Denis Nadolnyak (Auburn, U.S.)

Valentina Hartarska (Auburn University, U.S.)

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Financial markets and catastrophic risks

Emerging literature studies how financial markets are affected by catastrophic risk events including those caused by climate extremes

Basic conclusion: In the absence of adequate insurance markets , commercial banks and their clients are affected by catastrophic events (Garmaise and Moskowitz , 2009)

Empirical evidence In developing countries climate extremes affect agricultural loan

portfolios (Collier, Katchova, and Skees, 2010; Bergyz and Schraderx2009)

Tornadoes and hurricanes impact on US community banks is localized and small (Ewing, Hein and Kruse, 2005)

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Motivation

Agriculture is climate dependent, especially in US Southeast (less irrigation, more floods & droughts)

Credit risk/loan performance in agricultural lending may be related to climate variations/extremes

Farmers get production loans mainly from

Farm Credit System (FCS) – $161 bln in 2008 - serves more (professional) farmers in the Southeast

Commercial Banks – $121 bln in 2008

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HypothesisAgricultural loan portfolios of banks serving agricultural

producers in the Southeastern U.S. are affected by inter-annual climate fluctuations (ENSO events measured by MEI)

Impacts are expected through changes in repayment patterns

delinquencies (loans past due 30-90 days)

default with realized losses (charge offs)

in the future – restructuring of loans?

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Possible implicationsResults could be suggestive of

Usefulness of ENSO forecasts to both farmers and banks in decision making

The degree to which existing financial system and government support mechanisms (including insurance markets) and able to diversify climate related risks

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ENSO has impacts on local climate in different world regions. Impacts agriculture.

Source: NOAA7

ENSO Impact in the Southeast

• Historical data and models suggest some dependency yields (agroclimate.org; Royce et al., 2011; Martinez, 2009, 2010; Baigorria et al., 2010).

Aggregate impacts of ENSO on ag. production risk:

ENSO events found positively associated with agricultural disaster payments in county level analysis (Nadolnyak and Hartarska, 2009). La Niña years associated with more disaster payments.

Downward volatility of corn, cotton, and peanut yields in the southeast has been consistently higher in La Niña: crop insurance implications (Nadolnyak and Vedenov, 2008).

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CHOOSING INDEX INTERVAL

Most recent aggregate data analysis scores Multivariate ENSO Index MEI as best predictor for corn, cotton, peanut yields (Royce, Fraisse, Baigorria, 2011)

Takes 1-2 weeks for the global atmosphere to respond to tropical SST anomalies (NOAA)

Impact Interval must correspond to regional growing season

To match available banking data, mean MEI values over March-June are used as continuous measure of ENSO signal during growing season in Southeast US

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Banking Data

Call Reports: quarterly commercial banks data for 1991-2010 in AL, FL, GA, LA, MS, and SC

Agricultural Bank definition:

FDIC: ag. loans / total loans > 25%

Federal Reserve System (Fed): ag. loans / total loans > all banks’ average. Better for increasing portfolio diversification

Sample: ag. loans / total loans = 5.8% .

Agricultural banks by FED: ag./total loans = 16.3%

473 banks in 5 states, ~1,000 annual obs.

Av. annual lending of $198mil, ag. loans about $10mil

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Banking Data

Agricultural loans of two types:

Regular crop production loans

Production loans secured by farm real estate

Portfolio performance by delinquency & default of Ag Production loans (also loans secured by real estate):

Ag. loans overdue 30-90 days (since 2001)

Ag. loans overdue >90 days (since 1991)

Nonaccrual ag. production loans (since 1991)

Ag. production loans charge-offs (since 1984)

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Summary StatisticsVariable Obs Mean Std. Dev. Min Max

Non performing loans backed by

real estate ('000 $)

2,996 130.5 534.3 0 9,408.0

RE Loans Delinquent >30 days 2,926 108.3 328.6 0 9,627.6

RE Loans Delinquent >90 days 2,996 28.1 169.7 0 5,319.0

Real Estate charge off ('000%) 2,996 25.3 149.5 0 3,150.6

MEI Feb-August 2,996 0.448 0.677 -0.713 1.609

Mei squared 2,996 0.659 0.852 0.000 2.587

JMA La Nina 2,996 0.105 0.350 0 1

JMA Neutral 2,996 0.600 0.487 0 1

JMA El Nino 2,996 0.294 0.430 0 1

ONI Medium El Nino 2,996 0.102 0.303 0 1

ONI Medium La Nina 2,996 0.105 0.350 0 1

ONI Neutral 2,996 0.400 0.492 0 1

ONI strong EL Nino 2,996 0.156 0.363 0 1

ONI Weak El Nino 2,996 0.191 0.282 0 1

ONI Weak La Nina 2,996 0.105 0.300 0 1

Bank Size (mln assets) 2,996 198.9 255.4 6.5 2971.1

Land value ($ per acre) 2,996 2,238.4 947.5 1,171.6 5,783.9

RE loans by FCS (billions) 2,996 40.1 7.8 32.2 59.4

RE Loans by banks ( billions) 2,996 35.3 7.8 25.1 51.2

Number of farms (millions) 2,996 2.2 0.0 2.1 2.2

Value of real estate (millions) 2,996 1,275.6 288.1 995.7 1,841.8

Debt/equity ratio 2,996 15.8 2.2 12 19

Value of crops (billion $) 2,996 33.9 5.8 23.9 44.4

Dummy 2008 -2009 crisis 2,996 0.09 0.28 0 1

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Model Reasons for default:

Non-strategic:

Idiosyncratic shock events (divorce, illness) not captured by aggregate banking data. Canceled by diversification

Systemic shock events (due to weather) are reflected in aggregate commercial bank data

Strategic: when value (land) < loan.

To control for strategic default , use proxy for the strategic default option (change in land value). Studies show strategic defaults significant (Briggeman et al., 2009)

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Model Estimated equation:

Dijt = β0 + β1.ENSOt-1 + β2

.LandValueijt + β3.LandValueijt-1

+ β3.DummyFinCrisist-1 + γ’controlsijt-1 + eijt

Dijt measures delinquency / default on ag. loans backed by farmland for bank i in state j in year t.

Main variable is loans in default and nonperforming (nonaccrual) loans - that is loans borrowers cannot repay

Also loans delinquent for 30-90 days

Bank losses not relevant for collateralized loans (farmland)

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Model ENSO measures (year preceding default/delinquency):

MEI averaged over late winter – early summer

JMA and ONI (classification borrowed from Royce, Fraisse,

Baigorria, 2011)

Control variables:

Bank size (scale efficiency)

Shares of FCS and commercial banks in lending

Crop value

Debt-to-Equity ratio of farm operators (demand for loans)

Production subsidies, including disaster payments

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Results Fixed effects regression of nonaccrual loans backed by farm

land 1 2 3 4

JMA La Nina -83.69***

(27.43)

JMA El Nino -94.44*

(53.06)

ONI Medium La Nina -347.4***

(88.36)

ONI Medium El Nino -181.1*

(96.05)

ONI Strong El Nino -326.1**

(143.9)

ONI Weak El Nino 39.64

(34.26)

ONI Weak La Nina -258.5***

(68.47)

MEI (growing season avg) 295.9***

(77.42)

MEI 2 (growing season avg) -303.7***

(82.65)

Disaster Payments -0.564**

(0.240)

Bank Size 1.089*** 1.099*** 1.110*** 1.101***

(0.250) (0.243) (0.241) (0.253)

Land Value -0.186*** -0.231*** -0.176*** -0.148***

(0.0464) (0.0536) (0.0451) (0.0422)

Lagged Land Value 0.234*** 0.286*** 0.224*** 0.211***

(0.0535) (0.0620) (0.0526) (0.0505)

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Results Fixed effects regression, nonaccrual loans

Lag RE Loans by FCS 26.69 -51.49* 59.52*** -9.694

(18.48) (28.63) (18.08) (15.22)

Lag RE Loans by banks -51.82 -6.509 -88.32*** 2.871

(35.38) (34.07) (27.97) (18.75)

Lag Number of Farms 1456 -701.7 2172 -10200**

(2442) (2280) (2449) (3976)

Lag Value of Real Estate 0.640 0.514 1.686*** -0.634

(0.521) (0.341) (0.579) (0.406)

Lag Debt/Equity Ratio 33.05 86.85*** 108.8*** 29.93*

(27.65) (29.50) (33.01) (16.33)

Lag Value of Crops 21.68*** -5.989 43.25*** 41.42***

(7.857) (11.87) (10.76) (10.36)

Dummy Financial Crisis -14.87 995.1*** -554.9* 1181***

(271.0) (323.3) (324.3) (452.9)

Mississippi (Alabama base) 231.7*** 218.0*** 222.2*** 213.7***

(27.83) (25.91) (26.37) (26.72)

States Controls yes yes yes yes

Constant -4415 2024 -9127 20986**

(5499) (4884) (5896) (8691)

Observations 2968 2968 2968 2944

R-squared 0.138 0.143 0.137 0.141

Number of banks 473 473 473 470

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 17

Results Overall, results are contrary to expectation that non-

neutral years (more climate extremes) have higher defaults:

Fewer defaults in both Warm and Cold Phases

By JMA, $84,000/bank less in La Nina and $94,440/bank less in El Nino

By ONI (weak, medium, strong): lowest defaults in Cold phase, then in Warm phase, highest in Neutral!

By MEI (continuous, averaged Feb-Jun), significant inverted parabolic relationship:

-1 -0.5 0 0.5 1 1.5 2-9800

-9700

-9600

-9500

-9400

-9300

-9200

-9100

-9000

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Results

Similar results found for loans overdue >30 days and > 90 days

Bank losses (charge offs associated with agricultural loans backed by farmland) are lower by about $100,000 following a strong El Nino growing season for the average banks

Due to ENSO persistence (memory), indexes from Fall preceding the year when loans are taken can be used to predict default rates

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Results Possible explanation: government support mechanisms

overcompensate + financial markets pool the risks Lower default rates associated with higher disaster payment

AL disaster payments: $0.25mil in Neutral , $51mil in La Nina; $30,000/bank less in default loans

Correlation b/w state-level ag. disaster payments and MEI(Feb-Jun)

Correlation b/w crop insurance subsidies and MEI(Feb-Jun) very similar, less for counter-cyclical & deficiency payments.

AL FL GA LA MS SC

CORR -0.4874 -0.5188 -0.5004 -0.4729 -0.3942 -0.5081

PVAL 0.0654 0.0693 0.0575 0.0751 0.146 0.0531

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Results Farmers-clients of agricultural banks are affected by weather

variation but government supports reverse the impacts:

Overcompensation mechanism explanation consistent with Ewing, Hein and Kruse, (2005) who found that community banks serving areas affected by natural disaster are better off in the aftermath of a catastrophic event, likely due to well functioning complementary insurance markets.

Controlling for strategic default is important:

Land prices in the period prior to default when collateral was valued are associated with larger defaults

Current land prices are associated with fewer default loans => land prices affect farmers’ strategic default decisions

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Results Farmers’ leverage is associated with higher defaults

Total value of crops production is associated with higher defaults. ? higher level of output => lower prices => harder to repay for low income farmers?

Larger farm numbers associated with more defaults in agricultural banks’ portfolios

Higher value of farmland is associated with more loans in default perhaps due use of more marginal land in agricultural production.

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Conclusion Question Posed: Are agricultural loan portfolios affected by

inter-annual climate fluctuations?

Result: Non-neutral years have fewer loans in defaults contrary to expectation that banks would suffer from weather extremes

Explanation: Financial markets, government supports (disaster relief, countercyclical/deficiency, and crop insurance) mitigate climate related risks associated with agricultural lending in the Southeast U.S

Emerging literature on non-agricultural bank lending and catastrophic risk events reports similar results & explanations

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Suggestions and questions?

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