Optimal dividend policies with random profitability

We study an optimal dividend problem under a bankruptcy constraint. Firms face a trade-off between potential bankruptcy and extraction of profits. In contrast to previous works, general cash flow drifts, including Ornstein--Uhlenbeck and CIR processes, are considered. We provide rigorous proofs of continuity of the value function, whence dynamic programming, as well as comparison between the sub- and supersolutions of the Hamilton--Jacobi--Bellman equation, and we provide an efficient and convergent numerical scheme for finding the solution. The value function is given by a nonlinear PDE with a gradient constraint from below in one dimension. We find that the optimal strategy is both a barrier and a band strategy and that it includes voluntary liquidation in parts of the state space. Finally, we present and numerically study extensions of the model, including equity issuance and credit lines

A Metaheuristic-Based Simulation Optimization Framework For Supply Chain Inventory Management Under Uncertainty

The need for inventory control models for practical real-world applications is growing with the global expansion of supply chains. The widely used traditional optimization procedures usually require an explicit mathematical model formulated based on some assumptions. The validity of such models and approaches for real world applications depend greatly upon whether the assumptions made match closely with the reality. The use of meta-heuristics, as opposed to a traditional method, does not require such assumptions and has allowed more realistic modeling of the inventory control system and its solution. In this dissertation, a metaheuristic-based simulation optimization framework is developed for supply chain inventory management under uncertainty. In the proposed framework, any effective metaheuristic can be employed to serve as the optimizer to intelligently search the solution space, using an appropriate simulation inventory model as the evaluation module. To be realistic and practical, the proposed framework supports inventory decision-making under supply-side and demand-side uncertainty in a supply chain. The supply-side uncertainty specifically considered includes quality imperfection. As far as demand-side uncertainty is concerned, the new framework does not make any assumption on demand distribution and can process any demand time series. This salient feature enables users to have the flexibility to evaluate data of practical relevance. In addition, other realistic factors, such as capacity constraints, limited shelf life of products and type-compatible substitutions are also considered and studied by the new framework. The proposed framework has been applied to single-vendor multi-buyer supply chains with the single vendor facing the direct impact of quality deviation and capacity constraint from its supplier and the buyers facing demand uncertainty. In addition, it has been extended to the supply chain inventory management of highly perishable products. Blood products with limited shelf life and ABO compatibility have been examined in detail. It is expected that the proposed framework can be easily adapted to different supply chain systems, including healthcare organizations. Computational results have shown that the proposed framework can effectively assess the impacts of different realistic factors on the performance of a supply chain from different angles, and to determine the optimal inventory policies accordingly

Essays on macroeconomics of banking and asset bubbles

The dissertation consists of three chapters. In the first chapter, I develop a model to study the production of private safe assets by the banking sector. In response to a shortage of safe assets, the banking sector produces more private safe assets which alleviate the decline of aggregate investment and output. However, producing more private safe assets exposes the bank to more aggregate risk. Macroprudential policies can adjust the production of private safe assets with a tradeoff: encouraging the production of private safe assets alleviates the safe asset shortage problem and improves output, at the cost of a more volatile economy. In the second chapter, I document that during the 2008 financial crisis, U.S. shadow banks deleveraged sharply while commercial banks maintained their leverage. I find that banks that relied more on short-term funding tended to deleverage more during the crisis. I build a model to incorporate both shadow banks and commercial banks with different leverage determination mechanisms. The model can explain the leverage dynamics of the banking sector and the flight-to-quality phenomenon observed in data. The third chapter is coauthored with Jianjun Miao and Pengfei Wang. We revisit Galí’s (2014) analysis by extending his model to incorporate persistent bubble shocks. We find that under adaptive learning, a stable bubbly steady state and the associated sunspot solutions under optimal monetary policy are not E-stable. When deriving the unique forward-looking minimum stable variable (MSV) solution around an unstable bubbly steady state, we obtain results that are consistent with the conventional views: leaning against the wind policy reduces bubble volatility and is optimal. Such a steady state and the associated MSV solution are E-stable

PRICING AND FINANCING DECISIONS WITH UNRELIABLE SUPPLIES

Ph.DDOCTOR OF PHILOSOPH