Pricing and hedging aggregated micro-loan cashflows

小额贷款现金流汇总的定价和对冲

• 批准号: 2601287
• 金额: --
• 依托单位: Brunel University London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2601287
• 关键词: Pricing; hedging; aggregated; micro; loan

Microfinance institutions (MFIs) play a critical role in providing access to capital to a large proportion of adult population in developing countries as well as in the developed world. According to a world bank report in 2017, a third of the world's adult population has no bank accounts and therefore rely on MFIs or lenders in informal economy for credit. Given the important role that MFIs continue to play in the wellbeing of society, it is imperative that they have appropriately tailored mathematical tools at their disposal for quantifying and mitigating financial risk posed by the business of lending funds to individuals with no credit history and no collateral. The purpose of this project is to develop mathematical formulation for pricing and hedging financial derivative products for a pools of loans, taking into account of specific constraints that microloan lenders might have (large number of very small loans, no collateral etc). The project will build on two different strands of research in financial mathematics: the mathematics of pricing Collateralized Loan Obligations (CLOs) in developed markets and recent work on credit scoring for microloans in absence of customer credit history, using machine learning

小额供资机构在向发展中国家和发达国家的大部分成年人提供获得资本的机会方面发挥着关键作用。根据世界银行2017年的一份报告，全球三分之一的成年人口没有银行账户，因此依赖小额信贷机构或非正规经济中的贷款人获得信贷。鉴于小额金融机构在社会福祉方面继续发挥重要作用，它们必须拥有量身定制的适当数学工具，以量化和减轻向没有信用记录和抵押品的个人提供贷款的业务所带来的金融风险。该项目的目的是为各种贷款的金融衍生产品定价和套期保值制定数学公式，同时考虑到小额贷款放款人可能面临的具体限制（大量非常小的贷款，没有抵押品等）。该项目将建立在两个不同的金融数学研究基础上：发达市场中抵押贷款债务（CLO）定价的数学，以及最近在缺乏客户信用历史的情况下使用机器学习进行小额贷款信用评分的工作