A theory of money needs a proper place for financial intermediaries. Intermediaries create money by taking deposits from savers and investing them in productive projects. The money multiplier depends on the size of intermediary balance sheets, and their ability to take risks. In downturns, as lending contracts and the money multiplier shrinks, the value of money rises. This leads to a Fisher deflation that hurts borrowers and amplifies shocks. An accommodative monetary policy in downturns, focused on the assets held by constrained agents, can mitigate these destabilizing adverse feedback effects. We devote particular attention to interest rate cuts, and study the potential for such policies to create moral hazard.

Traditional economics argues that financial derivatives, like CDOs and CDSs, ameliorate the negative costs imposed by asymmetric information. This is because securitization via derivatives allows the informed party to find buyers for less information-sensitive part of the cash flow stream of an asset (e.g., a mortgage) and retain the remainder. In this paper we show that this viewpoint may need to be revised once computational complexity is brought into the picture. Using methods from theoretical computer science this paper shows that derivatives can actually amplify the costs of asymmetric information instead of reducing them. Note that computational complexity is only a small departure from full rationality since even highly sophisticated investors are boundedly rational due to a lack of requisite computational resources.

When is record-keeping better arranged through a blockchain than through a traditional centralized intermediary? The ideal qualities of any record-keeping system are (i) correctness, (ii) decentralization, and (iii) cost efficiency. We point out a \textit{blockchain trilemma}: no ledger can satisfy all three properties simultaneously. A centralized record-keeper extracts rents due to its monopoly on the ledger. Its franchise value dynamically incentivizes correct reporting. Blockchains drive down rents by allowing for free entry of  record-keepers and portability of information to competing ``forks.'' Blockchains must therefore provide static incentives for correctness through computationally expensive proof-of-work algorithms and permit record-keepers to roll back history in order to undo fraudulent reports. While blockchains can keep track of ownership transfers, enforcement of possession rights is often better complemented by centralized record-keeping.

The reversal interest rate is the rate at which accommodative monetary policy reverses and becomes contractionary for lending. Its determinants are (i) banks' fixed-income holdings, (ii) the strictness of capital constraints, (iii) the degree of pass-through to deposit rates, and (iv) the initial capitalization of banks. Quantitative easing increases the reversal interest rate and should only be employed after interest rate cuts are exhausted. Over time the reversal interest rate creeps up since asset revaluation fades out as fixed-income holdings mature while net interest income stays low. We calibrate a New Keynesian model that embeds our banking frictions.

China's economic model involves regular and intensive government interventions in financial markets, while Western policymakers often refrain from substantial interventions outside crisis periods. We develop a theoretical framework to rationalize the approaches of both China and the West to managing the financial system as being optimal given the differences in their respective economies. In this framework, a government leans against trading of noise traders but at the expense of introducing policy noise to the market. Our welfare analysis shows that under certain underlying economic conditions, the optimal government policy induces a government-centric equilibrium, in which government intervention is so intensive that all investors choose to acquire private information about policy noise rather than fundamentals. This policy regime characterizes China's approach with financial stability prioritized over other policy objectives.

The financial crises of the last twenty years brought new economic concepts into classrooms discussions. This article introduces undergraduate students and teachers to seven of these models: (i) misallocation of capital inflows, (ii) modern and shadow banks, (iii) strategic complementarities and amplification, (iv) debt contracts and the distinction between solvency and liquidity, (v) the diabolic loop, (vi) regional flights to safety, and (vii) unconventional monetary policy. We apply each of them to provide a full account of the euro crisis of 2010-12.

Many business sectors and households face an unprecedented loss of income in the current COVID recession, triggering financial distress, separations, and bankruptcy.  Rather than stimulating demand, government policy’s main aim should be to provide insurance to firms and workers to avoid undue scarring that will hamper a recovery, once the pandemic is past. We develop a corporate finance framework to guide interventions in credit markets to avoid such scarring.  We emphasize three main results. First, policy should inject liquidity into small and medium sized firms that are liquidity constrained and for which social costs of bankruptcy are high. Second, large firms for whom solvency is the dominant issue require a more nuanced approach.  Debt overhang creates a distortion leading these firms to fire workers, forgo expenditures that maintain enterprise value, and delay filing for a Chapter 11 bankruptcy longer than is socially efficient. Government resources toward reducing the legal and financial costs of bankruptcy are unambiguously beneficial. Policies that reduce funding costs are only socially desirable if the pandemic is expected to be short-lived and if bankruptcy costs are high. Last, transfers necessary to avoid bankruptcy allow borrowers to continue paying their mortgages or credit card bills and ultimately benefit owners of assets such as real estate or credit card receivables.  Taxes to fund transfers should be raised from these asset owners.

This paper finds non-interest income to be positively correlated with total systemic risk for a large sample of U.S. banks.  Decomposing total systemic risk into three components, we find that non-interest income has a positive relationship with a bank’s tail risk, a positive relationship with a bank’s interconnectedness risk, and an insignificant or positive relationship with a bank’s exposure to macroeconomic and finance factors. These results are generally robust to endogenizing for non-interest income and for trading and other non-interest income activities.

This paper develops a framework for measuring, allocating and managing systemic risk. SystRisk, our measure of total systemic risk captures the a priori cost to society for providing tail-risk insurance to the financial system. Our allocation principle distributes the total systemic risk among individual institutions according to their size-shifted marginal contributions. To describe economic shocks and systemic feedback effects we propose a reduced form stochastic model that can be calibrated to historical data. We also discuss systemic risk limits, systemic risk charges and a cap and trade system for systemic risk.