ABSTRACT
Asset-based lending companies and other loan providers are exposed to risk of loan
defaults by borrowers. To reduce this risk, these companies acquire credit insurance. Thus
when the borrower defaults in payment, the insurance company covers a percentage of the
outstanding balance which generates a way to lessen and spread credit risk that the lender
incurs. Therefore there are a number of methods put in place such as frequency-serverity
and hazard rate models used to value credit insurance. Valuing of credit insurance for
asset-based lending companies is a challenging task especially in Kenyan market, where
in the case of a borrower’s default, the process for recovering of the collateral will last a
longer period of more than a year and where data on the borrower’s behaviour of payment
is of poor quality or generally unavailable. The existing methods do not consider the
time to repossession of the collateral in case of loan default. Our proposed model takes
into account time to repossession of the collateral and can be used in emerging market
economies where other available methods may be either unsuitable or are too complex
to implement due to lack of enough data. Therefore, this project aims to incorporate
the discrete and continuous time models to forecast loss reserves in credit insurance for
asset-based lending companies. First, we established a discrete-time model to describe
delinquency of credits in loan insurance product. Martingale properties, Replicating of
asset portfolio strategy and Ito’s calculus are used to obtain results on expected values
of future losses of credit insurance products. Secondly, we used the Black-Scholes model
to develop a continuous-time model to forecast future losses in credit insurances. This is
constructed by linking it from the discrete-time model using the methods of stochastic
calculus. We estimated the loss reserves by first applying the Geometric Brownian Motion
simulation to predict the probability of default of the borrower. The probability of default
was then multiplied by the simulated outstanding balances, a factor that considers the
time to repossession of the collateral and the assumed percentage coverage of the insurance
company to obtain estimates of loss reserves in credit insurance.
OCHODI, E (2021). Application Of Discrete And Continous Time Models In Valuation Of Credit Insurance For Asset-Based Lending Companies. Afribary. Retrieved from https://afribary.com/works/application-of-discrete-and-continous-time-models-in-valuation-of-credit-insurance-for-asset-based-lending-companies
OCHODI, ETYANG "Application Of Discrete And Continous Time Models In Valuation Of Credit Insurance For Asset-Based Lending Companies" Afribary. Afribary, 07 May. 2021, https://afribary.com/works/application-of-discrete-and-continous-time-models-in-valuation-of-credit-insurance-for-asset-based-lending-companies. Accessed 22 Nov. 2024.
OCHODI, ETYANG . "Application Of Discrete And Continous Time Models In Valuation Of Credit Insurance For Asset-Based Lending Companies". Afribary, Afribary, 07 May. 2021. Web. 22 Nov. 2024. < https://afribary.com/works/application-of-discrete-and-continous-time-models-in-valuation-of-credit-insurance-for-asset-based-lending-companies >.
OCHODI, ETYANG . "Application Of Discrete And Continous Time Models In Valuation Of Credit Insurance For Asset-Based Lending Companies" Afribary (2021). Accessed November 22, 2024. https://afribary.com/works/application-of-discrete-and-continous-time-models-in-valuation-of-credit-insurance-for-asset-based-lending-companies