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.

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 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.

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.

We examine the relation among measures of credit expansion, measures of financial market stress, and standard macroeconomic aggregates. We use a form of structural VAR with monthly data on 10 variables. The model explains observed variation as driven by 10 mutually independent structural disturbances. We identify the shocks from variation across time in their relative variability. One of them emerges as representing monetary policy. We find two distinct financial stress shocks, suggesting that attempts to create a one-dimensional index of financial stress may be misguided. While our results are consistent with the finding by others of a negative reduced form relation between credit expansion and future output growth at certain frequencies, we find the output decline to be explained by the monetary policy response to the inflation that accompanies the credit expansion. In pseudo-out-of-sample forecasting tests, neither bond spreads, interbank spreads, nor credit aggregates had much predictive value far in advance of the 2008-9 downturn, though spreads (but not credit aggregates) were helpful in recognizing the downturn once it had begun.

China's economic model involves active government intervention in financial markets. We develop a theoretical framework that anchors government intervention on a mission to prevent market breakdown and volatility explosion caused by the reluctance of short-term investors to trade against noise traders. In the presence of information frictions the
government can alter market dynamics by making noise in its intervention program an additional factor driving asset prices, and can divert investor attention toward acquiring information about this noise factor rather than fundamentals (as a result of complementarity in investors' information acquisition). Through this latter channel, the widely-adopted objective of government intervention to reduce asset price volatility may exacerbate,
rather than improve, information efficiency of asset prices.

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.

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.

China’s gradualistic approach allowed the government to learn how the economy reacts to small policy changes, and to adjust its reforms before implementing them in full. With fully developed financial markets, however, private actors’ may front-run future policy changes making it impossible for the implement policies gradually. With financial markets the government faces a time-inconsistency problem. The government would like to commit to a gradualistic approach, but after it observes the economy’s quick reaction, it has no incentive to implement its policies in small steps.

The euro crisis was fueled by the diabolic loop between sovereign risk and bank risk, coupled with cross-border flight-to-safety capital flows. European Safe Bonds (ESBies), a union-wide safe asset without joint liability, would help to resolve these problems. We make three contributions. First, numerical simulations show that ESBies would be at least as safe as German bunds and approximately double the supply of euro safe assets when protected by a 30%-thick junior tranche. Second, a model shows how, when and why the two features of ESBies---diversification and seniority---can weaken the diabolic loop and its diffusion across countries. Third, we propose a step-by-step guide on how to create ESBies, starting with limited issuance by public or private-sector entities.

This paper puts forward a manual for how to set up and solve a continuous time model that allows to analyze endogenous (1) level and risk dynamics. The latter includes (2) tail risk and crisis probability as well as (3) the Volatility Paradox. Concepts such as (4) illiquidity and liquidity mismatch, (5) endogenous leverage, (6) the Paradox of Prudence, (7) undercapitalized sectors (8) time-varying risk premia, and (9) the external funding premium are part of the analysis. Financial frictions also give rise to an endogenous (10) value of money.

We develop a structural theory of beliefs and behavior, that relaxes the assumption of time-consistency in beliefs. Our theory is based on the trade-off between optimism, which raises anticipatory utility, and objectivity, which promotes efficient actions. We present it in the context of allocating work on a project over time, develop testable implications to contrast it with models assuming time-inconsistent preferences, and compare its predictions to existing evidence on behavior and beliefs. Our predictions are: (i) optimal beliefs are optimistic and time-inconsistent; (ii) people optimally exhibit the planning fallacy; (iii) incentives for rapid task completion make beliefs more optimistic and worsen work smoothing, while incentives for accurate duration prediction make beliefs less optimistic and improve work smoothing; (iv) without a commitment device, beliefs become less optimistic over time; (v) in the presence of a commitment device, beliefs may become more optimistic over time, and people optimally exhibit preference for commitment.

In our incomplete markets economy households choose portfolios consisting of risky (uninsurable) capital and money. Money is a bubble, it has positive value even though it yields no payoff. The market outcome is constrained Pareto inefficient due to a pecuniary externality. Each individual agent takes the real interest rate as given, while in the aggregate it is driven by the economic growth rate, which in turn depends on individual portfolio decisions. Higher inflation due to higher money growth lowers the real interest rate (on money) and tilts the portfolio choice towards physical capital investment. Modest inflation boosts growth rate and welfare.