This paper proposes a new method to measure and monitor the risk in a banking system. Standard tools that regulators require banks to use for their internal risk management are applied at the level of the banking system to measure the risk of a regulator’s portfolio. Using a sample of international banks from 1988 until 2002, I estimate the dynamics and correlations between bank asset portfolios. To obtain measures for the risk of a regulator’s portfolio, I model the individual liabilities that the regulator has to each bank as contingent claims on the bank’s assets. The portfolio aspect of the regulator’s liability is explicitly considered and the methodology allows a comparison of sub-samples from different countries. Correlations, bank asset volatility, and bank capitalization increase for North American and somewhat for European banks, while Japanese banks face deteriorating capital levels. In the sample period, the North American banking system gains stability while the Japanese banking sector becomes more fragile. The expected future liability of the regulator varies substantially over time and is especially high during the Asian crisis starting in 1997. Further analysis shows that the Japanese banks contribute most to the volatility of the regulator’s liability at that time. Larger and more profitable banks have lower systemic risk and additional equity capital reduces systemic risk only for banks that are constrained by regulatory capital requirements.

The severity and longevity of the recession caused by the 2007 financial crisis has highlighted the lack of a reliable macro-based financial regulation framework. As a consequence, addressing the link between the stability of the financial system as a whole and the performance of the overall economy has become a mandate for policymakers and scholars. Many countries have adopted macroprudential tools as policy responses for safeguarding the financial system. This paper provides a literature review of macroprudential policies, its objectives and the challenges that a macro-based framework needs to overcome, such as financial stability, procyclicality, and systemic risk.

When setting banks’ regulatory capital requirement based on their contribution to the overall risk of the banking system we have to consider that the risk of the banking system as well as each bank’s risk contribution changes once bank equity capital gets reallocated. We define macroprudential capital requirements as the fixed point at which each bank’s capital requirement equals its contribution to the risk of the system under the proposed capital requirements. We use a network based structural model to measure systemic risk and how it changes with bank capital and allocate risk to individual banks based on five risk allocation mechanisms used in the literature. Using a sample of Canadian banks we find that macroprudential capital allocations can differ by as much as 25% from observed capital levels, are not trivially related to bank size or individual bank default probability, increase in interbank assets, and differ substantially from a simple risk attribution analysis. We further find that across all risk allocation mechanisms macroprudential capital requirements reduce the default probabilities of individual banks as well as the probability of a systemic crisis by about 25%. Macroprudential capital requirements are robust to model risk and are positively correlated to future capital raised by banks as well as future losses in equity value. Our results suggest that financial stability can be substantially enhanced by implementing a systemic perspective on bank regulation.

This paper surveys the growing empirical literature on decentralized finance (DeFi), emphasizing how protocol design and incentive structures shape economic outcomes in blockchain-based financial systems. We review evidence on tokens, decentralized exchanges, lending platforms, yield farming, derivatives, governance, infrastructure, and regulation. Across these domains, research highlights mechanisms of liquidity provision, price discovery, leverage, systemic fragility, and investor behavior, as well as vulnerabilities stemming from arbitrage frictions, liquidation dynamics, and maximal extractable value. We also examine the roles of audits, oracle networks, settlement mechanisms, and transparency tools in substituting for traditional oversight. The findings indicate that DeFi replicates many functions of traditional finance while introducing new risks linked to pseudonymity, smart contracts, and composability. The survey concludes by outlining open questions for research and policy on market efficiency, governance, systemic risk, and long-term sustainability.

ABSTRACT Uniswap is a system of smart contracts on the Ethereum blockchain and is the largest decentralized exchange with a liquidity balance worth up to 4 billion USD and daily trading volume of up to 7 billion USD. It is a new model of liquidity provision, so‐called automated market making. For this new market form, we characterize equilibrium in the liquidity pools. We collect all 95.8 million Uniswap interactions and compare this automated market maker (AMM) to a centralized limit order book. We document absence of long‐lived arbitrage opportunities, and show conditions under which the AMM dominates a limit order market.

Abstract Analysts in a bank's research department cover firms that have no relationship with the bank as well as companies in which the bank has a strategic interest. Officially, banks must establish Chinese Walls around their research departments to allow the analysts to work independently and to avoid the flow of insider information. We examine analyst behavior under long-term bank-firm relationships using ownership data and analysts' earnings per share forecasts for German companies from 1994 to 2001. We find evidence that is consistent with analysts reconciling their employers' interests with their own career concerns. They seem to use their information advantage strategically by releasing favorable and thereby more precise reports when the market underestimates earnings. In order not to jeopardize the bank-client relationship, they suppress negative information when the market is too optimistic. Combining situations where the market over- and underestimates earnings, we can replicate the unconditional positive bias in analyst forecasts found in the previous literature. Despite the bias in affiliated analysts' forecasts, they nonetheless selectively communicate valuable information to investors.

In this paper we compare the out-of-sample performance of two common extensions of the Black–Scholes option pricing model, namely GARCH and stochastic volatility (SV). We calibrate the three models to intraday FTSE 100 option prices and apply two sets of performance criteria, namely out-of-sample valuation errors and Value-at-Risk (VaR) oriented measures. When we analyze the fit to observed prices, GARCH clearly dominates both SV and the benchmark Black–Scholes model. However, the predictions of the market risk from hypothetical derivative positions show sizable errors. The fit to the realized profits and losses is poor and there are no notable differences between the models. Overall, we therefore observe that the more complex option pricing models can improve on the Black–Scholes methodology only for the purpose of pricing, but not for the VaR forecasts.

Abstract Trade credit can serve as a collusion mechanism for competing supply chains to increase producer surplus in medium concentrated industries. We analyze theoretically how this form of financing influences retailers’ behavior in the product market, study incentives to deviate, and show evidence consistent with the model’s predictions. Trade credit use is inversely U shaped in industry concentration, and this pattern is more pronounced in industries more prone to collusion and when incentives to deviate are smaller.