Loan guarantees and SMEs' investments under asymmetric information and Bayesian learning
构建了贷款担保下的动态投资模型，研究保险公司在信息劣势下通过贝叶斯学习了解借款企业质量，分析分离均衡与混同均衡的条件，发现学习能缓解逆向选择并降低担保成本。
Abstract We develop a dynamic investment model with loan guarantees wherein insurers face information disadvantages and learn about borrower quality. Borrowers signal their qualities through investment timing, which is characterized by the investment threshold and elapsed time. We derive the conditions for separating or pooling equilibria. We show that the separating investment threshold is constant and determined mainly by the maximum threshold preventing mimicry. If project risk is higher (lower) than the market growth rate, the pooling investment threshold declines (increases) with elapsed time, and learning enhances (reduces) the willingness of high‐quality borrowers to wait. Learning alleviates adverse selection and reduces guarantee costs. These effects are more pronounced with a greater uncertainty of the insurer on borrower quality. We reveal dual effects of waiting. The worse the market prospect, the higher the value of waiting in pooling outcomes. Fee‐for‐guarantee swaps are superior to equity‐for‐guarantee swaps in environments with marked information asymmetry.