Peer-to-Peer Lending: a Growth-Collapse Model and its Steady-State Analysis

Onno Boxma (Corresponding author), David Perry, Wolfgang Stadje

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Samenvatting

We present a stochastic growth-collapse model for the capital process of a peer-to-peer lending platform. New lenders arrive according to a compound Poisson-type process with a state-dependent intensity function; the growth of the lending capital is from time to time interrupted by partial collapses whose arrival intensities and sizes are also state-dependent. In our model the capital level administered via the platform is the crucial quantity for the generated profit, because the brokerage fee is a fixed (small) fraction of it. Therefore we study its steady-state probability distribution as a key performance measure. In the case of exponentially distributed upward jumps we derive an explicit expression for its probability density, for quite general arrival rates of upward and downward jumps and for certain collapse mechanisms. In the case of generally distributed upward jumps, we derive an explicit expression for the Laplace transform of the steady-state cash level density in various special cases. An alternative model featuring up and down periods and a shot noise mechanism for the downward evolution is also analyzed in steady state.

Originele taal-2Engels
Pagina's (van-tot)233-258
Aantal pagina's26
TijdschriftMathematical Methods of Operations Research
Volume96
Nummer van het tijdschrift2
Vroegere onlinedatum4 jul. 2022
DOI's
StatusGepubliceerd - okt. 2022

Bibliografische nota

Funding Information:
The research of Onno Boxma is supported by the NWO Gravitation Programme NETWORKS (Grant number 024.002.003). The research of David Perry is partly supported by a grant of the Israel Science Foundation (Grant number 3274/19).

Publisher Copyright:
© 2022, The Author(s).

Financiering

The research of Onno Boxma is supported by the NWO Gravitation Programme NETWORKS (Grant number 024.002.003). The research of David Perry is partly supported by a grant of the Israel Science Foundation (Grant number 3274/19).

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