Indicators Asset Price : A Role for Global Liquidity Lucia Alessi European Central Bank Carsten Detken Milan, 24 June 2010
The need for Models The set up of an System is one of the key tasks of the European Systemic Risk Board With the aim of identifying threats to financial stability in a timely manner, and allow for the adoption of targeted macro-prudential regulatory measures Implications of financial stability for price stability in the medium to long run EWMs are necessary input for leaning against the wind New generation of EWMs
Literature and contributions The target The literature on Indicators has typically dealt with currency and banking crises (e.g. Kaminsky and Reinhart 1999, Borio et al. 2002, 2004, 2009...) Alternative ways for identifying systemic risks have recently been proposed Application to predict costly aggregate asset price boom/bust cycles Real vs financial variables Global vs domestic financial variables Money vs credit Prediction of mid-2000s asset price boom wave
Literature and contributions The methodology Signalling approach: a warning signal is issued when some indicator variable exceeds some threshold, e.g. a particular percentile of its own distribution Pseudo Real Time Ranking according to policy maker loss function, not noise to signal ratio, and focus on usefulness
Country-specific boom identification Aggregate Asset Price Index Boom quarter if at least 3 consecutive quarters where QAAPR i > recursive HP trend i + 1.75 recursive stdev i QAAPR = Quarterly Aggregate Asset Price Index weighted average of commercial property, residential property and equity prices
Country-specific boom identification Recursive detrending Boom quarter if at least 3 consecutive quarters where QAAPR i > recursive HP trend i + 1.75 recursive stdev i 300 250 200 150 100 50 0 1970 1972 1974 1976 1978 1980 1982 1984 1986 Asset Prices 1988 1990 1992 1994 1996 1998 2000 2002
Country-specific boom identification Recursive detrending Boom quarter if at least 3 consecutive quarters where QAAPR i > recursive HP trend i + 1.75 recursive stdev i 300 250 200 150 100 50 0 1970 1972 1974 1976 1978 1980 1982 1984 Asset Prices 1986 1988 1990 1992 Ex-Post Trend 1994 1996 1998 2000 2002
Country-specific boom identification Recursive detrending Boom quarter if at least 3 consecutive quarters where QAAPR i > recursive HP trend i + 1.75 recursive stdev i 300 250 200 150 100 50 0 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 Asset Prices Ex-Post Trend Rec. Trend 2000 2002
Country-specific boom identification Recursive detrending Boom quarter if at least 3 consecutive quarters where QAAPR i > recursive HP trend i + 1.75 recursive stdev i 300 250 200 150 100 50 0 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 Asset Prices Ex-Post Trend Rec. Trend 2000 2002
Country-specific boom identification Recursive detrending Boom quarter if at least 3 consecutive quarters where QAAPR i > recursive HP trend i + 1.75 recursive stdev i 300 250 200 150 100 50 0 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 Asset Prices Ex-Post Trend Rec. Trend 2000 2002
Country-specific boom identification Identified booms Boom quarter if at least 3 consecutive quarters where QAAPR i > recursive HP trend i + 1.75 recursive stdev i 60 booms identified for 18 countries from 1970 robust classification
Country-specific boom identification High cost vs Low cost booms High Cost Booms if real GDP growth 1 pp p.a. lower than potential growth on average over 3 post boom years 45 classifiable booms: 29 are HC 16 are LC (control group) costly banking crises (FI 91-94, IT 90-95, SE 91-94) follow HC booms
Boom/bust cycles 3 waves: mid-late 80s, 90s, 00s 14 12 10 8 6 4 2 Number of Countries with Aggregate Asset Price Booms 0 High Cost Booms 1970 Q1 1971 Q1 1972 Q1 1973 Q1 1974 Q1 1975 Q1 1976 Q1 1977 Q1 1978 Q1 1979 Q1 1980 Q1 1981 Q1 1982 Q1 1983 Q1 1984 Q1 1985 Q1 1986 Q1 1987 Q1 1988 Q1 1989 Q1 1990 Q1 1991 Q1 1992 Q1 1993 Q1 1994 Q1 1995 Q1 1996 Q1 1997 Q1 1998 Q1 1999 Q1 2000 Q1 2001 Q1 2002 Q1 2003 Q1 2004 Q1 2005 Q1 2006 Q1 2007 Q1 Low Cost or Unclassified Booms
18 variables for 18 countries Real: GDP, private consumption, total investment, housing investment, consumer prices Financial: equity, private housing, aggregate asset prices (including also commercial housing), long rates, short rates, term spread, M1, M3, private credit, domestic credit, real effective exchange rates, global M1, global M3, global private credit, global domestic credit, global short rates Sample is 1970:Q1 to 2007:Q4 Countries are AU, BE, CA, CH, DE, DK, ES, FI, FR, GB, IE, IT, JP, NL, NO, NZ, SE, US
Indicators 89 indicators (Up to) 6 transformations per variable: ratios to GDP annual growth rates 6 quarters cumulated growth rates recursively HP detrended (constant and variable history) ratios to GDP recursively HP detrended (constant and variable history) cumulated shocks from recursive VAR in growth rates (for money and credit variables)
Global private credit gap and optimal threshold period 1979-2002 1.5 Housing/ Savings and Loans dot.com Credit 1 0.5 0-0.5-1 -1.5 1979Q1 1983Q1 1987Q1 1991Q1 1995Q1 1999Q1 2003Q1 2007Q1
Signalling approach Policy Maker s Loss Function Costly Boom No Costly Boom Signal A B No signal C D L = θ C A + C }{{} booms not called +(1 θ) B } B {{ + D } false alarms θ= policy maker s relative aversion to missing a call versus receiving a false alarm Usefulness = min[θ; 1 θ] L
The trade-off between missed crises and false alarms Optimal thresholds for the Global M1 Gap Frequency 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.95 0.90 0.85 0.40 0.10 0.2 0.1 0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Type I errors (missing crises) Type II errors (false alarms)
Optimal threshold Time-varying threshold Given θ, we optimize threshold quantile (by means of grid search in [0.05; 0.95], step 0.05) for each indicator with respect to loss function (for same θ) Three levels of aggregation 18 individual countries average over all countries GDP weighted average over 8 EA countries
Joint indicators A signal is issued when both indicators breach their respective thresholds Global PC and Global M1 plus 16 best individual indicators in 2 dimensional grid search reduce false alarms
(Pseudo) Real Time availability at each point in time is (roughly) taken into account by means of using appropriately lagged variables (usually -1 quarter, often more for housing and aggregate asset prices, up to -4). We do not use a true real time dataset (with original vintages of data) The distribution of the indicators and thus the actual threshold for issuing a signal changes at each point in time. What is not real time is the optimal quantile, which is derived using all booms for which data availability allowed a classification as high or low cost
(Pseudo) Real Time availability at each point in time is (roughly) taken into account by means of using appropriately lagged variables (usually -1 quarter, often more for housing and aggregate asset prices, up to -4). We do not use a true real time dataset (with original vintages of data) The distribution of the indicators and thus the actual threshold for issuing a signal changes at each point in time. What is not real time is the optimal quantile, which is derived using all booms for which data availability allowed a classification as high or low cost
Best 3 indicators Average over all countries θ = 0.2 θ = 0.3 usefulness good false usefulness good false calls alarms calls alarms GlobM1-detr 0.03 0.38 0.06 GlobM1-detr 0.07 0.38 0.06 Shock-GlobalM1 0.01 0.09 0.01 GlobPC-detr 0.06 0.55 0.15 GlobM1-HP 0.01 0.11 0.02 QEPR-detr 0.04 0.47 0.14 θ = 0.4 θ = 0.5 usefulness good false usefulness good false calls alarms calls alarms GlobPC-HP 0.14 0.82 0.32 GlobPC-HP 0.25 0.82 0.32 GlobPC-detr 0.13 0.55 0.15 INV-cum 0.22 0.85 0.42 GlobM1-detr 0.12 0.48 0.12 QAAPR-yoy 0.21 0.9 0.47 θ = 0.6 θ = 0.8 usefulness good false usefulness good false calls alarms calls alarms GlobPC-HP 0.17 0.88 0.41 GDPR-HP 0.07 0.99 0.63 GDPR-HP 0.16 0.94 0.51 QAAPR-cum 0.06 0.99 0.64 QAAPR-cum 0.15 0.95 0.54 GlobSR-HP 0.06 0.98 0.63
Relative usefulness of money and credit Average results over all countries Usefulness (%) 25 20 15 10 5 0 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Global Credit/GDP Global M1/GDP θ
Joint indicators Weighted-average over EA countries usefulness % booms good false ants cond. diff. alt called calls alarms prob. prob. GlobPC-detr 0.19 0.63 0.60 0.09 0.14 0.57 0.42 5.4 LRR-HP GlobPC-detr 0.18 0.63 0.58 0.09 0.15 0.59 0.44 5.4 QAAPR-HP GlobPC-detr 0.18 0.63 0.57 0.08 0.13 0.63 0.47 5.2 QRPR-HP GlobPC-detr 0.17 0.63 0.63 0.14 0.23 0.51 0.35 5.5 θ = 0.4
Prediction of the best indicators GlobPC-HP GlobM1-detr Optimal Number of Optimal Number of threshold signals threshold signals 70 (all) 7 90 (all) 0 85 (EA) 3 95 (EA) 0 If last wave included in the evaluation, GlobPC best indicator Signals should be only one of the inputs for decision makers
Receiving early warning signals for costly asset price booms in real time is possible (ants in the 0.20s feasible). If central bankers preferences are relatively balanced, monitoring credit and money provides value added (weighted sum of errors reduced 14-25 p.p. for best indicators). Aggregate asset price cycles are correlated across countries (standard) global financial variables are useful for leaning against the wind type of policies.
Outlook Focus on the EU and on equity and housing markets separately for the ESRB Investigate possible differences between market vs bank oriented countries Improve real time concept Test (other) balance sheet items of (other) financial intermediaries Extract financial factors using dynamic factor models Use model selection algorithms Use regime switching models
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