Forewarned is Forearmed: Configuring an Early Warning Mechanism for Macro-financial Space in India1
Dr. Rabi N. Mishra and Dr. M. Sreeramulu2
Abstract This paper makes attempts to identify a set of early warning indicators for Indian macro-financial space and estimates the efficacy of chosen indicators in signaling the crises. The results showed that a few indicators (such as ‘credit to GDP gap’, ‘output GAP’, ‘Credit growth’, ‘Deposit growth’, ‘CRAR’, ‘Gross NPA Ratio’ and ‘ROA’) performed well in identifying the financial distress with good signal and low noise ratios. As it is difficult to monitor movements of several indicators individually, that too pertaining to different sectors, while predicting crises, an attempt is also made to construct three composite indices, viz., Index of Speculative Pressures, Index of Macroeconomic Vulnerability and Index of Banking Sector Vulnerability and finally an overall Systemic Financial Stability Index. Results corroborate that composite indices performed better in signaling the crises well in advance. The noise ratio too looks better for them.
JEL Classification: Key Words:
C40, E44, G01, C43 Financial Crisis, Early Warning Indicators, Financial Stability, Output Gap
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An earlier version of the paper was presented in the IFABS 2017 Oxford Conference on July 16, 2017 at Oxford, United Kingdom and Study Circle of DEPR, RBI, Mumbai on August 14, 2017. 2
Dr. Rabi N. Mishra (
[email protected]) is Chief General Manager and Principal of Reserve Bank Staff College (RBSC), Reserve Bank of India and Dr. M. Sreeramulu is Assistant Adviser and Member of Faculty of RBSC. The views expressed in the paper are the Authors’ personal and do not reflect in any manner the official stance of the Reserve Bank of India. Valuable comments offered by Benjamin Friedman (Harvard University-USA), Martin Cihak (IMF), Adam Gersi (Vienna IMF Institute), Moazzam Farooque (Central Bank of Oman), Sanjay Singh (FSU, RBI), Romar Correa (Mumbai University) and the participants of the above cited two events are gratefully acknowledged.
Forewarned is Forearmed: Configuring an Early Warning Mechanism for Macro-financial Space in India Introduction History is replete with episodes of financial crises. One of the distinguishing features of all crises, ranging from the first recorded economic bubble, the Tulip mania at the turn of the 17th Century, to the most recent global financial crisis of 2008, is the lack of a framework and tools to forewarn the impending crisis and its potential impact on the economy at large. . Though there was general consensus of the negative effect of financial crises on the broader economy, due to inadequate measurement tools, there was no agreement on whether central banks and other policymakers should at times ‘lean against the wind’ and alter their policies accordingly. As a result, policymakers have time and again had to clean up after the bubble burst and deal with sharp decline in economic activity. . The lessons of past crises3 have generated much research interest in many nations to frame country-specific Crisis Management Frameworks (CMF), of which Early Warning Mechanism (EWM) is an important pillar. The success of such a framework depends on the choice of variables, long data series and study of movement of the variables with a reasonable periodicity. Assessing the implications of movement in these variables, preparation of assessment reports, stress testing and recovery/resolution planning are also key elements of this framework. Firming up of a standard operating procedure to act swiftly and conclusively in the event of a crisis really befalling would be the last leg of an ideal CMF. It is difficult to premise if financial crises can be prevented. But that should not undermine the effort to predict them so that adequate safeguards can be employed to mitigate the adverse impact of the crisis on the financial system and the economy. The EWM evaluates possibilities of crises in the macro-financial space, i.e., it forecasts the likelihood that a crisis will occur based on the trends and patters of a given set of covariates. This in turn is expected to identify critical events the authorities need to be vigilant about. An early warning indicator (EWI) would qualify to be called one, if it has the ability to signal impending crises and potential crises even if they fail to materialize. Choice of variables and constructing effective indicators are identified as the basic challenge. The process starts with the identification of /variables, which could potentially provide useful early warning about potential imbalances in the system. Using empirical methodology the predictive power of these variables will be tested to decide which among them exhibit sufficient predictive ability to be useful in the early warning exercise. Considering the fact that EWIs are ultimately judged on their efficacy to be used as macro prudential policy tools, they should satisfy three requirements. Firstly the timing:
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Except Balance of Payment (BoP) crisis of mid-eighties, Indian economy has not witnessed any crisis and there were no upfront banking crises or currency crisis in India. However, in this paper, we have used the word ‘crisis’ to represent financial disstress or economic deceleration.
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EWIs must provide signals early enough so that policy actions can be implemented in time to be effective. The timeframe required to do so depend inter alia on the lead-lag relationship between changing a specific macro prudential tool and the impact on the policy objective4.The second requirement is stability: the indicator should not flip-flop between signaling a crisis and being “off”. EWIs that issue stable signals reduce uncertainty regarding trends and allow for more decisive policy actions. The final requirement is interpretability (Drehmann and Juselius - 2014). EWI signals should be easy to analyse and interpret and should be making sense for the policy makers to work upon. The Motivation for this Research The importance of financial system in supporting economic growth is well documented. Equally, an unstable financial system can retard the growth potential of a country. The stability of the financial system is even more important for an emerging market country like India, which require high rates of growth to lift a large section of the society out of poverty and improve the per capita GDP. India’s financial sector is large with varied participants, products and markets. Though our financial system has been largely insulated from the adverse impact of the Global Financial Crisis, it is important to learn the lessons so that it is more robust and resilient. The FSB-propelled regulatory reforms, as a sequel to the last global financial crisis, are being seriously pursued here. Constructing a genuine EWM for India could be worth exploring as its financial system is becoming more integrated with the rest of the world and also becoming more and more complex. It is not that such mechanisms are not in vogue – given the changing circumstances, their rigours need to be steeled. Focusing on a select few indicators, which are intuitively featured, may have outlived their utility. What is needed is an in-depth empirical study dealing with (i) identifying and compiling a set of leading macro-prudential, macroeconomic, financial sector indicators, (ii) setting up thresholds for them based on empirical strength, (iii) designing composite indices for measuring the vulnerability in the macroeconomic environment, the banking sector, and speculative pressures in the economy and finally (iv) to design a composite index to reflect the vulnerabilities of the entire financial system. Against the backdrop of the issue of interconnectedness, the present paper purports to empirically explore the identification of EWIs, not only for the banking sector but also for the real and external sector in a comprehensive manner. It is expected that the EWM suggested in this paper would add to the existing literature in this area. It would also complement the extant prudential supervision framework in place to manage financial instability by anticipating the incidence of financial distress/crisis. The paper also attempts to enhance credibility of EWM by selecting appropriate indicators through robust empirical testing.
4For
instance, the Basel III guidance states that “the indicator should breach the minimum (critical threshold) at least 2–3 years prior to a crisis” (BCBS (2010, page 16)).
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The paper makes an attempt to identify a set of early warning indicators and their efficiency in signaling the forthcoming crises. Macro-financial space is its horizon for operation. Smelling the intensity of a potential systemic risk is its motto. Homogenising the effects of inter-sectoral variables is one of the key challenges. An attempt is hence made to consolidate various indicators to construct composite indices on the lines of Ndung’u et al., (2014). The paper is organized in eight sections, section II documents brief survey of literature, section III describes crisis periods, methodology and database used for empirical analytics, section IV documents theoretical underpinnings and selection of early warning indicators. Section V presents analytical framework of constructing vulnerability indicators, Section VI lists out possible scope for future research and Section VII shows EWM as an essential part of the overall architecture of macroprudential policy. Section VIII, the last section presents concluding observations. Section II Review of Literature Research on devising an early warning framework does not carry a long antiquity till financial crises occupied a profound place in the history of financial space. Some of the prominent studies that have significantly contributed to the early warning literature include Kaminsky et al., (1998), Herrera and Garcia (1999), Berg and Pattillo (1999), Edison (2003), Kumar et al., (2002) and Bussiere and Fratzscher (2006). These studies adopted different methods such as qualitative approach, signal extraction approach, multivariate regression approach, limited dependence approach and duration models (Gaytan and Johnson, 2002) approach while devising early warning framework5. However, of the above cited methods, two methods viz., signal extraction approach and multivariate regression (particularly logit regression) approach have received wider attention. Accordingly, good number of studies make use of either of the above two approaches while devising Early Warning Mechanism (EWM). The choice between the two approaches is largely driven by the availability of long time series datasets. Literature shows that for developing cross-country (for instance European Union) early warning framework, studies mostly make use of multivariate regression approach and in the case of country specific early warning framework, they have adopted signal extraction approach. Some studies also compared the operative performance of the two methods 5 However, some studies could able to foresee the structural conditions preceding the onset of crises, by
simply observing the descriptive trends or patterns of economic, financial sector indicators (Joy Mark et al., (2015)). The authors found that low interest spreads and inverted yield curve in the short term while high house price inflation in the long-term were the precursors of banking crisis. High domestic short-term rates coupled with overvalued exchange rates are robust predictors of currency crisis in the short term. Similarly, Burke J.V (2015), inferred marginal leverage ratio as a barometer for measuring the vulnerability in the financial sector. According to the author, banks in search for high yields generally involve in taking high levels of risk, which may not be concealed under the normal leverage ratio. Descriptive analyses undertaken by the author corroborate the fact that normal leverage ratio almost witnessed a stable trend and its predictive capacity in signaling crisis events is almost zero. On the contrary, trends in marginal leverage ratio shows extreme values with drastic swings before happening of crisis.
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and confirm that multivariate models generally improve the operative performance of EWM compared to univariate signalling models (see for instance, Duca Marco Lo et al., 2017). While formulating cross-country early warning framework. Babecky Jan et al., (2012) found out prediction horizon and most useful leading indicators by applying econometric methods6. The authors found that domestic housing prices, share prices, and credit growth, and a few global variables, such as private credit, are risk factors essential for monitoring in developed economies for anticipating crises. Jahn. N and K. Thomas (2012) regressed banking stability indicator on a set of lagged values of macroprudential indicators (such as asset price indicators, leading indicators for the business cycle and monetary policy indicators)7 and found to be reliable early warning indicators. By using logit models, Coasta Matias et al., (2013) examined the efficacy of Financial Sector Indicator, broad macroeconomic indicators and other institutional factors in detecting banking distresses. Behn Markus et al., (2013) assessed the usefulness of private credit, macro-financial and banking sector variables under multivariate econometric model framework. The authors found that apart from credit variables, equity and house prices help in predicting the vulnerabilities in EU member states more correctly. Under dynamic panel probit regression model framework8, Antunes et al., (2016) found that price index and debt to service ratio provide valuable early warning guidance for policy makers while the credit to GDP ratio gap has better signaling properties closure to the emergence of crisis. Holopainen et al., (2016), conducted horse race between conventional statistical methods and modern machine learning methods (k-nearest neighbors and neural networks). The authors inferred that conventional statistical methods generally outperform the advanced machine learning methods9. The authors also combined several economic indicators and built-up a composite indicator. A mix of indicators with different frequencies are useful in emitting timely warning signals. The empirical relationship between stock market volatility and financial crisis is verified under regression framework by Danielsson Jon et al., (2016). The author found that low volatility increases the probability of banking crisis. Further they added that low-volatility significantly increases risk taking and risk taking eventually leads to a crisis when the riskier investments turn into sour. The authors (Rancan et al., (2015) and Minoiu C et al., (2013) examined usefulness of financial inter-connectedness as a source of systemic risk and whether it can serve as an early warning indicator. The authors found that increase in a country's financial interconnectedness and decrease in its neighbours’
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The prediction horizon for each potential leading indicator is worked out by adopting vector auto regression approach and to identify the most useful leading indicators the used Bayesian model averaging approach. 7
The regression model is estimated by using a database of 3330 banks, (in 1995) and 1685 banks (in 2010). 8
The authors used cross-country data pertaining to 28 countries spanning over 1970-Q1 to 2010-Q4 for conducting empirical analysis. 9
By considering 15 European Union economies over the three decades, Virtanen et al., (2016) developed unit root based early warning systems in ex-ante forecasting of financial vulnerabilities.
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connectedness are associated with a higher probability of banking crises after controlling for macroeconomic fundamentals. By applying logit regression model, Coudert V and Julien Idier (2016) found that four indicators viz., the bank credit-to-GDP gap, residential property price-to-income ratio (annual change), three-year real equity price growth and debt service-to-income ratio carry sufficient explanatory power in predicting the crises. Many authors proved that Credit-to-GDP gap has greater predictability of banking crisis and is very useful in signaling vulnerabilities (see for instance Drehmann (2013). Through his empirical estimations he proved that credit to GDP gap is the best indicator in detecting crises across many countries for several decades including emerging market economies. Some authors (Sarlin P.G et al., 2017) used statistical procedures such as binary-choice model for fixing ex-ante thresholds, and setting probability thresholds based on preferences. The authors were of the opinion that the binary choice approach improved out-of-sample predictions and reduced the positive bias of in-sample performance. Mikhail V et al., (2011), designed a hybrid class of models for systemic risk by incorporating the structural characteristics of the financial system and a feedback amplification mechanism. The authors, through the methodology viz., Systemic Assessment of Financial Environment (SAFE) monitored micro prudential information from the largest bank holding companies to anticipate the build-up of macroeconomic stresses in the financial markets. The methodology (SAFE) is useful in predicting a certain vulnerabilities that provides policy makers sufficient time to take ex-ante policy actions in the medium term to mitigate inherent uncertainties. Some authors argue that using of multivariate models while developing early warning mechanism may not be possible in the context of developing countries, primarily on account of non-availability of large time series datasets, which is very essential for empirical analytics. Accordingly, they argue that methodologies planned for preparing EWM for developed countries could be customised by creating the need for country specific case studies (Seth et al, 2012). Some authors, (Krkoska, 2000 and Wong et al, 2012) through their research findings found that early warning framework devised by adopting ‘Signal Extraction Approach’ provides valuable insights in assessing the vulnerability in the macroeconomic environment, particularly for developing countries. By using signal approach, the authors Alessi et al., (2010) and Betz Frank et al., (2013) worked out type-I and type-II errors of major leading macroeconomic indicators. Augmentation of bank specific vulnerabilities with indicators related to macro-financial imbalances significantly improves efficiency of early warning framework under signal extraction approach. Ndung’u et al., (2012) constructed two indices viz., Index of Speculative Pressure10 (ISP) and Index of Macroeconomic Vulnerability11 (IMV) while developing early warning framework for Kenya’s economy. Loloh F.W (2015) also constructed Banking Sector Fragility Index (BSFI) for identifying Ghanaian banking 10
Summation of standardized monthly percentage change in three key variables viz., (i) nominal exchange rate, (ii) short-term interest rate and (iii) international reserves. 11IMV
is defined as a summation of (i) real effective exchange rate, (ii) real domestic credit growth and (iii) M2 as a ratio of international reserves.
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sector crisis. Three leading indicators namely private sector credit, foreign liabilities and deposits have been used for building BSFI. By examining its trends and patterns, the author identified three episodes of excessive risk taking and four periods of high fragility in Ghana. The author also noticed that high fragility episodes are followed by significant increase in Non-Performing loans in Ghana financial system. For identifying the likelihood of failure of huge number of financial institutions, Schwaab Bernd et al., (2011), constructed coincident measures and forward looking indicators under dynamic factor framework. The authors inferred that credit risk conditions can significantly and persistently de-couple from macro-financial fundamentals. Such decoupling can serve as an early warning signal for macro-prudential policy. As seen above, signal extraction approach looks to be most suitable for framing country specific EWM and is more robust in signalling crises if the movements in the relevant macroeconomic and financial sector indicators are aggregated. This paper accordingly, opted to adopt Signal Extraction Approach (Kaminsky et al.-1998) to configure country specific EWM for Indian financial space. Non-availability of longer time series data sets (particularly baking sector variables - in the public domain) remained the constraint to try out others. We have chosen key variables pertaining to macroeconomic, macrofinancial, banking sector and external sector vulnerability. The task of choosing these variables is largely based on previous research on these variables and also data availability in the public domain. The list of Early Warning Indicators (EWIs) chosen in various empirical studies is presented in Annex-3. Section III Data, Crises Periods and Methodology The Data The dataset used in this paper has been collected with an aim to cover as many economic indicators as possible. We also focus on collecting dataset as large as possible for the data demanding estimations. We use annual data spanning from 1986 to 2016. However, in respect of few indicators (particularly banking sector indicators) due to non-availability of data in the public domain, we could able to use data from 1996 to 2016. Crisis Periods Identified12 The sample period chosen for the study, 1986 to 2016, has gone through five different crises/distress events - external debt (BoP) crisis (1984-85), economic crisis (1990-91), Asian crisis (1997-98), global financial crisis (2007-08) and economic deceleration (2012-13). We believe that these crises/distresses impacted Indian economy at various 12
External debt (BoP) and economic crisis have origin in India while other crises are exogenous and have origin in rest of the world. We believe that besides Indian crises, other crises also have some impact on Indian economy as the same have become open economy particularly since early nineties. Therefore, other crises periods also considered while calculating efficiency scores of various leading indicators pertain to macroeconomic, macro-financial, market and banking sector.
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degrees. However, impact of some crises had not been fully experienced by the Indian economy. For instance, at the time of Asian crisis of 1997, as Indian economy was not that financially open, as also for variety of other reasons, one wouldn’t expect the macro/banking/market indicators of India to have adequately forewarned the imbalances in the South Asia including India. But descriptive data analysis reveals that during Asian crisis period, some of the banking sector and macroeconomic variables have shown excessive stress in their behavior13. A brief description of crises along with their impact on various macroeconomic factors is presented in the following paragraphs to provide a background for choosing the various macroeconomic, banking sector and market indicators as potential EWI for the present study. Indian economy started facing difficulties in balance of payments (BoP) since 1985 and subsequently, BoP problems turned into economic crisis within a period of five years. During this period, the Indian government was almost at the verge of default. The foreign exchange reserves had been reduced significantly to such levels that India could be able to finance only three weeks’ worth of imports. We therefore, expect that during the period 1985 to 1990, there may be stress in some of the indicators such as current account deficit, exchange rate14, forex reserves, gross fiscal deficit15 etc. Global financial crisis began in 2007 with a stress in the subprime mortgage market in USA. Subsequently, the crisis turned into international banking crisis. To prevent the possible failure of the world’s financial system, massive bailouts of financial institutions and other palliative monetary and fiscal policies were employed. However, the crisis was followed by a global economic downturn/great recession. As far as Indian economy is concerned, the impact of crisis was not seen at the initial stages, in fact at the initial stages, the impact was positive and the country received significant Foreign Institutional Investment (FII) inflows during the period September 2007 to January 2008. However, at later stages, the economy started facing stress in capital and current account transactions of BoP. Accordingly, as the foreign institutional investors started selling equity stakes in a bid to replace overseas cash balances, the net portfolio inflows to the country turned to negative. Further, the crisis had considerable impact on stock market and exchange rates through creating supply and demand imbalances16. During the financial crisis, the current account was mainly affected after September 2008 through slowdown in exports. In the recent period particularly since 2013, India’s economic growth lost momentum 13
For instance, bank credit growth decelerated to 9.6 per cent in 1997 from its level of around 28 per cent in 1995. Similarly, deposit growth also declined to 16.5 per cent from 22.5 per cent during the same period. Output gap also increased sharply to Rs. 524 billion in 1997 from Rs. -125 billion in 1995. 14
During the period of economic crisis, rupee exchange rate was under severe adjustment. Monetary authority of India started defending the value of the currency by enhancing the stock of forex reserves. Nonetheless, on account of severe depletion of reserves in mid-1991, the government is permitted for a sharp depreciation of rupee in two steps within three days (July 01 and July 03, 1991). 15The
gross fiscal deficit (for centre alone), increased from 6.1 per cent of GDP in 1980-81 to 8.3 per cent in 1985-86 and to 8.4 per cent in 1990-91 (source: Reserve Bank of India). 16
Logarithmic return on stock market (BSESENSEX) was negative at -0.74 in 2008 as against 0.38 in 2006. Similarly, nominal exchange rate (dollar vis-à-vis rupee) shown much volatility during the crisis period (it fluctuated between 41.3 to 45.3 during 2006 and 2008).
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essentially on cutbacks in domestic and global demand. Accordingly, macroeconomic factors such as high inflation, slowdown of corporate earnings, high fiscal deficit, low bank credit and deposit growth, and fluctuations in the industrial output growth have hampered the growth of Indian economy. Methodology For assessing the validity of early warning indicators in predicting the impending crises we use ‘signal approach’, in a manner similar to Kaminsky et al., (1998), Herrea and Garcia (1999) and Ndung’u et al., (2014). In this framework, a signal is emitted if the value of a particular early warning indicator exceeds the threshold value. The signal is said to be effective if it is followed by a crisis within a certain period. Of course, the indicator can also generate noise, or a false signal, if it emits a signal which is not followed by a crisis within the stipulated period. The possible outcomes of signal approach are summarized in the form of contingency matrix in the following Table 1. Table 1: Contingency Matrix Actual Class Signal Predicted Class No signal
Crisis A (correct call) C (missed crisis)
No crisis B (false alarm) D (correct silence)
In the above matrix, ‘A’ is the number of times distress event followed by a signal (in this paper we have assumed that crisis may occur within 1 or 2 years17 once the signal is emitted), ‘B’ is the number of times, distress event not followed (within 1 or 2 years) after signal is emitted, ‘C’ is the number of times distressed event took place while indicator failed to transmit a prior signal, ‘D’ is the number of times signal is not emitted and no distress event ensured. If a variable is a perfect early warning indicator, then ‘A’ should be 100 per cent, while ‘B’, and ‘C’ should be zero per cent. In other words, a perfect indicator will have zero noise and seamless signalling of distress events. The characteristics of good early warning indicators are described in the following Table 2. Table 2: Characteristics of Good Early Warning Indicators Ratio Good signal ratio
Bad signal ratio (also called noise ratio) Noise to Signal Ratio
Definition 𝐴 (𝐴 + 𝐶) 𝐵 (𝐵 + 𝐷) 𝐵/(𝐵 + 𝐷) 𝐴/(𝐴 + 𝐶)
Expectation Good early warning indicator supposed to have high proportion of good signals Low noise will always be a noble characteristic of good indicator. Low noise to signal ratio is a desired characteristic of good early warning indicator
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An attempt is also made to ensure whether results will change if the observation window/ tracking period is changed from 2 years to 1 year. We found that most of the indicators behaved in a similar way.
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Ratio
Definition
Probability of distress event conditional on a signal from the indicator Difference between conditional and unconditional probability of distressed event
𝐴 (𝐴 + 𝐵) 𝐴 (𝐴 + 𝐵) (𝐴 + 𝐶) − (𝐴 + 𝐵 + 𝐶 + 𝐷)
Expectation High conditional probability is expected to qualify an indicator as a good early warning indicator A higher difference signifies more usefulness of the indicator
Section IV Theoretical Underpinnings and Selection of Early Warning Indicators As money market is generally considered as the fulcrum of monetary transmission policy of any central bank, it forms the key candidate in the financial system. It is generally believed that an increase in the interest rate will decrease the output (generally proxied by GDP) and decrease in the interest rate will accelerates output growth. Output gap (positive or negative) is considered as unreceptive economic indicator. A positive output gap reveals that extremely high demand for goods and services and spurs inflation in the economy. On the contrary, negative output gap corroborates a lack of demand and generally a sign of sluggish economy and the prices of goods generally fall. Inflation is one among the several factors which influence the foreign exchange rate of an economy. Research on the topic shows that inflation is more likely to have a significant (overall) negative effect rather than positive effect on the macro-economy. In other words, very low inflation rates may not witness favorable exchange rate for an economy, but high inflation exert considerable pressure on the exchange rate negatively. Inflation is also closely associated with interest rates which in turn influences the exchange rates. Higher interest rates generally attract foreign investment, which can accentuate the demand for domestic currency. Higher interest rates also tend to raise inflation rates which can negatively impact the exchange rate of a country. Low interest rates spur consumer spending and economic growth and influences positively on the value of currency but at the same time may not attract (may even distract) foreign investment. The linkages among a set of macroeconomic indicators are depicted in the following Figure 1. Figure 1: Framework of Macroeconomic Vulnerability 𝑴𝒔
Interest Rate
𝑹𝟎
Money Market
Internal / External Balance (REER)
Exchange Rate
Md
𝒆𝟎
𝑹𝟏
𝑮𝟏
𝒆𝟏 𝑰𝒏𝒇𝟏 𝑰𝒏𝒇𝟎
Inflation
𝑮𝟎
GDP Growth Output Gap
Source: Njuguna N et al., (2013), ‘Early Warning System for Macroeconomic Vulnerability in Kenya’.
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Early Warning Indicators Chosen for the Study After examining number of research articles and prominent papers, which have been complemented and cross checked by the previous researchers, we have chosen a set of 19 early warning indicators; these broadly cover a range of macroeconomic, macrofinancial imbalances. The details of early warning indicators selected for the purpose of the study include - banking sector indicators (such as credit growth, deposit growth, CRAR, Gross NPA), macro-financial indicators (credit-to-GDP-Gap), macro-economic indicator (such as output gap, gross fiscal deficit, external debt), market indicators (interest rate, return on stock market). While observing the trends and patterns of the cited indicators, we have relied on the most commonly used transformations, such as ratios, growth rate, and absolute and relative deviations from trend value. The list of indicators chosen for the study is detailed below (Table 3) Table 3: Early Warning Indicators Chosen for the Study Indicator Macro-financial Indicators 1) Credit to GDP Gap [note 1]
Macroeconomic Indicators 2) Output Gap18 [Note 2] 3) Inflation (WPI)19 4) Fiscal Deficit 5) Forex Reserves 6) External Debt Market Indicators 7) Lending Rate 8) Interest Rate` 9) Return on Stock Market (SENSEX ) 10) Return on Stock Market (Top 100 Companies)
Definition
Transformation
Difference between bank credit to GDP ratio and its trend
Ratio
Difference between actual out and potential output Growth in Wholesale Price Index Gross fiscal deficit as a percentage to GDP Forex reserves as a percentage to GDP External debt as a percentage to GDP
Growth
SBI advance rate Call/notice money rate Logarithmic return
Ratio Ratio Logarithm
Logarithmic return
Logarithm
Growth Ratio Ratio Ratio
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Output gap usually captures a lot of risk taking and it has significant power in predicting the crisis. Output gap (positive or negative) is considered as unreceptive economic indicator. A positive output gap reveals that extremely high demand for goods and services and spurs inflation in the economy. On the contrary, negative output gap corroborates a lack of demand and generally a sign of sluggish economy and the prices of goods generally fall. 19 Wholesale
Price Indices of different base years have been converted to 2004-05 base year before working out growth in WPI.
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Indicator Banking Sector Indicators 11) Credit Growth 12) Deposit Growth 13) Credit to Deposit Ratio
14) CRAR 15) Gross NPA Ratio
16) Return on Asset 17) Leverage Ratio External Vulnerability Indicators 18) Exchange Rate 19) Current Account Deficit
Definition
Transformation
Year on year growth in bank credit Year on year growth in bank deposits Bank credit as a percentage to bank deposits Capital to risk weighted assets ratio Gross non-performing assets as a percentage to advance Net profit as a percentage to total assets Total Assets as a percentage to equity
Growth
Exchange rate of rupee vs US$ Current account deficit as a percentage to GDP
Growth
Growth Ratio
Ratio Ratio
Ratio Actual Ratio (not in percentage terms)
Ratio
Note 1: Credit to GDP Gap is calculated as the difference between credit to GDP ratio and its trend. Trend in Credit to GDP Ratio is estimated by using HP filter with smoothing parameter (λ) of 1600. Note 2: Output Gap is worked out as the difference between actual output (output is proxied with GDP) and potential output. Potential output is reckoned by using HP filter with smoothing parameter (λ) of 1600
Section V The Analytical Framework- Construction of Composite Indices20 As it is difficult to examine the movements of various indicators simultaneously for predicting the impending crises, policy makers, academia and researchers generally syndicates the moments of various macro-financial, macroeconomic and banking sector indicators and construct composite indices. By studying the behavior of these composite indices they try to forecast the probability of happening of a crisis/financial distress much in advance. Though there are many indices that have been documented in the literature, three indices viz., (i) Index of Speculative Pressures (ISP), (ii) Index of Macroeconomic Vulnerability, and (iii) Index of Banking Sector Vulnerability have been received wider attention in predicting the crises more efficiently.
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We have aggregated various individual variables pertain to macroeconomic, external and banking sector indicators while constructing composite indicators namely Index of Speculative Pressures (ISP), index of macroeconomic vulnerability and banking sector vulnerability. As our objective is not estimating regression models but simply bringing together movements of multiple indicators, we have nether tested the problem of Multicollinearity among the variables nor tested the stationarity of individual indicators considered for computing composite indices, for instance while compiling ISP, we have used three variables namely nominal exchange rate, interest rate and forex reserves, multicollinearity among the three cited variables is not tested.
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While constructing the composite indices, it is also a good practice in the literature to discriminate two aspects (i) risk materialization indicators – generally measured in terms of NPA ratio, CRAR, Return on Assets and (ii) risk accumulation factors – guided variables such as credit growth, output gap etc. Empirical research proved that crises are often preceded by low NPAs, high RoA, these measures generally don’t through any signals before the crisis. On the other hand, high credit growth or output gap usually captures a lot of risk taking and these indicators have significant power in predicting the crisis. In this paper, we have not discriminated risk taking and risk accumulation indicators. However, we have added both of them together while constructing the composite indices. We believe that having a proper mix of indicators with an appropriate theoretical signs (positive or negative) before aggregation generally improves the efficiency of the overall index in signaling the crises. Index on Speculative Pressure Previous research corroborate the fact that crisis as a phenomenon during which significant decrease in exchange rate although monetary authority makes an effort to prevent the currency depreciation by a sharp increase in interest rates, and/or by intervening the foreign currency markets characterized by a large decline in international reserves (Herrera and Garcia, 1999). The Index of Speculative Pressures therefore, combines the information on three indicators namely the nominal exchange rate, the short-term interest rate and international reserves. Although, there are many variables that have been suggested in the literature, good number of studies (see for instance Eichengreen et al, 1996, Kaminsky et al., 1998, Krkoska, 2000, Abiad 2003, and Frankel & Saravelos, 2010) concur the fact that reserves and exchange rate movements are the most statistically significant variables and are highly reliable estimates of crises events. Before combining the movements of the variables, we have normalized them by adopting the following min-max rule. Normalization is very essential to ensure differences in volatility of distinct indicators. The variable ‘X’ is normalized by using the following equation. 𝑵𝒐𝒓𝒎𝒂𝒍𝒊𝒔𝒆𝒅 𝑽𝒂𝒓𝒊𝒂𝒃𝒍𝒆 (𝑿) =
𝑽𝒂𝒓𝒊𝒂𝒃𝒍𝒆(𝑿) − 𝑴𝒊𝒏𝒊𝒎𝒖𝒎 (𝑿) 𝑴𝒂𝒙𝒊𝒎𝒖𝒎 (𝑿) − 𝑴𝒊𝒏𝒊𝒎𝒖𝒎 (𝑿)
It is generally believed that speculative activities exert considerable pressure on exchange rate. The central bank may normalize the changes in the value of currency by buying or selling international reserves. For instance, central bank may run down reserves to secure the value of domestic currency during the periods of depreciation. Furthermore, as documented in the literature (Eichengreen et al, 1996), the interest rate policy can be used to countervail pressure against the value of local currency. Accordingly, interest rates need to be increased to a certain level which can attract shortterm capital flow into the country to protect the value of domestic currency. We have constructed the Index of Speculative Pressures (ISP) on the lines of Herrera and Garcia, (1999) and Ndung’u et al., (2014) as follows 𝑰𝑺𝑷 =△ 𝑵𝑬𝑹 +△ 𝑰𝑹 −△ 𝑭𝑹
12
Where 𝐼𝑆𝑃 𝑖𝑠 the Index of Speculative Pressures, △ 𝑁𝐸𝑅 is the change in nominal exchange rate, △ 𝐼𝑅 is the change in short-term interest rates. Call/ notice money has been used as a proxy for short-term interest rate and △ 𝐹𝑅 is the change in foreign reserves. As mentioned in the methodology, a signal is generated when an indicator crosses a pre-defined threshold limit, accordingly, a period of speculative pressures is defined as the period during which the ISP crosses the pre-specified thresholds. In this paper, a crisis period is said to occur when: 𝑰𝑺𝑷 > 𝜇 + 0.5𝝈 We have also estimated thresholds by adding one standard deviation as well as 1.5 standard deviation to the mean value of ISP, we found that 0.5 standard deviation above the mean value could able to identify crises most accurately in more than 40 per cent cases with reasonably less noise. Figure 2 portrays the moments of ISP and the individual indicators used for constructing the combined index (i.e., ISP) during the sample period 1986 to 2016. The conditional probability of predicting the crises given the signal is emitted by the ISP indicator remained at 57 per cent. The normalised movements of ISP index along with its components are furnished in the following Figure 2 Figure 2: Movements of Index of Speculative Pressures and Its Components Index of Speculative Pressures 1.60
Excahnge Rate 1.20
1.40 1.00
1.20
1.00
0.80
0.80 0.60
0.60
0.40 0.40
0.20
-0.40
0.20
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
0.00
ISP
Threshold
Interest Rate
Forex Reserves to GDP Ratio 1.20
1.00
1.00
0.80
0.80
0.60
0.60
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0.00
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1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
1.20
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
-0.20
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
0.00
Note: The variables presented in the above chart are standardised variables and not the actual variables
The Index of Macroeconomic Vulnerability Literature highlights that Index of Macroeconomic Vulnerability is fabricated as the summation of the fiscal deficit, current account and inflation. Fiscal deficit and current account deficit are measured as percentages to GDP and the inflation is worked out as the year-on-year percentage change in appropriate price index (i.e., Consumer Price Index or Wholesale Price Index). In other words, IMV essentially brings together movements of three key macroeconomic imbalances viz., fiscal management, external imbalances and domestic prices. The higher the IMV, the greater the macroeconomic
13
imbalances, and more vulnerable the economy to distress or a crisis. Accordingly, many researchers through their empirical work envisaged that IMV quantifies economic fragility well in advance by emitting signals. Generally, indicators which measure the macroeconomic vulnerability are constructed based on complex models by making use of sophisticated statistical procedures. In contrast, the IMV, is easy to calculate and comprehend. On account of simplicity in computational aspects, IMV has been considered as one among the excellent EWI and the same has been proved as an indicator which red-flags the presence of systemic vulnerabilities as early as possible21. The IMV is compiled based on the following equation. 𝑰𝑴𝑽 = 𝑮𝑭𝑫 + 𝑪𝑨𝑫 + 𝑰𝑵𝑭 + 𝑹𝑬𝑻 As our objective is not estimating regression relationship, we have not verified whether there exists Multicollinearity problem among variables included in the composite indices. The economic logic for choosing the variables such as GFD, CAD, INF and RET is explained below. Fiscal deficit arises whenever, government spending exceeds its income. Economists and policy analysts envisages that fiscal deficits crowd out private borrowing, manipulate capital structures22 and interest rates, decrease net exports, and lead to either higher taxes, higher inflation or both. One of the factors responsible for Asian crisis of 1997 was countries were running with large current account deficits and they were financing these deficits by attracting capital flows. When the confidence in the country fall, these flows will dry up, leading to macroeconomic imbalances in the form of rapid devaluation of domestic currency, lower growth of export sector. Price stability or a relatively constant level of inflation, allows businesses to plan for the future. Theoretically, it also allows economies to promote maximum employment. Inflation is a crucial factor in determining the flow of foreign investments in the economy. A high rate of inflation signifies economic instability and generally distort the economic activities, leading to lessor inflow of foreign capital. On the other hand a low and stable inflation rate acts as a sign of internal economic stability, as the same reduces uncertainty and boosts confidence of people and business houses for making investment decisions. After construction of IMV, we investigate whether or not the index is able to throw precrisis signals well in advance. After grid search, we found that 0.5 standard deviation above the mean value of IMV could able to identify cries more correctly. 𝑰𝑴𝑽 > 𝜇 + 0.5 𝜎 21
However, previous research particularly studies conducted by Kaminsky et al., (1998), Herrera and Garcia (1999) and Ddung’u (2014) used three leading indicators viz., namely Real Effective Exchange Rate (REER), domestic credit growth and ratio of international reserves while compiling IMV. However, for improving the efficiency of IMV, some authors also include return on stock market and besides the previously mentioned three indicators (see for instance Gavin (1986), Dornbusch (1980)). 22
The capital structure is how a firm finances its overall operations and growth by using different sources of funds. Debt comes in the form of bond issues or long-term notes payable, while equity is classified as common stock, preferred stock or retained earnings. Short-term debt such as working capital requirements is also considered to be part of the capital structure.
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The good signal ratio of the IMV remained at 56 per cent and has acceptable noise ratio of 24 per cent. The normalised movements of IMV index along with its components are furnished in the following Figure 3. Figure 3: Trends in Index of Macroeconomic Vulnerability and Its Components Return on Stock Market
Index of Macreconomic Vulnerability 3.00
1.20
2.50
1.00
2.00
0.80
1.50 0.60 1.00 0.40
0.50
0.20
IMV
0.00
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
0.00
Threshold
Inflation
1.00
1.00
0.80
0.80
0.60
0.60
0.40
0.40
0.20
0.20
0.00
0.00
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
1.20
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Gross Fiscal Deficit
1.20
Current Account Deficit 1.20 1.00 0.80 0.60 0.40 0.20
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
0.00
Banking Sector Vulnerability The third composite index constructed to measure the crisis periods is Index of Banking Sector Vulnerability (IBSV). For constructing IBSV, we have used variables on business performance (credit growth, deposit growth), asset quality, capital adequacy and profitability. As mentioned earlier, the choice of variables is largely determined the availability of variables for longer time-periods in the public domain23. Before consolidation, we have standardized all variables by using min-max principle. Previous research on the topic confirmed that high growth in credit/deposit generally accumulates vulnerabilities in the economy. The index is constructed based the following equation. 𝑰𝑩𝑺𝑽 = 𝑮𝑩𝑪 + 𝑮𝑩𝑫 + 𝑮𝑵𝑷𝑨 − 𝑪𝑹𝑨𝑹 + 𝑹𝑶𝑨 Where 𝐼𝐵𝑆𝑉 is the Index of Banking Sector Vulnerability, 𝐺𝐵𝐶 is the Growth in Bank Credit, 𝐺𝐵𝐷 is Growth in Bank Deposits, 𝐺𝑁𝑃𝐴 is Gross Non-performing Assets to Total Advances,𝐶𝑅𝐴𝑅 is Capital to Risk Weighted Assets Ratio, 𝑅𝑂𝐴 is the Return on Assets. Bank of India, compiles banking stability indicator by using five indicators – capital adequacy, asset quality, profitability, liquidity and efficiency. We were unable to use liquidity and efficiency variables as the information on these is not readily available. 23Reserve
15
After iterative process, we understand that 0.5 standard deviation above the mean value of IBSV could forecast the cries accurately. Accordingly, the index estimated crisis events in around 60 per cent cases with a noise ratio of 36 per cent. The behavior of IBSV and normalized bank performance variables are presented in the following figure 4. Figure 4: Movements in Index of Banking Sector Vulnerability and Its Components Index of Banking Sector Vulnerability
Credit Growth
4.00
1.20
3.50 1.00
3.00 2.50
0.80
2.00 0.60
1.50 1.00
0.40
0.50 0.20
2016
Deposit Growth
2016
2015
2014
2013
2012
2011
2010
2009
2008
2006
2005
2007 2007
2008
2009
2010
2011
2012
2013
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2006 2006
2005
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2000
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1996
2016
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2013
2012
2011
2010
2009
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2007
2006
Return on Assets 1.20 1.00 0.80 0.60 0.40 0.20
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
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1998
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0.00
1996
2005
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0.00
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0.00
2002
0.40
0.20
2001
0.60
0.40
2000
0.80
0.60
1999
1.00
0.80
1998
1.00
1997
1.20
1997
CRAR
1.20
1996
2007
Gross NPA
2005
2004
2003
2002
2001
2000
1999
1998
1996
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
0.00
2003
0.20
0.00
2002
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0.20
2001
0.60
0.40
2000
0.80
0.60
1999
1.00
0.80
1998
1.00
1997
1.20
1997
Leverage Ratio
1.20
1996
2004
2003
2002
2001
2000
1999
1998
Threshold
1997
2015
2014
2013
2012
2011
2010
2009
2008
0.00
1996
BSV
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
0.00
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Multivariate Super Index (Systemic Financial Stability Index) An attempt also made to fabricate multivariate super index by assembling indicators representing speculative pressures, macroeconomic and banking sector vulnerabilities. Indices which aggregates a diversified set of indicators are extensively used to provide directional analysis of degree of vulnerability. The super index computed can able to provide valuable insights of vulnerability spread across three dimensions namely external, domestic and banking sectors. The trends in multivariate super index are presented in the following chart. Before the crises episodes, it appears that the index clearly shown some degree of excessive volatility in its behaviour. Figure 5: Trends in Multivariate Super Index Multivariate Super Index 2.00 1.80
1.60 1.40 1.20 1.00
0.80 0.60 0.40 0.20
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
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1997
1996
1995
1994
1993
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1991
1990
1989
1988
1987
1986
0.00
Multivariate Stability Map Multivariate stability map shows that banking sector vulnerability worsened in 2016 as compared with last three preceding years, while speculative pressures and macroeconomic vulnerability showed improvement. The banking sector vulnerability deteriorated in 2016 largely on account of increase in Gross NPAs and decrease in CRAR and profitability of banking sector. Banking Sector Vulnerability 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0
Speculative Pressures
Macroeconomic Vulnerability
2013
2014
2015
2016
17
Section VI Scope for future research The assessment of performance of two approaches reveals that multivariate logit regression models can improve upon univariate signaling models. It was not possible to attempt logit regression analysis in this single country framework due to non-availability of quarterly data series on banking sector variables in the public domain. The trend of cyclisation (financial and business) has come to stay. To that extent, EWM needs further nuancing to be able to smell bubbles. A ‘Bubble Threat Warning System’ should be a logical extension for future research. Bubbles carry different stages of transformation as seen in the following picture24i. As may be clear, the data should allow the policy maker to smell the bubble before the stage of “take off” that when an idea gets brewed into the making of a bubble. It would be then possible to make use of macroprudential tools to soften it. But this would require choicest variables (say leverage ratios across banks and price rises in an important class of assets viz. land, housing, equities etc.) and sufficient volume of data and may be much finer models to achieve this. If leverage ratios go off the board (permissible limits as set by the model) and/or prices of the underlying assets tend to rise rapidly (beyond tolerable limits as given by the model), the EWM should flash these as ‘red alerts’ to prevent investing public to keep off and the banks to revert to permissible levels by way of regulatory interventions. These should continue till the threat tends to moderate. The transition across various cyclical phases of a bubble need to be estimated. The length and intensity of various such phases need to be explained in sync with the state of affairs of the economy. The EWM should be able to differentiate between the acute phase of the crisis and the subsequent recovery period.
24
Developed by Jean-Paul Rodrigue, a professor at Hofstra University in January 2006, the chart depicts the stages of bubbles as “backed up by 500 years of financial history.
18
This brings to fore another space for attention, namely sectoral credit. If the growth rate in a particular (or sensitive) sector outpaces that of the overall credit growth that could be smelt as a possible bubble in the making. Simple ‘Industry limits’ are prescribed for this purpose without regard to its interconnectivity with other sectors (cement and steel with housing sector) which might be the ‘more influencing factor’ for the possible overall distress to set in. This is warranted for a more effective EWM but requires complex models to be configured. It is difficult to measure interconnectedness due to the issues of data unavailability and also due to their nebulous interpretability. Presence of different types of domestic and international imbalances and multi-dimensional determinants of vulnerabilities to external shocks has made the very process of identification of EWIs cumbersome. The post-crisis effect on GDPs of various countries tended to vary, as also their recovery periods from the end of the most severe phase of the crisis. Separate models are being envisaged to capture its effects. Stress Testing Framework (STF) is a robust counter-verification system post EWM. Based on the alerts given by the EWM, it would be desirable to subject the specific sector under question to a stress test to assess the intensity of possible vulnerabilities. STF should be more comprehensive and aims at identifying stresses which are not observable readily from EWM. It should also indicate the quantified distance from distress (say regulatory capital in case of test of solvency) and create the possibility for focused regulatory attention/response. One more partly unresolved issue is the characteristic of being ‘forward looking’. A combination of EWM and STF could answer this but an empirical model would be helpful, which, while being able to define the required extent and quality of ‘forward looking–ness’, should be able to display the level of honesty in ‘predicting’ distress. It may be worth mentioning that even in the developed world, the quest for an appropriate systemic risk measurement system, which includes a macro stress testing model and a forward looking early warning model, is still on. And going by the progress so far, it will continue for some more time. Section VII EWM – An essential part of the overall architecture of macro prudential Policy The macro-prudential policy framework essentially needs a mechanism to detect buildup of system wide risks and threats to financial system. In the absence of a scientific measure for systemic risk, EWM could act as the next best alternative. EWM should have the efficiency in identifying the risk and distress in the system. Indicators should be a chosen few from various sub-sectors, which can inform state of health of the sector it represents. They should be juxtaposed into an index to represent the combined risk of the Sector. And finally the sector-specific index should be statistically joined into a Super Index (Systemic Financial Stability Index) to represent the whole financial sector which can also be called as Systemic Financial Stability Index. Therefore, making the EWM as an operational part of overall architecture of macro-prudential policy framework is
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important. EWM should be simple to calculate, easy to understand, universally acceptable and above all credible. Each indicator should flash ‘red’ if respective threshold is exceeded so that adequate attention is paid in terms of policy response after an incisive ‘probe’ on the potential ‘do’s’ and don’ts’25. All potential stakeholders should understand and accept the importance of these indicators so that response functions of both the policymakers and the market participants can be accordingly tailored and there is little element of surprise. This can be achieved through making EWM part of (i) quarterly MIS26 and (ii) Financial Stability Report published by the designated Macroprudential Authority (MA)27 and (iii) periodical press releases and or speeches by spokespersons. This way, the stakeholders can get the required alert. The MA should implement it in a ‘Top Down’ manner. They should collect data pertaining to all variables/indicators28, assess the possibility and extent of systemic risk and take remedial actions. This approach will be more beneficial if the exercise is done in a ‘Bottom Up’ manner also. All the structural units of the MA (namely, Government and other regulators) and further the regulated entities (banks, non-bank financial companies, insurance agencies, pension funds etc.) should also have the same EWM for their respective space and share it with the MA. This should be made definitely a part of regulatory requirement for the identified D-SIBs. Section VIII Conclusion Financial Crises do not land as bolt from the blue. Still policymakers get caught napping. The need to have a pre-emptive mechanism to handle these episodes as and when they occur are well-appreciated in literature; still financial crisis manager miss out on them every time. The Asian crisis of late 1990s, the failure of Lehman Brothers in September 2008, the Sovereign default crisis of Europe (Greece, Ireland, and Portugal and the health of Spain's banking sector (collectively, the GIPS countries), all tend to testify the Sometimes ‘thresholds’ could prove situation-specific or country-specific. For example, in developing economies like India, the thirst is for more and more credit going into production and hence helping growth and employment. In such cases, it may not be correct to initiate policy action of constraining overall credit growth in response to revealed numbers breaching the defined ‘threshold’. A more nuanced action of using macroprudential tools to hit credit growth in the specific sector may be considered. 25
26
Should be meant for restricted internal circulation only. Those could be as granular as possible to reflect the exact picture to arouse commensurate seriousness on the part of the policy maker. 27
There are counter comments on this. Despite having EWM and its highlights flashed in FSRs, financial crises keep on happening. FSR-July 2006-BOE had in fact indicated about rising UK household indebtedness creating pockets of vulnerability. FSR-April 2007-BOE had singled out 16 “large complex financial institutions” as having the potential to put the financial system at risk. It also noted smaller concerns could pose a threat by virtue of their position in key markets. But it is not a sad comment on requirement of FSR but on the inactive regulatory response system. 28
The last crisis had revealed a serious data dimension - weaknesses in the quality of existing data and/or because of a lack of relevant data. For example, the degree and location of leverage or excessive risktaking within financial system particularly as regards the unregulated or lightly regulated shadow banking system was beyond regulatory information system. Similarly, the liquidity, credit and tail risks within the regulated sector were not in the know. Given the prevalence of the risk transfer instrument, it was not possible to know where exactly the risk was lying.
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same tendency called in literature as “disaster Myopia”. Every company practice to invest on fire-fighting measures despite having not seen even one such disaster in decades as it discounts the severity of possible future losses at present value and compares it with current cost of fire-fighting mechanism at place. It could be beneficial to try, if not prevent such crisis, at least, to minimize the extent of damage it might inflict if it ever happens29. This could be the best example in defense of why countries should have ‘Financial Crisis Management Frameworks” of which “Early Warning Mechanism” is a necessary cannon. The intensity of inter-connectedness : inter-economy (thanks to spillover effects of globalisation), intra-economy (real and financial sector), inter-sector (banks and non-banks) and intra-sector (bank to bank) has made the study of predicting and preparing for onset of financial crisis quite intricate but immensely interesting. Despite the development of large body of empirical literature on early warning framework, accurate forecasting of crises is still a very challenging task. For testing the early warning framework, research essentially highlights two approaches (i) Signal Extraction Approach (SEA) and (ii) Logit Regression Analysis (LRA). It has been cited in the literature that SEA is mostly suitable for developing country specific early warning framework while LRA is basically meant for framing cross-country early warning mechanism. In this paper an attempt is made to locate various macro-financial, macroeconomic, banking, and market indicators, which have sound statistical power of forecasting financial distress / crises. Accordingly, we have chosen 19 key indicators. The efficacy of these indicators in predicting the crises is assessed through ‘Signal Approach’, which has been considered as the most widely used approach in developing country specific early warning framework. As it is quite difficult to monitor the movements of each and every individual indicator simultaneously in forecasting the crises events, an attempt is also made to build three composite indices, viz., Index of Macroeconomic Vulnerability, Index of Speculative Pressures and Index of Banking Sector Vulnerability by aggregating suitable indicators, which have theoretical explanatory power in estimating vulnerabilities in the economy. Such hybridization of inter-sectoral variables has been attempted to justify “mutually reinforcing processes between the financial and real sides of the economy. For instance, as the economy grows, cash flows, incomes and asset prices rise, risk appetite increases and external funding constraints weaken, thereby generating potentially large financial imbalances. At some point, these imbalances have to unwind, potentially causing a crisis, characterised by large losses, liquidity squeezes and possibly a credit crunch” – (Drehmann and Juselius – 2013). Finally, a supercomposite indicator is constructed reflecting the state of financial instability in the whole system. The results showed that few indicators (such as ‘credit to GDP gap’, ‘output GAP’, ‘Credit growth’, ‘Deposit growth’, ‘CRAR’ ‘Gross NPA Ratio’ and ‘ROA’ performed well in 29
EWIs could be considered ideal if the crisis they signal does materialize. But they don’t happen that way in real world. A few “False Alarms” would be inevitable. It is the trade-off between “the rate of missed crises against the rate of false positives (i.e. the percentage of signals they emit for crises that do not happen)” (Drehmann, Tsatsaronis-2014). But it should be a policy maker’s delight to err on the right side i.e. it is alright to react to a false signal and try to correct a position that would not happen THAN not to respond to possible onslaught of vulnerability. But sometimes, the cost of reacting to false signals could be too costly especially when the reaction means imposing hard regulations having economy-wide implications. But these are part of the art of policy making which should be implemented in a calibrated manner after feeling the rock inside water.
21
identifying the financial distress with good signal and low noise ratios. However, some other indicators such as gross fiscal deficit to GDP, forex reserves to GDP and return on stock market, although displayed reasonably good signals ratio also generated notable noise ratio. The results are in line with the earlier findings of Behn et al (2013) and (Drehman and Juselius-2013). Banking sector variables are seen to exert a significant influence on build-up of financial vulnerabilities. Inadequately capitalised banking sector is a precursor to vulnerability. This is in sync with the Basel proposal of a notion of higher CCB rates in the advent of possible state of vulnerability. Credit-GDP gap showing signs of a good EWI would gel well with this policy prescription. Similarly, position of high profitability (ROA) will encourage more lending (Credit growth) meaning more risk taking and thus leading to increased vulnerabilities in the form of higher level of contamination in loans (NPA ratio). The recent episode of severe distress in India’s banking sector testifies this. The analysis suggests that a clear “quantitative definition of crisis”, in respect of banks in India, is possible. For example, the three critical indicators could be - credit growth exceeding 19 per cent, CRAR falling below 11.75 per cent and ROA30 growing more than 1 per cent. This is what an ideal functional EWM would represent as per Davis and Karim (2008). It tends to satisfy the three pillars of (i) Timing, (ii) Stability and (iii) interpretability as proposed by Drehmann and Juselius (2014). All the chosen indicators showed signals of impending crisis before 2 years of their happening. They are also stable in foretelling crisis with reasonably low noise (being off when crisis does occur). Further, as explained above, these results are simple to understand and interpret. Further, composite indices performed better in signaling the crises well in advance. For example, in case of the composite banking sector vulnerability index, the good signal ratio is displaying better performance as compared to individual banking sector indicators as above. The noise ratio looks better as well. The jury is still out on what should be an effective31 EWM and whether it resolves the issues for which it is administered. EWM is being extensively adopted to identify financial distresses in the financial system, primarily to be able to initiate suitable policy actions to contain extent of possible damages. Thanks to many of the forecasts about the mishap having gone wrong, it has incurred enough ‘credibility deficit’ as well, a trend similarly seen in cases of the Meteorologists and Economists. Thus, EWM should be effective – fool proof in its prediction - i.e. the EWM should be able to predict reasonably accurately an impending crisis. But it continues to be “better said than done”.
30
The exercise was tried out for the indicator ROE too. The results showed similar trend.
31
But in contrast, it may be argued that an effective EWI may tend to fall in to the definitional trap of the usual Lucas Critique implying that once these are able to track distress adequately and are able prevent them to appear (in less harmful state), they may get susceptible to lose their ‘leading indicator’ characteristics. “For instance, if banks are forced to build up buffers based on signals issued by well specified EWIs, they would be more resilient toward busts, which in turn could make crises less likely. As Drehmann et al (2011) argue, however, the loss of predictive content per se would be no reason to abandon the scheme – it would be rather a sign of its success (Drehmann and Juselius – 2013)”.
22
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Annex 1: Early Warning Indicators and Its Efficiency S.No.
(1)
Distress Indicator
Definition Adopted
(2)
(3)
Threshold (Actual Ratio/Indicator)
(4)
Critical Region
Good Sign al Ratio
False Signal Ratio
Noise to Signal Ratio
Probability of Stress given a Signal
Conditional Probability of Distress minus Unconditional Probability of Distress
(5)
(6)
(7)
(8)
(9)
(10)
Composite Indices 1)
Index of Speculative Pressures (ISP)
Aggregation of nominal exchange rate, interest rate and foreign reserves
0.81
0.81
0.44
0.14
0.32
0.57
0.27
2)
Index of Macroeconomic Vulnerability (IMV)
Summation of Gross Fiscal Deficit and Current Account Deficit and Inflation Rate and Return on Stock Market
1.96
1.96
0.56
0.24
0.43
0.50
0.20
3)
Index on Banking Sector Vulnerability (IBSV)
Consolidation of credit/deposit growth, CRAR, Gross NPA and RoA
2.65
2.65
0.56
0.36
0.65
0.56
0.11
32
S.No.
4)
Distress Indicator
Definition Adopted
Multivariate Super Index (Systemic Financial Stability Index)
Average of ISP, IMV and IBSV
Threshold (Actual Ratio/Indicator)
1.5
Critical Region
Good Sign al Ratio
False Signal Ratio
Noise to Signal Ratio
Probability of Stress given a Signal
Conditional Probability of Distress minus Unconditional Probability of Distress
1.5
0.30
0.30
1.00
0.33
0.00
0.70
0.25
0.36
0.58
0.25
13.0% or -180%
0.70
0.30
0.43
0.54
0.21
More than 7%
0.44
0.24
0.54
0.44
0.14
Macro-financial Indicator 5)
Credit to GDP Gap
Difference between credit to GDP ratio and its trend
Gap is more than 1.74%
1.74%
Macroeconomic Indicators 6)
Output GAP
Difference between actual GDP and potential GDP
7)
Inflation
Growth in WPI
Change in Output gap is more than 13% or less than 180% More than 7%
33
S.No.
Distress Indicator
Definition Adopted
Threshold (Actual Ratio/Indicator)
Critical Region
Good Sign al Ratio
False Signal Ratio
Noise to Signal Ratio
Probability of Stress given a Signal
Conditional Probability of Distress minus Unconditional Probability of Distress
8)
Fiscal Deficit
Gross Fiscal Deficit as a percentage to GDP
More than 6.16%
6.8%
0.70
0.55
0.79
0.39
0.06
9)
Forex Reserves to GDP (%)
Change in forex Reserves to GDP Ratio
-0.35% or 1.31%
0.90
0.45
0.50
0.50
0.17
10)
External Debt to GDP
External Debt as a percentage to GDP
26%
0.11
0.29
2.65
0.17
-0.18
Change in the ratio is less than -0.35% or more than 2.19% More than 26%
Market Indicators 11)
Lending Rate (%)
12)
Interest Rate (%) Call/Notice Money Rate
13)
SENSEX
Lending rate is proxied by SBI Advance Rate
Return on SENSEX
More than 16%
16.4%
0.30
0.35
1.17
0.30
-0.03
More than 10.7%
10.7%
0.20
0.15
0.75
0.40
0.07
0.28
0.36
0.32
0.87
0.40
0.03
Return is more than 28%
34
S.No.
14)
Distress Indicator
Definition Adopted
BSE-100
Return on BSE-100
Threshold (Actual Ratio/Indicator)
Return is more than 29%
Critical Region
Good Sign al Ratio
False Signal Ratio
Noise to Signal Ratio
Probability of Stress given a Signal
Conditional Probability of Distress minus Unconditional Probability of Distress
0.29
0.27
0.32
1.16
0.33
-0.03
21.0%
0.30
0.20
0.67
0.43
0.10
19%
0.40
0.25
0.63
0.44
0.11
54 % 73%
0.50
0.35
0.70
0.42
0.08
Banking Sector Indicators 15)
Credit Growth (%)
Growth (y-o-y) in credit provided by Banks
More than 21.0%
16)
Deposit Growth (%)
Growth (y-o-y) in deposits collected by the Banks
More than 19%
17)
Credit-Deposit Ratio (%)
Credit as a percentage to Deposits
Less than 54 % and more than 73%
18)
Leverage Ratio
Total assets to Owned Fund
More than 17
More than 17
0.22
0.27
1.23
0.40
-0.05
19)
CRAR (%)
Capital to RWA Ratio
Less than 11.75%
11.75
0.50
0.17
0.33
0.67
0.27
35
S.No.
Distress Indicator
Definition Adopted
20)
Gross NPA Ratio (%)
Change in Gross NPA Ratio
21)
Return on Assets (%)
Net Profit as a % to total assets
Threshold (Actual Ratio/Indicator)
Critical Region
Good Sign al Ratio
False Signal Ratio
Noise to Signal Ratio
Probability of Stress given a Signal
Conditional Probability of Distress minus Unconditional Probability of Distress
Above 3.0 %
1.85%
1.00
0.10
0.10
0.67
0.50
More than 1.0%
1.00%
0.25
0.14
0.57
0.50
0.14
External Vulnerability Indicators 22)
Exchange Rate
Exchange rate of Rupee against US $
Increase in the exchange rate by more than 13.8%
More than 13.8%
0.18
0.16
0.87
0.40
0.03
23)
Current Account Deficit (CAD)
Change in the ratio CAD as a % to GDP
More than 0.5%
2.14%
0.45
0.37
0.81
0.42
0.05
Note: 1. Trend in Credit to GDP Ratio is estimated by using HP filter with smoothing parameter (λ) of 1600. As suggested by Basel Committee on Banking Supervision, we have also used smoothing parameter (λ) of 400000, however, there is no much change in the ratio. 2. Potential GDP is calculated by using HP filter with smoothing parameter (λ) of 100 3. Return on SENSEX is a logarithmic return i.e., return for time period t, 𝑹𝒕 = 𝑳𝒏(
𝑺𝑬𝑵𝑺𝑬𝑿𝒕 𝑺𝑬𝑵𝑺𝑬𝑿𝒕−𝟏
)
4. Department of Banking Supervision, Reserve Bank of India, also designed a framework of early warning indicators for measuring vulnerabilities at bank level. The department prescribed thresholds for five indicators – (i) growth in advances (above 18%), (ii) CRAR (less than 9%), (iii) Net NPA ratio (above 3.35%), (iv) RoA (less than 0.40%) and (v) LCR (below 60%)
36
37
Figure 4: Early Warning Indicators - Heat Map (March 2015 and March 2016) Deterioration MACRO-
Improvement
Credit to GDP Gap CAD (% to GDP)
EXTERNAL SECTOR VARIABLES
Exchange Rate (INR Vs $) Return on BSE-100 (%) Return on SENSEX (%) Interest Rate (%)
MARKET
Lending Rate (%) External Debt (% to GDP) Forex Reserves (% to GDP) Fiscal Deficit (% to GDP) Output Gap RoA (%) CRAR (%) Gross NPA Ratio (%)
BANKING
Leverage Ratio C-D Ratio (%) Deposit Growth (%) Credit Growth (%)
Each indicator is normalized to have a mean zero and standard deviation one. The direction of arrow displays the direction of change in the indicator. An orange leftward arrow depicts deterioration of the indicator, a blue rightward arrow depicts imporvement in the indicator.
38
Annex-2: Early Warning Indicators Chosen in Various Empirical Studies Study Early Warning Indicators Virtanen T, et al., Credit-to-GDP ratio (2016) Debt-service ratio and Real house prices Loloh W F (2015)
Credit to domestic private credit (measure of credit risk), Bank deposits (liquidity) and Foreign liabilities (foreign exchange risk)
Joy Mark et al., Net interest rate spread, Government (2015) yield, Financial development, Trade openness, Short-term interest rate, Currency, appreciation Rancan Michela Total assets to GDP, Non-core et al., (2015) liabilities, Debt to equity, Debt securities to liabilities, Mortgages to loans, Loans to deposits, Real GDP, Inflation, Stock prices, House prices, Long-term Govt. yield, Interest investment to GDP, Government debt to GDP, Private credit flow to GDP Drehmann M and Credit to GDP gap Kostas Tsatsaronis (2014)
Betz Frank et al., Bank Specific indicators: Capital (2013) ratio, Tier- I capital, Impaired assets, Reserves to impaired assets, RoA, Loan loss provisions, Cost to income, RoE, Net Interest margin, Interest expenses to liabilities, Deposits to funding, Net short-term borrowing, Share of trading income, Total assets to GDP, Non-core liabilities, Debt to equity, Loans to deposits, Debt securities to liabilities Mortgages to loans Macro financial Indicators: Real GDP, Inflation, Stock prices, House
Remark All indicators performed quite well in signaling the alarms well in advance of onset of crisis The composite index constructed based on three mentioned indicators predicted the levels of fragility and risk taking within the Ghanaian banking sector well in advance The authors found that the measures are very useful in signaling currency and banking crisis The performance of ‘Mortgages to loans’, ‘Real GDP’, ‘Inflation’, ‘Govt. debt to GDP’ and ‘Private credit flow to GDP’ is very good in signaling the crisis in advance and are statistically significant The authors through their empirics confirmed that for a large cross section of countries and crisis episodes, the credit-to-GDP gap is a robust single indicator for the build-up of financial vulnerabilities Capital ratio, RoA, Cost to income, interest expenses to liabilities, deposits to funding, Total assets to GDP, Non-core liabilities, Debt to equity, debt securities to total liabilities, mortgages to loans, real GDP, inflation, house prices, InIP to GDP, govt. debt to GDP and private credit to GDP are statistically significant in predicting crises in European banks
39
Study
Navajas M. C and Aaron Thegeya (2013)
MinoiuCamelia, C. Kang, V.S. Subrahmanian, A. Berea (2013) Behn Markus, CarstenDetken, Tuomas A.P and Willem Schudel (2013) Drehmann Mathias (2013)
Babecky Jan, Tomas Havranek, Jakub Mateju, Marek Rusnak, Katerina Smidkova and BorekVasicek (2012)
Early Warning Indicators prices, Long-term government yield, International investment position to GDP, Government debt to GDP, Private sector credit flow to GDP Capital to risk weighted assets, Nonperforming loans net provisions to capital, Nonperforming loans/total loans, Return on equity, Interest margin to gross income, Non-interest expenses to gross income, GDP (real) growth, Broad money/international reserves, Inflation, Credit to the private sector, Current account balance / GDP, Monetization, Real exchange rate, Credit default swap spread Per-capita GDP, Net foreign assets/GDP Capital inflows, Forex reserves to GDP Credit Growth, Credit to GDP Gap, GDP Growth, Inflation, Equity Price Growth House Price Growth, Banking sector capitalisation, Banking sector profitability Total credit (i.e., all sources of credit to the private non-financial sector) to GDP Gap
Government balance (%GDP), real GDP, seasonally adjusted GDP, Unemployment rate (%), corporate bond spread, Gross total fixed capital formation (constant prices), Commodity prices, Current account (%GDP), Domestic credit to private sector (%GDP), FDI net inflows (%GDP), Government consumption (constant prices), Government debt (%GDP), Private final consumption expenditure (constant prices), Gross liabilities of personal sector
Remark
Capital to risk weighted assets, Return on equity, Noninterest expenses to gross income, Broad money/international reserves, Inflation and monetization are statistically significant indicators in predicting banking crises
Capital inflows and forex reserves to GDP are found statistically significant in predicting financial crises Credit to GDP gap, House price growth, Banking sector capitalization and its profitability are significant early warning indicators Efficiency of ‘credit to GDP Gap’ in predicting crises/distresses improved by taking account of all sources of credit to the private nonfinancial sector, rather than just bank credit Domestic housing prices, Share prices, Credit growth, and Private credit are found most important early warning indicators out of 33 potential ones chosen for the study
40
Study
Early Warning Indicators Remark House price index, Industrial production index, Industry share (%GDP), Consumer price index, M1, M3, Money market interest rate, Nominal effective exchange rate, Net national savings (%GNI), Stock market index, Total tax burden (%GDP) Terms of trade, Trade (%GDP),Trade balance, Global domestic credit to private sector (%GDP), Global FDI inflow (%GDP) Global inflation, Global GDP, Global trade (constant prices), Long term bond yield – money market interest rate Marchettini D and Credit growth When the countries at an early Rodolfo Maino stage of financial (2015) development, credit expansion is positively related with economic growth. In countries with a developed financial system, rapid credit growth is frequently associated with excessive risk-taking, and the likelihood of a systemic banking crisis. In these countries, an acceleration of credit growth above its secular trend is likely to reflect systemic risk build-up rather than additional financial deepening. Anundsen A.K, Private credit growth, Household Except ‘Global credit to GDP K.Gerdrup, F. credit to GDP gap, Non-financial gap’, all variables are Hansen, and K.K. enterprises credit to GDP gap, House statistically significant early Sorensen (2014) price to income gap, Non-core funding warning indicators gap, Equity ratio, Global credit to GDP gap, Global house prices to income gap, Output gap Drehmann M and Credit-to-GDP gap, Credit growth, Credit-to-GDP gap, and debt Mikael Juselius Debt Service Ratio, Equity price gap, service ratio (DSR) (2013) Equity price growth, GDP growth, consistently outperform other Non-core liability ratio, Property price measures. The credit-to-GDP gap, Property price growth gap is the best indicator at longer horizons, whereas the
41
Study
Tiberiu A.C, and C.S.Loana (--)
NjugunaNdung’u, Lydia Ndirangu2 Conrado Garcia, EsmanNyamongo and CiliakaGitau (2013)
Laina P, JuhoNyholm, Peter Sarlin (2014)
Early Warning Indicators
Remark DSR dominates at shorter horizons Banking Ratings’ deterioration, by The authors’ intend to build means of CAAMPL method. The early warning system based method consists of the following key on banks’ ratings deterioration ratios: Capital Adequacy Ratio 1 (Equity/Risk Weighted Assets), Capital Adequacy Ratio 2 (Capital/Risk Weighted Assets), Equity Ratio (Equity to total net assets), General Risqué Ratio (Risk weighted balance sheet and offbalance sheet assets /accounting value balance sheet and off-balance sheet assets), RoA, RoE, Liquidity Indicator (actual liquidity/required liquidity), Immediate liquidity [(deposits at banks + treasury securities)/(loans from attracted funds)] Change in nominal exchange rate, By making use of mentioned Change in 91 day Treasury bill rate, indicators the authors Change in international reserves, constructed two composite REER, Real domestic credit growth, indices viz., Index of Ratio of international reserves Speculative Pressures and Index of Macroeconomic Vulnerability and the authors found that composite indices performed well in signaling the forthcoming crises. House Prices: Real house prices, The authors found that all the growth indicators performed well in Real house prices, proportional HP- identifying financial distresses trend deviation in the economy Mortgages: Real mortgages, growth, Real mortgages, proportional HPtrend deviation Mortgages to GDP*, HP-trend deviation, Real interest rate of mortgages Other loans: Real household loans, growth Real household loans, proportional HP-trend deviation
42
Study
Early Warning Indicators Remark Households loans to GDP*, HP-trend deviation Real private loans, growth, Real private loans, proportional HP-trend deviation Private loans to GDP*, HP-trend deviation Loans-to-deposits: OECD loans to deposits OECD loans to deposits, growth, ECB loans to deposits, ECB loans to deposits, growth Macro: Real GDP growth, Inflation, Current account deficit to GDP
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