Creating more efficient trading platforms can lower costs but also distort trade patterns. We model brokers’ tokenization decisions—i.e., whether to represent assets as tradable tokens on a shared, programmable ledger. Brokers with heterogeneous market power compete to attract investors and execute their trades intra-broker or over-the-counter. Moreover, coalitions of brokers can invest in creating a tokenized market with faster, cheaper inter-broker settlement. Due to trade diversion incentives, equilibrium coalition structures feature excessive investment or insufficient unification. Public-private cost-sharing or interoperability mandates fail to achieve first best in isolation, but succeed when implemented jointly. These results withstand incorporating an open-access ledger (e.g., a public blockchain).

In this paper, we examine the influence of monetary policy on nineteen clean energy stock indices for the period 2010-2023. We uncover the common factors among various sectors and sub-sectors of clean energy stocks and shed light on how monetary policy shocks propagate across different sectors. Our findings reveal that the panel of indices is influenced mainly by one common factor that accounts for up to 60% of the data, and this factor responds positively to monetary policy shocks. However, the responses of individual clean energy stock indices vary remarkably across different sectors and sub-sectors. Some sectors, such as energy storage, green IT, and wind stocks, respond positively, while others, like smart grid, green building, and transportation stocks, show negative responses to monetary tightening. Understanding this heterogeneity is crucial for effective investment strategies and underscores the need for targeted policy interventions considering the sensitivity of individual sectors.

A long tradition of macroeconomic analysis accords monetary policy only a transient role

in driving real outcomes. At the same time, a large body of evidence highlights the long-lasting impact of boom-bust cycles. We present a model where monetary policy, through

its impact on and reaction to the financial cycle, influences long-term economic trajectories. The core setup is an overlapping generations model featuring bank financing – the creation of bank loans and inside money – which is critical for production and consumption. Monetary policy attains the first-best allocation by sustaining an efficient flow of financing. We then introduce coordination-failure frictions among lenders, which give rise to an endogenous boom-bust cycle in bank financing and an intertemporal policy tradeoff. A forward-looking policymaker optimally leans against excessive risk-taking during the boom, trading off short-term activity with longer-term stability. An inordinate focus on short-term outcomes can lead to ‘monetary policy hysteresis’, where low interest rates increase the vulnerability to financial busts over successive cycles. As a result, low rates can beget lower rates.

The relative restrictiveness of a central bank’s supply of money predicts the raw and risk-adjusted returns of its currency—both next month and at least three years into the future. Ar-chived data, known by currency traders at the time, estimates central bank restrictiveness as a scaling of the residual from out-of-sample panel regressions of M1 on macroeconomic variables tied to domestic and international transaction requirements. Carry’s ability to forecast currency returns is subsumed by the central bank restrictiveness signal, which also forecasts inflation.