International Prudential Regulation, Regulatory Risk and the Cost of Bank Capital

We define regulatory risk as any regulatory action that leads to an increase in the cost of capital for the regulated firm. In a general equilibrium setting the paper considers the impact of globally harmonising capital adequacy requirements on the cost of bank equity capital. The results show that uniform increases in capital requirements lead to an increase in the cost of capital. However when regulatory standards differ across countries, financial integration leads to positive spillovers which reduces the cost of capital mark up for a given increase in bank capital. Accordingly, regulatory risk may be greater under a regulatory agreement such as the Basel Accord which imposes international uniformity in capital ratios.

Endogenous Capital and Profitability in Banking

This paper investigates the relation between bank capital and profitability. To my knowledge, no previous paper has analysed this problem in a two-equation structural model. Contrary to what is reported with surprising frequency in this field of research, the results show no statistically significant relationship between capital and profitability. Given non-binding capital requirements this finding is consistent with the view that, while raising capital is costly for banks, it is associated with compensating benefits that offset these additional costs. Consequently, when capital structure is endogenously determined in a profit maximising equilibrium, no systematic relation between capital and profit is expected.

A Theory of Precautionary Regulatory Capital in Banking

The orthodox assumption in the banking literature is that capital requirements are a binding constraint on banking behaviour. This is in conflict with the empirical observation that banks hold a bu¤er of capital well in excess of the minimum requirements. This paper develops a model where capital is endogenously determined within a profit maximising equilibrium. Optimality involves balancing the reduction in expected costs associated with regulatory breach with the excess cost of financing from increasing capital. We demonstrate that when the equilibrium probability of regulatory breach is less than one half, banks are expected to hold precautionary capital.

Data-driven Methodologies and Applications in Urban Mobility

The world is urbanizing at an unprecedented rate where urbanization goes from 39% in 1980 to 58% in 2019 (World Bank, 2019). This poses more and more transportation demand and pressure on the already at or over-capacity old transport infrastructure, especially in urban areas. Along the same timeline, more data generated as a byproduct of daily activity are being collected via the advancement of the internet of things, and computers are getting more and more powerful. These are shown by the statistics such as 90% of the world’s data is generated within the last two years and IBM’s computer is now processing at the speed of 120,000 GPS points per second. Thus, this dissertation discusses the challenges and opportunities arising from the growing demand for urban mobility, particularly in cities with outdated infrastructure, and how to capitalize on the unprecedented growth in data in solving these problems by ways of data-driven transportation-specific methodologies. The dissertation identifies three primary challenges and/or opportunities, which are (1) optimally locating dynamic wireless charging to promote the adoption of electric vehicles, (2) predicting dynamic traffic state using an enormously large dataset of taxi trips, and (3) improving the ride-hailing system with carpooling, smart dispatching, and preemptive repositioning. The dissertation presents potential solutions/methodologies that have become available only recently thanks to the extraordinary growth of data and computers with explosive power, and these methodologies are (1) bi-level optimization planning frameworks for locating dynamic wireless charging facilities, (2) Traffic Graph Convolutional Network for dynamic urban traffic state estimation, and (3) Graph Matching and Reinforcement Learning for the operation and management of mixed autonomous electric taxi fleets. These methodologies are then carefully calibrated, methodically scrutinized under various performance metrics and procedures, and validated with previous research and ground truth data, which is gathered directly from the real world. In order to bridge the gap between scientific discoveries and practical applications, the three methodologies are applied to the case study of (1) Montgomery County, MD, (2) the City of New York, and (3) the City of Chicago and from which, real-world implementation are suggested. This dissertation’s contribution via the provided methodologies, along with the continual increase in data, have the potential to significantly benefit urban mobility and work toward a sustainable transportation system