[PPT] - Sustainable Financial Obligations and Crisis Cycles Mikael Juselius PowerPoint Presentation

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Sustainable Financial Obligations and Crisis Cycles

Mikael Juselius and Moshe Kim

1985 1990 1995 2000 2005 2010 120 140 160 180 200 220

(a) U.S. household sector total debt to income.

1985 1990 1995 2000 2005 2010 −2 2 4 6 8 10

(b) Nominal (solid line) and real (dotted line) federal funds rate.

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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

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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

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– 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

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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 be- havior 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

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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

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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

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1985 1990 1995 2000 2005 2010 0.5 1.0 1.5 2.0 2.5 3.0

(a) Loss rate on total loans.

1985 1990 1995 2000 2005 2010 0.5 1.0 1.5 2.0 2.5

(b) Loss rate on real estate loans.

1985 1990 1995 2000 2005 2010 0.5 1.0 1.5 2.0 2.5

(c) Loss rate on business loans.

1985 1990 1995 2000 2005 2010 0.5 1.0 1.5 2.0 2.5 3.0

(d) Bank failure rate.

1985 1990 1995 2000 2005 2010 −5 −4 −3 −2 −1 1 2 3

(e) Output gap

1985 1990 1995 2000 2005 2010 2 4 6 8 10

(f) Nominal (solid line) and real (dotted line) long-term government T- bill rate.

1985 1990 1995 2000 2005 2010 −3.5 −3.0 −2.5 −2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0

(g) Interest rate spread

1985 1990 1995 2000 2005 2010 −2 2 4 6 8 10

(h) Nominal (solid line) and real (dotted line) federal funds rate.

Figure 1: Credit loss rates and various indicators of financial, monetary, 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.

1

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1985 1990 1995 2000 2005 2010 120 140 160 180 200 220

(a) Total leverage in the household sector.

1985 1990 1995 2000 2005 2010 70 80 90 100 110 120 130 140 150 160 170

(b) Real estate leverage in the household sector.

1985 1990 1995 2000 2005 2010 100 105 110 115 120 125 130

(c) Total leverage in the business sector.

1985 1990 1995 2000 2005 2010 4 5 6 7 8

(d) Real estate leverage in the business sector.

1985 1990 1995 2000 2005 2010 16.5 17.0 17.5 18.0 18.5

(e) Total financial obligations in the household sector.

1985 1990 1995 2000 2005 2010 9.0 9.5 10.0 10.5 11.0

(f) Real estate financial obligations in the household sector.

1985 1990 1995 2000 2005 2010 10.0 10.5 11.0 11.5 12.0

(g) Total financial obligations in the business sector.

1985 1990 1995 2000 2005 2010 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75

(h) Real estate financial obligations in the business sector.

Figure 2: Indicators of leverage and financial obligations in the household and business sectors.

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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(τt))(µ1 + γ′ 1xt) + ϕ(τt)(µ2 + γ′ 2xt) + ψ′dt + υt

(1) where ˜ cl

j t is the credit loss rate in loan category j, xt is a vector of explanatory

variables, τt is a transition variable, and dt is a vector of deterministic terms. The transition function takes the form ϕ(τt) = 1/(1 + e−κ1(τt−κ2)).

The transition variable, τt, is selected from a set which includes the leverage

variables, lij

t , the financial obligations ratios, f ij t , and several control variables.

xt 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

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t t n

cl = − 0.1 * (y − y ) cl = − 0.5 * (y − y )

t t t n n t t t

cl = − 0.3 * (y − y )

2

κ

1

κ

t

τ

t

Figure 1: A graphical example of the regime switching STR-model. 1

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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 τt: the κ2 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

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STR estimates Transition parameters Regime 1 Regime 2 ˜ cli

t

τt κ1 κ2 γ˜

iS

γ˜

y

γ˜

iS

γ˜

y

˜ clT

t

fHR

t

12.678

(5.630)

10.192

(0.056)

−0.063

(0.034)

0.002

(0.045)

−0.276

(0.094)

−0.224

(0.051)

˜ clR

t

fHR

t

3.609

(1.128)

10.079

(0.106)

−0.023

(0.041)

−0.051

(0.038)

−0.267

(0.099)

−0.243

(0.049)

˜ clB

t

fBT

t

2.318

(0.968)

10.44

(0.199)

−0.249

(0.085)

– −0.619

(0.119)

–

Table 1: Estimated transition parameters and regime coefficients from STR-models of the credit loss rates. Recall the STR-model: ˜ clt = (1 − ϕ(τt))(µ1 + γ′

1xt) + ϕ(τt)(µ2 + γ′ 2xt) + ψ′dt + υt

ϕ(τt) = 1 1 + e−κ1(τt−κ2).

Juselius and Kim - ’Sustainable Financial Obligations and Crisis Cycles’, 3-4 November, Amsterdam 9

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1985 1990 1995 2000 2005 2010 1 2 3 1985 1990 1995 2000 2005 2010 9 10 11

MSBD

Regime 1

Loss rate on real estate loans Financial obligations ratio,

Regime 2

household’s real estate debt

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

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1985 1990 1995 2000 2005 2010 1 2 1985 1990 1995 2000 2005 2010 10 11 12

Loss rate on business loans

Regime 1 Regime 2

MSBD business sector debt Financial obligations ratio,

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

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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