Iceland: Selected Issues Paper

International Monetary Fund. European Dept.

doi:10.5089/9781498365437.002.A004

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Iceland: Selected Issues Paper

Author: International Monetary Fund. European Dept.

Publication Date:: 13 Mar 2015

eISBN:: 9781498365437

Language:: English

Keywords:: ISCR; CR; Iceland; financial crisis; saving; investment; predicative power; one-dimensional FCI; risk premia; developed FCI; Iceland's FCI; Credit; Market risk; Asset prices

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This Selected Issues paper examines implications of capital account liberalization in Iceland. Capital controls were critical in 2008 to avoid a more severe collapse of the Icelandic economy. Six years later, capital inflows have been liberalized, but most outflows remain restricted. Iceland has used the breathing room to reduce flow and stock vulnerabilities, strengthen institutions, and prepare for the lifting of capital controls. Simulations using the central bank’s Quarterly Macroeconomic Model (QMM) suggest that, compared with the 2008 crisis episode, the economy can better withstand the impact of an abrupt removal of capital controls. However, the outcome would be dependent on a number of factors, including resident depositor behavior.

Abstract

Financial Conditions: are we There Yet?¹

This paper develops a Financial Conditions Index (FCI) for Iceland. An FCI is an aggregate measure of financial and macro-financial variables that is widely used as a financial surveillance tool and can be employed to improve forecasts for credit and growth. An FCI can be helpful in providing a snapshot of financial and credit conditions in a country such as Iceland that is recovering from a financial crisis and has a large financial sector. The FCI constructed for Iceland is based on measures of asset prices, interest rates, exchange rates, and private sector balance sheets. It paints a picture of a gradual improvement in financial conditions after the recent crisis, though conditions still remain well below the pre-crisis average. This highlights the importance of maintaining financial stability as underlying conditions recover. As a leading indicator, the FCI suggests that the recovery in credit is set to continue, led by improved private sector balance sheets and reduced interest rate risk premia, but also rising asset prices and possibly some real appreciation.

A. Introduction

1. The extent of the recovery in financial conditions in Iceland after the recent financial crisis is yet to be assessed. Iceland’s financial conditions were severely affected by the recent global financial crisis and the domestic banking crisis in 2007-10: equity prices collapsed by over 90 percent, money market rates spiked to 18 percent, and the króna fell by about 40 percent in real terms. Private sector leverage increased due to lower asset prices, higher inflation, and a weakened currency (affecting mostly corporations) leading to higher NPLs at banks. To contain the financial and economic crisis, the major banks were nationalized and restructured, capital controls were put in place, and a combined international financial aid package was activated. Since 2010, financial indicators have improved notably across the board. However, domestic interest rates remain high relative to comparator countries, asset prices have recovered only partially, the króna is weak by historical standards, and private sector leverage is still elevated. An assessment of current aggregate financial conditions, relative to the pre-crisis historical averages and crisis levels, is attempted in this paper by developing an FCI.

2. A financial conditions index (FCI) is a useful financial surveillance and forecasting tool. A financial conditions index is often used as a financial surveillance tool, as it conveniently summarizes the bulk of the informational content of an array of financial and economic indicators. In addition, an FCI can be used to improve the explanatory power of econometric models for both credit and GDP.

B. Methodology

3. Both judgment and parametric estimation can be used to construct an FCI. Judgment is widely used by investment bank and other private sector analysts to select the components of an FCI and assign their relative weights, if the FCI is predominately used as a monitoring tool. A principal component analysis (PCA) and a regression approach are the two methods commonly used in the literature on FCIs.

4. A PCA method can be used for a large set of financial indicators, but has limitations. A principle component analysis method constructs a set of orthogonal vectors that summarize the variance of the data.² A one-dimensional FCI is then defined as the first principle component which is the linear combination of the indicators with the greatest variance. The advantage of this method is its practicality, as it allows one to quickly collapse a large set of financial variables into a single indicator. However, as a purely statistical tool, a PCA has a major disadvantage because it assumes that the indicators with the greatest variability have the biggest economic significance. In addition, a PCA method may yield results such that some indicators enter the FCI with a “wrong” sign, and thus, are not economically meaningful and have to be dropped.

5. A regression-based method relies on the explanatory power of financial indicators in a pre-defined model. If the main purpose of an FCI is to explain credit growth, a regression model can be used to derive the relative weights of pre-selected financial indicators. There is no theoretical guidance on what model specification should be used and how to measure the explanatory power of the financial variables. One common approach is to run a vector-autoregression model (VAR) with credit and GDP growth and to measure the increase in the R-squared when one of the financial variables is added to the model.³ Thus, while promising more economic meaningfulness than a PCA, a regression-based method still involves a high level of discretion.

C. Constructing an FCI for Iceland

6. A PCA-based FCI for Iceland is constructed based on asset prices, interest rates, exchange rates, and balance sheet indicators. We began with a fairly large data set that included various measures of consumer and asset prices, interest rates, exchange rates, risk premia, banking sector indicators, balance sheet indicators, and external sector indicators. We reduced the original set of quarterly indicators using the following criteria: (1) the time span should cover at least the last 20 years to capture the last two episodes of financial stress; (2) the sign corresponding to an indicator entering the first principle component has to be economically meaningful; (3) the relative weight of a standardized component in the FCI has to be greater than five percent. As a result, the final set of FCI components includes seven variables: equity prices, house prices, the interbank rate, the long-term nominal interest rate, the long-term indexed interest rate, the real effective exchange rate, and household leverage.⁴ All of the indicators have a meaningful relative weight (of at least 10 percent), and are moderately to highly correlated with the FCI (figure).

7. A regression-based FCI is built using a VAR model and turns out to be very similar to the PCA-based FCI. We used a VAR model for credit and GDP to measure the increase in the explanatory power when one of the components from the PCA-based FCI is added. Three measures of the explanatory power were used: the log likelihood, the R-squared, and the Chi-squared. The increases in the explanatory power corresponding to each indicator were normalized and used as weights in the regression-based FCIs (figure). In what follows, we use the PCA-based FCI as the only measure of financial conditions to simplify the presentation.

D. Interpreting the FCI for Iceland

8. The 2008-10 financial crisis had by far the most pronounced impact on financial conditions in Iceland’s recent history. The post-Lehman global financial crisis and the Icelandic banking crisis resulted in a sharp deterioration of domestic financial conditions, with the FCI falling three standard deviations below the pre-crisis average (figure). All the FCI components—collapsing equity prices, rising household leverage, currency depreciation, and spiking interest rates—contributed to the overall decline (figure).

9. The recovery in financial conditions was impressive in 2010-11 but has stalled since then. A rebound in asset prices, a gradual private-sector deleveraging, a reduction in interest rates, and some currency appreciation lifted the FCI in 2010-11. However, since then, the recovery in financial conditions has stalled, and the FCI remained hovering about 1 standard deviation below the pre-crisis average. Notably, the elevated and even rising interest rate risk premia—in contrast to the other indicators—has continued to halt further improvement in the FCI. Going forward, a reduction in the interest rate risk premia, continued household deleveraging, and some real appreciation (which should be taken in a broader economic context) may contribute to a further recovery in financial conditions.

E. Forecasting Properties of the FCI

10. The FCI is correlated with credit and GDP and leads credit growth. The FCI is highly correlated with credit growth over the entire 20 year period and leads by up to 6-7 quarters, based on various measures of credit (figure). The FCI is only weakly correlated with GDP growth during the pre-boom period but correlation becomes very high starting with the boom period (figure). High correlation between the FCI and GDP growth in 2006-14 can be explained by the increased size of the financial sector (over 10 times GDP) over the boom period and the large impact of the recent financial crisis on the economy.

11. The FCI notably improves the explanatory power of econometric models for credit and GDP growth. To assess the FCI’s predictive power for credit and GDP, we measure the increase in the R-squared relative to naïve (autoregressive) models for various measures of credit and output. When the FCI is added, the R-squared increases by 3 percentage points in the models for total credit, by 0.3-8 percentage points for bank credit, and by 7–12 percentage points for output. Thus, forecasting models for credit and GDP can benefit from the use of the FCI.

F. An International Comparison

12. A comparison of Iceland’s FCI with a similarly constructed FCI for Ireland reveals the importance of country-specific and credit supply-side factors. Based on the same PCA method, Ireland’s FCI paints a picture of faster recovery in financial conditions, relative to Iceland (figure). Indeed, the interest rate risk premia declined further in Ireland on the back of the post-OMT rally, while the recovery in Iceland has been limited, likely due to the risk perception associated with the capital controls. On the other hand, credit growth has recovered relatively faster in Iceland, as the banks have undergone major restructuring, have reduced their NPLs, and are ready to support the economic recovery. In Ireland, NPLs remain relatively high and banks are still under restructuring and deleveraging, and may be in a weaker position to support the recovery. This argues for the importance of having credit supply-side indicators in an FCI, such as NPL or capital ratios.⁵

G. Concluding Remarks

13. The developed FCI highlights a stalled recovery in Iceland’s financial conditions after 2011. Following the bank restructuring and the introduction of capital controls, financial conditions recovered remarkably in 2010-11 but the recovery has since paused, as the interest risk premia have failed to contract further, with the unresolved BOP overhang locked in by capital controls and stillhigh government debt.⁶ Policies that ensure a reduction in the interest rate risk premia, such as a successful capital account liberalization strategy, and help maintain public and private-sector deleveraging will support a further recovery in financial conditions. Some real appreciation would technically help boost the FCI but should be taken in a broader economic context, as a non-appreciating exchange rate supports a healthy current account and helps speed up the resolution of the BOP overhang in the medium term, which, in turn, positively affects the interest rate premia and other economic and financial variables.

14. Credit growth remains anemic and the FCI may help predict a credit recovery. Despite the currently strong economic performance and the solid financial position of the major domestic banks, credit growth remains weak. The developed FCI is shown to be a useful leading indicator for credit, can improve the forecasting power of credit models, and predict a credit recovery over the medium term. Future research will focus on forecasting credit using the developed FCI.

References

Angelopoulou, E., Balfoussia, H., and Gibson, H. (2013), “Building a Financial Conditions Index for the Euro Area and Selected Euro Area Countries: What Does It Tell Us About the Crisis?” European Central Bank Working Paper No. 1541.
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Hatzius, J., Hooper, P., Mishkin, F., Schoenholtz, K., and Watson, M. (2010), “Financial Conditions Indexes: A Fresh Look after the Financial Crisis,” National Bureau of Economic Research Working Paper No. 16150.
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Ho, G. and Lu, Y. (2013), “A Financial Conditions Index for Poland,” IMF Working Paper No. 13/252.
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Hofman, D. (2011) “A Financial Conditions Index for Russia”, IMF Country Report No. 11/295
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Manning, J. and Shamloo, M. (2014), “A Financial Conditions Index for Greece”, IMF Working Paper (unpublished).
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Swiston, A. (2008), “A U.S. Financial Conditions Index: Putting Credit Where Credit is Due,” IMF Working Paper No. 08/161.
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Prepared by Sergei Antoshin and Vizhdan Boranova.

Recent papers include: Angelopoulou et al. (2013), Manning et al. (2014).

Recent papers include: Swiston, A. (2008), Hatzius et al. (2010), Hofman D. (2011), Ho et al (2013).

All the interest rates are measured against the global short term rate.

In the case of Iceland, the data spans for the NPL and capital ratios are too short.

These two issues were also noted by ratings agencies.

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