internal indicators Capital adequacy Asset valuation

GDP growth Equity to assets Equity prices

Unemployment Tier 1 and 2 ratio Bond spreads

Inflation Asset quality Derivative valuation

Debt imbalances Impaired assets CDS prices

Credit imbalances Non-performing loans Option-adjusted spread House prices Loan loss provisions Credit ratings

External indicators Debt to equity Sovereign ratings Current account balance Return on assets Firm ratings External investment position Management Credit spreads Unit labor costs Cost to income Sovereign yield spread

Real exchange rate Earnings Default probabilities

Export market share Return on equity Distance-to-default Net interest margin Bond default probabilities Liquidity

Liquid assets to liquid liabilities

Interest expenses to liabilities

Deposits to funding Loans to deposits Sensitivity to market risk

Share of trading income Loans to assets Net open position in foreign exchange to capital Net open position in equities to capital

Macroeconomic linkages Banking sector linkages Market-based co-movements Equity and debt exposures Equity and debt exposures Asset and derivative

interdependence

. .

Notes: The table draws upon compilations in Betz et al. (2013), Cih´ak (2006), IMF (2006) and Woolford (2001). The table presents three types of indicators: macroeconomic, banking system and market-based.

Macroeconomic indicators may be defined to describe different sectors, such as private and government sector. Banking system indicators are defined on the country level, but may also be measured per firm if needed, as oftentimes is for SIFIs. Likewise, market-based indicators may be used on an entity or aggregated market level, as needed. Following IMF (2006), credit ratings are classified as market-based indicators as they are produced mainly for use by market participants. The table does not discuss how the data may be transformed. Hence, each mentioned indicator may address different imbalances depending upon its transformation.

4.2.2 Banking system data

Banking system data utilize, usually in the form of ratios, aggregated country-level information collected from balance sheets and income statements of individ-ual financial institutions. The need for macroprudential assessment of financial conditions on the level of banking systems, rather than only a microprudential, or institution-level, approach, has been accentuated not only by the ongoing financial crisis, but also by the Asian financial crisis in the late 1990s. San Jose and Georgiou (2008) describe that vulnerabilities in Asia were related to international capital flow reversals, also involving shocks to the corporate and household sectors, whereas the recent wave of distress stemming from the sub-prime mortgage markets highlights the importance of balance-sheet exposures of financial institutions and vulnera-bilities to credit and liquidity squeezes. Likewise, from a European viewpoint, the increasing integration of national financial systems has stimulated efforts to develop a common framework for financial stability analysis (Agresti et al., 2008).

The need for data to assess strengths and weaknesses in financial systems led to at-tempts to derive a commonly accepted list of financial stability indicators, not the least the financial soundness indicators (FSIs) developed at the IMF. Sundarara-jan et al. (2002) were the first to propose sets of so-called “core” and “encouraged”

FSIs. The FSIs are measures of the current aggregated financial health and sound-ness of the financial institutions in an economy. A final list, with more precise definitions of the FSIs, was laid down in a set of indicators compiled by the IMF (2006) in the Compilation Guide on Financial Soundness Indicators (henceforth the Guide). IMF (2006) puts forward a handbook on concepts and definitions, as well as sources and techniques, for compiling and disseminating FSIs. For macro-prudential surveillance, the key indicators are based upon aggregated information contained in the balance sheets and income statements of individual financial in-stitutions. The literature on individual bank failures draws heavily on the Uniform Financial Rating System, informally known as the CAMEL ratings system, in-troduced by U.S. regulators in 1979, where the letters refer to Capital adequacy (e.g., risk-based capital ratio), Asset quality (e.g., nonperforming loans to capital), Management quality (e.g., cost to income), Earnings (e.g., return on equity) and Liquidity (e.g., deposits to funding). Since 1996 the rating system also includes Sensitivity to Market Risk (e.g., net open position in equities to capital, which de-rives CAMELS). To implement the FSIs in the Guide, the IMF invited its members to participate in a Coordinated Compilation Exercise (CCE), which eventually led to 62 participating countries and regions (IMF, 2007a).

In the European context, the European Central Bank (ECB), jointly with the Banking Supervision Committee (BSC) of the European System of Central Banks, have put efforts into developing their own financial stability indicators, called macro-prudential indicators (MPIs) (see M¨orttinen et al. (2005) for an overview of the methodology). The aim of the MPIs is defined to be to gauge conditions in the financial system and its resilience to stress situations. While differing in terms of the aim, the scope of FSIs and MPIs is analogous. The MPIs were re-ported and analyzed in the European Union (EU) Banking Sector Stability report prepared by the BSC until 2010, whereafter the data have only been reported in the Consolidated Banking Data, a dataset published in the ECB Statistical Data

Warehouse.

Cross-border linkages among banking sectors is obviously a potential contagion channel (as also noted in Section 3.2), when assessing interdependence of the global economy. The BIS has been collecting international banking statistics with bilateral partner-country information on both a locational basis and on a consolidated group basis. Likewise, to assess the system-wide risk within countries, balance-sheet exposures between individual banks are of central interest.

4.2.3 Market-based data

Market-based data exploits aggregated information dispersed among financial mar-ket participants. The rationale for using marmar-ket data is that prices of financial instruments, such as equities, bonds and options, capture forward-looking percep-tions of financial market participants, not least related to vulnerabilities and risks in the financial system. Rather than being a substitute for the previous sources of information, market-based data complements analysis by conveying the view of financial market participants. Lately, joint efforts by the IMF, ECB and BIS have been put forward to assist the reporting and production of coherent, relevant and comparable securities statistics for use in financial stability analysis and monetary policy formulation. In a three-part series, the Handbook of Securities Statistics was published in 2009, 2010 and 2012 (BIS-ECB-IMF, 2009, 2010, 2012).

Market-based data capture the perceptions of markets about vulnerabilities and risks in the financial system. The degree of system-wide risk may be measured by, for instance, yields and spreads of financial instruments, asset prices, externally measured creditworthiness and sovereign ratings, interest rates, exchange rates and stock market volatility. Depending then on how these data are transformed, they function as forward-looking measures of the health of the financial system. They may be, for instance, changes in government or corporate bond spreads, relative stock-market prices, and indicators of volatility in share prices (e.g., Cih´ak (2006)).

Moreover, market-based data are oftentimes transformed into some more advanced stand-alone measures of default probability. One indicator that has gained large attention is Merton’s (1974) distance-to-default, which uses a structural valuation model to compute the ratio of a firm’s assets to debt. To be forward-looking, asset value and volatility is, however, estimated from equity data. Since supervisors com-monly intervene before capital is depleted, Chan-Lau and Sy (2006) and Danmarks Nationalbank (2004) present two alternative, but similar, measures: distance-to-capital and distance-to-insolvency. Likewise, bond prices may be turned into a default probability by Fons’ (1987) function of the additional required rate of re-turn over default-free bonds. A more direct measure of default probability may be obtained from credit default swaps (CDSs). CDSs provide an insurance against default, where the seller guarantees protection by compensating the buyer in the event of a default of the reference obligor during the life of the contract and the buyer pays a quarterly fee (i.e., the CDS spread). The default probability is then calculated from the CDS spread, interest rate of default-free bonds and recovery rate (i.e., the amount recovered in event of a default). While being defined on the firm level, these measures can obviously be aggregated through simple or weighted averages or measures for entire portfolios. However, due to the existence of large

co-movements in market-based data, aggregating these indicators from the entity level to the systemic level poses a number of challenges that still remain to be solved. One suggestion is the indicator by Cih´ak (2007) that attempts to account for correlation of defaults across institutions in an aggregate measure of financial stability.

These data may also be used to compute interdependence among economies. For this task, one can compute co-movements in country-specific market data, such as stock market indices, CDS spreads and bond spreads. Yet, the most common approach is to make use of firm-level data, in order to assess co-movements in their asset prices (see, e.g., Hautsch et al. (2011)).