Sustainable Financial Obligations and Crisis Cycles

Transcription

1 Sustainable Financial Obligations and Crisis Cycles Mikael Juselius and Moshe Kim (a) U.S. household sector total debt to income (b) Nominal (solid line) and real (dotted line) federal funds rate.

2 Outline 1. Background 2. Objectives and key findings 3. Data 4. Methodology 5. Results 6. Implications Juselius and Kim - Sustainable Financial Obligations and Crisis Cycles, 3-4 November, Amsterdam 1

3 Background Can aggregate private sector debt reach excessive levels? Lorenzoni (2008) and Miller and Stiglitz (2010): debt can reach (unsustainable) inefficient levels under dispersed beliefs or limited commitment in financial contracts. King (1994) and Mian and Sufi (2010) provide cross-sectional evidence from individual episodes of financial distress suggesting close association between high aggregate leverage (debt-to-income) and subsequent credit and output losses. They overlook the persistent upward trend that has been present in US debt to income ratios for the past 25 year, which may have been due to a concurrent decline in the real interest rate. Juselius and Kim - Sustainable Financial Obligations and Crisis Cycles, 3-4 November, Amsterdam 2

4 Such trends tend to have a uniform effect on the cross-section and would, hence, not generate much cross-sectional variation. For these reasons the strong association between leverage and losses reported in cross-sectional studies, may be much weaker when viewed in a time-series context. Borio and Lowe (2002) address the problem associated with growth trends by using leverage and asset price gaps, which are based on the Hodrick-Prescott filter. Since this procedure is not based on economic rationale, such gaps may still confuse sustainable developments in the variables with excessive buildups. Juselius and Kim - Sustainable Financial Obligations and Crisis Cycles, 3-4 November, Amsterdam 3

5 Objectives and key findings We model aggregate U.S. credit loss dynamics over the period 1985Q1-2010Q2, to assess the role of aggregate debt in generating both real and financial distress. We allow leverage to enter credit loss determination both linearly, in line with the literature on financial accelerators, as well as non-linearly, to capture altered behavior and contagion effects during episodes in which aggregate credit constraints become binding (e.g., Campello et al. (2010)). Key findings: Debt to income ratios (leverage) do not perform well as measures of excessive debt accumulations. We find no significant temporal relationship, linear or otherwise, between aggregate leverage and credit losses. Juselius and Kim - Sustainable Financial Obligations and Crisis Cycles, 3-4 November, Amsterdam 4

6 An alternative measure, the financial obligations ratio (interest payments and amortizations), produce good results. It acts as a transition variable which intensifies the interaction between credit losses and the business cycle, once a critical threshold is exceeded. This occurs in either the household or the business sector 1-2 years prior to each economic downturn in the sample. Together, these ratios likely play a significant role in shaping business cycle movements. Moreover, the magnitude of excessive debt in each sector seems to account for the severity and length of ensuing recessions. Juselius and Kim - Sustainable Financial Obligations and Crisis Cycles, 3-4 November, Amsterdam 5

7 Data We use net charge-off rates to capture credit losses. We distinguish between losses on total loans, real estate loans, and business loans. See Figure 1. We use debt to income ratios as measures of leverage. We distinguish between the household and business sectors, and between total and real estate debt. We use the financial obligations ratio, as constructed by the Federal Reserve, to capture interest payments and amortizations. See Figure 2. This measure is not available for the business sector: we construct it using the federal funds rates, a fixed maturity of 3 years, and linear amortizations. We control for several factors, such as interest rates and monetary policy. Juselius and Kim - Sustainable Financial Obligations and Crisis Cycles, 3-4 November, Amsterdam 6

8 (a) Loss rate on total loans. (b) Loss rate on real estate loans (c) Loss rate on business loans. (d) Bank failure rate (e) Output gap (f) Nominal (solid line) and real (dotted line) long-term government T- bill rate (g) Interest rate spread (h) Nominal (solid line) and real (dotted line) federal funds rate. Figure 1: Credit loss rates and various indicators of financial, monetary, 1 and real conditions in the United Sates. The real (ex-post) interest rates are constructed using the 4-quarter moving average inflation rate to facilitate the exposition.

9 (a) Total leverage in the household sector. (b) Real estate leverage in the household sector (c) Total leverage in the business sector. (d) Real estate leverage in the business sector (e) Total financial obligations in the household sector. (f) Real estate financial obligations in the household sector (g) Total financial obligations in the business sector. (h) Real estate financial obligations in the business sector. Figure 2: Indicators of leverage and financial obligations in the household and business sectors.

10 Methodology We allow the aggregate debt variables to enter credit loss determination in two ways: linearly, using a CIVAR, or nonlinearly, using a STR-model of the form: cl j t = (1 φ(τ t ))(µ 1 + γ 1x t ) + φ(τ t )(µ 2 + γ 2x t ) + ψ d t + υ t (1) where cl j t is the credit loss rate in loan category j, x t is a vector of explanatory variables, τ t is a transition variable, and d t is a vector of deterministic terms. The transition function takes the form φ(τ t ) = 1/(1 + e κ 1(τ t κ 2 ) ). The transition variable, τ t, is selected from a set which includes the leverage variables, l ij t, the financial obligations ratios, f ij t, and several control variables. x t consists of cyclical indicators, e.g., the output gap and the term spread. Juselius and Kim - Sustainable Financial Obligations and Crisis Cycles, 3-4 November, Amsterdam 7

11 n cl = 0.5 * (y y ) t t t n t cl = 0.3 * (y y ) t t t n t cl = 0.1 * (y y ) t τ t κ 2 κ 1 Figure 1: A graphical example of the regime switching STR-model. 1

12 Results 1. None of the debt variables are able to linearly account for credit losses and there are significant non-linearities in the data. 2. When leverage is used as τ t : the κ 2 estimate typically lie outside the variable s range, the statistical fit is poor, and unit-roots cannot be rejected in the residuals. 3. When the financial obligations ratios are used, these problems do not occur. Juselius and Kim - Sustainable Financial Obligations and Crisis Cycles, 3-4 November, Amsterdam 8

13 STR estimates Transition parameters Regime 1 Regime 2 cl i t τ t κ 1 κ 2 γĩs γỹ γĩs γỹ cl T t f HR t (5.630) (0.056) (0.034) (0.045) (0.094) (0.051) cl R t f HR t (1.128) (0.106) (0.041) (0.038) (0.099) (0.049) cl B t f BT t (0.968) (0.199) (0.085) (0.119) Table 1: Estimated transition parameters and regime coefficients from STR-models of the credit loss rates. Recall the STR-model: cl t = (1 φ(τ t ))(µ 1 + γ 1x t ) + φ(τ t )(µ 2 + γ 2x t ) + ψ d t + υ t φ(τ t ) = e κ 1(τ t κ 2 ). Juselius and Kim - Sustainable Financial Obligations and Crisis Cycles, 3-4 November, Amsterdam 9

14 3 2 Regime 1 Regime 2 Loss rate on real estate loans Financial obligations ratio, household s real estate debt 10 MSBD 9 Figure 1: Transitions in the loss rate on real estate loans. The upper panel depicts the loss rate on real estate loans, whereas the lower panel depicts the financial obligations ratio associated with household s real estate debt and the corresponding MSDB estimate. Episodes when regime 2 dominate are demarked by grey bars. 1

15 Loss rate on business loans 2 Regime 1 Regime Financial obligations ratio, business sector debt 11 MSBD 10 Figure 2: Transitions in the loss rate on business loans. The upper panel depicts the loss rate on business loans, whereas the lower panel depicts the financial obligations ratio associated with total business sector debt and the corresponding MSDB estimate. Episodes when regime 2 dominate are demarked by grey bars. 2

16 Implications Bank capital requirements: Our results suggests that assessments of aggregate credit risk which are based on the financial obligations ratio is likely to achieve more counter-cyclical capital standards. More important, because we model credit losses directly, our approach may provide a convenient way of mapping such assessments into actual capital requirements. Macro prudential policies: The financial obligations ratios, in particular those related real estate debt, may be useful as early warning indicators. Monetary policy: Interest rate increases, intended to curb inflationary pressure, can be detrimental to financial stability in periods when aggregate debt is close to or above the sustainable level. Juselius and Kim - Sustainable Financial Obligations and Crisis Cycles, 3-4 November, Amsterdam 10