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Macro economic cycle effect on mortgage and personal loan default rates

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  • Petrus Strydom

Abstract

The aim of this paper is to apply a Gaussian process to decompose the time series of crude default rates into three components: age of the loan, quality of the loan and the exogenous economic environment. This is supported by the empirical result for a mortgage and personal loans portfolio based on five years of historic data. The Gaussian process does not impose an explicit parametric structure to the relationship between the three components and the default rate compared to other methodologies that assume a linear structure. We find that the vintage and economic cycle components are more important drivers of the default rate compared to the age effect and this varies over the economic cycle. The contribution of the economic cycle component to the overall default (for both the mortgage and personal loan portfolios) range from between 20% to 50% depending the position in the economic cycle. In general the economic cycle effect is larger during economic stress.JEL Classification: C11; C23; C53; E51; G21Keywords: Credit Risk; Probability of Default; Forecasting; Gaussian process

Suggested Citation

  • Petrus Strydom, 2017. "Macro economic cycle effect on mortgage and personal loan default rates," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 7(6), pages 1-1.
    • Handle: RePEc:spt:apfiba:v:7:y:2017:i:6:f:7_6_1
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    References listed on IDEAS

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    1. Peter Guttorp & Tilmann Gneiting, 2006. "Studies in the history of probability and statistics XLIX On the Matern correlation family," Biometrika, Biometrika Trust, vol. 93(4), pages 989-995, December.
    2. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    3. Olena Havrylchyk, 2010. "A macroeconomic credit risk model for stress testing the South African banking sector," Working Papers 3579, South African Reserve Bank.
    4. Daniel Rösch & Harald Scheule, 2004. "Forecasting Retail Portfolio Credit Risk," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 5(2), pages 16-32, February.
    5. Daniel Rösch & Harald Scheule, 2004. "Forecasting Retail Portfolio Credit Risk," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 5(2), pages 16-32, February.
    6. L C Thomas & R W Oliver & D J Hand, 2005. "A survey of the issues in consumer credit modelling research," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1006-1015, September.
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    Cited by:

    1. Maria Rosa Borges & Raquel Machado, 2020. "Modelling credit risk: evidence for EMV methodology on Portuguese mortgage data," Working Papers Department of Economics 2020/03, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.

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    More about this item

    Keywords

    credit risk; probability of default; forecasting; gaussian process;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers

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