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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01s1784p169
Title: Forecasting Default Rates of Peer-to-peer Lending Using Dynamic Regression Models
Authors: Chen, Xin
Advisors: Cheridito, Patrick
Department: Operations Research and Financial Engineering
Class Year: 2016
Abstract: The recent decade witnesses the exponential growth of online peer-to-peer lending, but default dependence between individual peer-to-peer loans has rarely been studied. This paper examines monthly default rates of loans issued by Lending Club from June 2007 to December 2015. We find significant correlations between monthly default rates of different grades of loans and significant relationships between default rates and concurrent macroeconomic variables. We perform linear regression of monthly default rate against unemployment rate and inflation rate, and use an ARMA model to describe the behavior of residuals. Unemployment rate and inflation rate are modeled as seasonal ARIMA processes. We show that our model produces more accurate and consistent forecast of default rate than other models.
Extent: 61 pages
URI: http://arks.princeton.edu/ark:/88435/dsp01s1784p169
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Operations Research and Financial Engineering, 2000-2016

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