Green finance—including environmental, social, and governance investing and sustainable finance regulations—is widespread, but can it substitute for carbon pricing in fighting climate change? In a unified model, I show that (i) when carbon prices reflect the social cost of carbon, green finance should not be used; (ii) when carbon prices are too low, green finance can implement the social optimum if each firm's cost of capital can be set to its sustainable discount rate , which increases with the ratio of carbon emissions to firm value. I provide calibrations, analyze stranded assets, and present implementations through subsidies or preferential financing for green firms.

[In a model with heterogeneous-risk-aversion agents facing margin constraints, we show how securities' required returns increase in both their betas and their margin requirements. Negative shocks to fundamentals make margin constraints bind, lowering risk-free rates and raising Sharpe ratios of risky securities, especially for high-margin securities. Such a funding-liquidity crisis gives rise to "bases," that is, price gaps between securities with identical cash-flows but different margins. In the time series, bases depend on the shadow cost of capital, which can be captured through the interest-rate spread between collateralized and uncollateralized loans and, in the cross-section, they depend on relative margins. We test the model empirically using the credit default swap—bond bases and other deviations from the Law of One Price, and use it to evaluate central banks' lending facilities.]

An important feature of financial markets is that securities are traded repeatedly by asymmetrically informed investors. We study how current and future adverse selection affect the required return. We find that the bid-ask spread generated by adverse selection is not a cost, on average, for agents who trade, and hence the bid-ask spread does not directly influence the required return. Adverse selection contributes to trading-decision distortions, however, implying allocation costs, which affect the required return. We explicitly derive the effect of adverse selection on required returns, and show how our result differs from models that consider the bid-ask spread to be an exogenous cost.

[We provide a model that links an asset's market liquidity (i.e., the ease with which it is traded) and traders' funding liquidity (i.e., the ease with which they can obtain funding). Traders provide market liquidity, and their ability to do so depends on their availability of funding. Conversely, traders' funding, i.e., their capital and margin requirements, depends on the assets' market liquidity. We show that, under certain conditions, margins are destabilizing and market liquidity and funding liquidity are mutually reinforcing, leading to liquidity spirals. The model explains the empirically documented features that market liquidity (i) can suddenly dry up, (ii) has commonality across securities, (iii) is related to volatility, (iv) is subject to "flight to quality," and (v) co-moves with the market. The model provides new testable predictions, including that speculators' capital is a driver of market liquidity and risk premiums.]

We model how investors allocate between asset managers, managers choose portfolios of multiple securities, fees are set, and security prices are determined. Investors are indifferent between higher-cost informed managers and lower-cost uninformed managers, interpreted as passive managers as their portfolio is linked to the " expected market portfolio." We make precise Samuelson's dictum by showing that active investors reduce micro-inefficiencies more than they do macro-inefficiencies. In fact, all inefficiency arises from systematic factors when the number of assets is large. Further, we show how the costs of active and passive investing affect macro- and micro-efficiency, fees, and assets managed by active and passive managers. Our findings help explain the rise of delegated asset management and the resultant changes in financial markets.

Many financial instruments are designed with embedded leverage, such as options and leveraged exchange-traded funds (ETFs). Embedded leverage alleviates investors’ leverage constraints, and, therefore, we hypothesize that embedded leverage lowers required returns. Consistent with this hypothesis, we find empirically that options and leveraged ETFs provide significant amounts of embedded leverage; this embedded leverage increases return volatility in proportion to the embedded leverage; and higher embedded leverage is associated with lower risk-adjusted returns. The results are statistically and economically significant, and we provide extensive robustness tests and discuss the broader implications of embedded leverage for financial economics.

I present closed-form solutions for prices, portfolios, and beliefs in a model where four types of investors trade assets over time: naive investors who learn via a social network, “fanatics” possibly spreading fake news, and rational short- and long-term investors. I show that fanatic and rational views dominate over time, and their relative importance depends on their following by influencers. Securities markets exhibit social network spillovers, large effects of influencers and thought leaders, bubbles, bursts of high volume, price momentum, fundamental momentum, and reversal. The model sheds new light on the GameStop event, historical bubbles, and asset markets more generally.

[We provide the impact on asset prices of search-and-bargaining frictions in over-the-counter markets. Under certain conditions, illiquidity discounts are higher when counterparties are harder to find, when sellers have less bargaining power, when the fraction of qualified owners is smaller, or when risk aversion, volatility, or hedging demand is larger. Supply shocks cause prices to jump, and then "recover" over time, with a time signature that is exaggerated by search frictions: The price jump is larger and the recovery is slower in less liquid markets. We discuss a variety of empirical implications.]

[We model demand-pressure effects on option prices. The model shows that demand pressure in one option contract increases its price by an amount proportional to the variance of the unhedgeable part of the option. Similarly, the demand pressure increases the price of any other option by an amount proportional to the covariance of the unhedgeable parts of the two options. Empirically, we identify aggregate positions of dealers and end-users using a unique dataset, and show that demand-pressure effects make a contribution to wellknown option-pricing puzzles. Indeed, time-series tests show that demand helps explain the overall expensiveness and skew patterns of index options, and cross-sectional tests show that demand impacts the expensiveness of single-stock options as well.]

We present a model with leverage and margin constraints that vary across investors and time. We find evidence consistent with each of the model's five central predictions: (1) Because constrained investors bid up high-beta assets, high beta is associated with low alpha, as we find empirically for US equities, 20 international equity markets, Treasury bonds, corporate bonds, and futures. (2) A betting against beta (BAB) factor, which is long leveraged low-beta assets and short high-beta assets, produces significant positive risk-adjusted returns. (3) When funding constraints tighten, the return of the BAB factor is low. (4) Increased funding liquidity risk compresses betas toward one. (5) More constrained investors hold riskier assets.