Structured Finance and Credit Risk in European Banks

Structured finance and credit risk of European banks Chang Liu CSM, Cardiff Metropolitan University Email: cliu@cardiffmet.ac.uk

Abstract This study investigates the impact of structured finance (SF) on risk management for banks. Regression analysis will be applied to European banks which have been employed structured finance from 2001 to 2020. The objective of the study is to find out whether structure finance is associated with any change in the quality of loan portfolios and the level of credit risk of banks. It should lead to the discussion of the relationship between structured finance and banking stability during financial crisis.

Introduction Structured finance (SF) could be defined as the activities of pooling financial assets, followed by subsequent issuance of a prioritized capital structure of claims against these collateral pools (Coval, J, J. Jurek, and E. Stafford, 2009). SF transactions are collateralised by a broad spectrum of financial assets: Consumer assets (credit card receivables, auto loans, and corporate loans) Mortgage loans (residential and commercial properties), Securities The development if SF - Global SF 2019 securitization energized with $1trillion in volume (S&P Global) Table 1: Global structured finance volumes (www.spglobal.com)

Introduction For banks, SF promotes liquidity and transforms risky assets into securities that were widely considered to be safe: Remove illiquid assets (loans and receivables) from balance sheet; Reduce the requirement of capital; Credit rating agencies rate the securities high; Substitute risks that are largely diversifiable for risks that are highly systematic. After 2007 sub-prime crisis, it has been recognised that: Securities produced by SF activities are far riskier than originally advertised; They have far less chance of surviving a severe economic downturn than traditional corporate securities of equal rating; But the growth of global SF market has not yet been eliminated by the findings why?

Introduction The Covid-19 pandemic has induced a deep global economic crisis. The challenges for banks: large-scale insolvencies among firms may arise; a wave of bankruptcies among households may follow; low levels of interest rates and higher levels of capital; tighter regulation; increasing competition from shadow banks and new digital entrants. The stability of banking industry must be emphasized to cope with the challenges: What can we learn from 2007? Implications of banking stability during current climate.

Introduction Research questions: 1) What is the impact of SF on banks credit risk? 2) What is the impact of SF on banking stability? 3) What are the key factors driving the impact of SF? Contributions: 1) Provide new evidence for existing literature regarding the impact of SF on credit risk of banks. 2) In term of methodology, we extend the asset classes to ABS, CMBS, RMBS, and structured credit to find out if the impact of SF is heterogeneous. The yield curve is included as macro variables to indicate the expectation of economic growth. 3) We focus on the changes of European banks adopting SF during 2000 to 2020 to reflect the influences of financial crisis, including 2007 sub-prime, 2010 European debt crisis and what 2020 Covid-19 leads to. This should generate some implications for risk management and regulation for banks.

Literature Review Existing literature suggests that banks use structured finance as a risk transfer tool over the last decades to actively manage credit risk. However, empirical studies on the effect of structured finance on credit risk generate mixed results. Positive view (risk reduction): Gorton and Pennacchi (1995) provide evidence in supporting that securitization is adopted to separate loans from originators, therefore reduces credit risk. Jiangli and Pritsker (2008) find that American bank holding companies use MBS (Mortgage-backed securities) to reduce the risk of insolvency, which is consistent with Casu et al. (2010) that mortgage securitization improve the banking stability. Negative view (risk increase): more recent studies concentrate on the role securitization has on risk-taking and how it makes banks more aggressive in risk management. Aggarwal and Jacques (2001) and Dionne and Harchaoui (2003) find a positive association between securitization and bank risk in Canada.

Literature Review The impact of SF on banking stability is mainly driven by the change of systemic risk after banks transferring risks to other market participants using SF. The incremental systemic risk is caused by i) higher level of correlation; and ii) insufficient capital level in the economy to cover risk. A number of studies confirm that securitization harms banks financial stability, including Europe (Krahnen and Wilde, 2006; Baur and Joossens, 2006; and Michalak and Uhde 2010) and US (Salah and Fedhila, 2012). Accordingly, the choice of the observation period (e.g. pre or post 2007 financial crisis), the geographic region of the study (e.g. American, Spain), and the underlying asset (e.g. mortgage, credit loan) distinguish the test results.

= + + R Rm R R 0 1 ft f mt t Methodology Variable Dependent variables: Credit risk (CR) Bank stability (BS) Description (Risk weighted assets)/(total assets) Z score - the sum of the return on average assets (ROAA) and equity capital to total assets to standard deviation of ROAA. (Total structured finance)/ (total assets) Expected Sign on Regression Independent variables: Structured finance ratio (SF) Asset classes (AC) (+/-) (1)ABS / total assets (+/-) (2)RMBS / total assets (+/-) (3)CMBS / total assets (+/-) (4)SC (Structured credit) / total assets (+/-) Bank specific variables: Capital adequacy ratio (CA) (Tier1&2 capital) / risk weighted assets Liquidity ratio (LR) Liquid assets / total assets Profitability ratio (PR) Net income / equity capital Bank size(S) Natural logarithm of total assets Macro-economic variables: (-) (+/-) (-) (-) Yield curve (YC) Spread between 10-year and 1-year government bonds Volatility of one-year interest rate is measured as the residual of regression: mt ft R R + + = 1 0 + Interest rate risk (IR) + R =annual return R = annual return , t f on one year government bond, on market portfolio Dummy = 0: before crisis = 1: after crisis m Control variables: Financial crisis (FC) (+)

Methodology Data collection: FitchConnet Fitch classifies SF transactions into four main sectors: RMBS, CMBS, ABS and Structured Credit. Software: Stata A linear regression model derived from the one of Salah N and Fedhila H (2012) is followed: k =1 = + + + CR ASF B X C it it j it it j k =1 = + + + BS ASF B X C it it j it it j Where X is the vector of the independent variables representing bank specific and macro-economic specificities of bank i for year t. A, Bj and C are parameters to be estimated; The test will consider different asset classes.

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