Annual Review of Financial Economics - Current Issue
Volume 15, 2023
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How to Use Microdata for Macro-Finance
David Sraer, and David ThesmarVol. 15 (2023), pp. 387–406More LessFinancial frictions generate misallocation of resources across firms by restricting productive firms’ access to capital. This article reviews the literature that estimates the aggregate losses generated by such misallocation. The first approach relies on structural estimations: A model of firm dynamics with financial frictions is typically estimated and used to compare efficiency in the actual economy with a counterfactual with perfect financial markets. We argue that robustness is a central concern for this structural literature and discuss ways to analyze robustness in structural estimation (using well-identified moments and analyzing sensitivity to moments). The second approach relies on sufficient statistics, namely formulas that measure misallocation using simple empirical statistics and that are valid for a class of models. We discuss key contributions in that space, the generality of this approach, and the conditions under which it is valid. Sufficient statistic approaches are simpler and more robust, but they come at a cost: The range of counterfactual analyses that can be explored is more limited.
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Macro-Finance Models with Nonlinear Dynamics
Vol. 15 (2023), pp. 407–432More LessWe review macro-finance models featuring nonlinear dynamics that have recently been developed in the literature, including models with funding liquidity constraints, market liquidity frictions, and bank run frictions, and discuss the empirical evidence and challenges of this class of models. We also construct an illustrative model featuring financial frictions and nonlinear dynamics for readers who are unfamiliar with the literature. We solve the model using different solution techniques, including both global and perturbation solution methods, and comprehensively compare the accuracy of these solutions. Within this framework, we highlight that local linearization approximations omit important nonlinear dynamics and yield biased impulse responses.
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Inflation and Asset Returns
Vol. 15 (2023), pp. 433–448More LessThe past half-century has seen major shifts in inflation expectations, how inflation comoves with the business cycle, and how stocks comove with Treasury bonds. Against this backdrop, we review the economic channels and empirical evidence on how inflation is priced in financial markets. Not all inflation episodes are created equal. Using a New Keynesian model, we show how “good” inflation can be linked to demand shocks and “bad” inflation to cost-push shocks driving the economy. We then discuss asset pricing implications of “good” and “bad” inflation. We conclude by providing an outlook for inflation risk premia in the world of newly rising inflation.
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Inflation-Adjusted Bonds, Swaps, and Derivatives
Vol. 15 (2023), pp. 449–471More LessThe purpose of this article is to review the literature on inflation-adjusted bonds, swaps, and derivatives. The methodology for valuation and risk management of these securities is an application of the foreign currency extension of a standard HJM term structure model. The two “currencies” in the extended model are real and nominal prices. Currently, for their use in monetary policy, the empirical literature primarily uses these models to estimate both the expected inflation rate and the inflation risk premium. A literature investigating the efficiency of the inflation derivative markets and a comparison of the relevant valuation models is almost nonexistent and a fruitful area for future research.
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Short-Term Rate Benchmarks: The Post-LIBOR Regime
Vol. 15 (2023), pp. 473–491More LessThe London Interbank Offered Rate (LIBOR), the predominant family of global short-term rate benchmarks for the past 40 years, ceased to exist in June 2023. Given the low volumes of interbank loans on which LIBOR had been based, the revelations that LIBOR had been manipulated, and the risks that countless LIBOR-dependent financial instruments without fallback rates would be cast into limbo, regulators over the last decade pushed to replace LIBOR with risk-free overnight rate benchmarks. In the United States, the new benchmark, Secured Overnight Financing Rate (SOFR), is an overnight rate based on US Treasury repo transactions: Use of term rates and derivatives on term rates is limited, and use of credit-sensitive term rates is discouraged. This article recounts the rise and fall of LIBOR; reviews the academic literature on the efficiency benefits of benchmarks, the LIBOR scandal, and the pros and cons of risk-free versus credit-sensitive rate benchmarks; and calls for further academic work on the current policy of entrenching a single-benchmark SOFR regime relative to the alternative of encouraging a two- or multi-benchmark regime of SOFR and one or more credit-sensitive term rates.
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The Q-Measure Dynamics of Forward Rates
Vol. 15 (2023), pp. 493–522More LessI review how the theoretical modeling of the dynamics of forward rates in the context of derivatives pricing has evolved over time. I review the theoretical developments from the short rate models of the 1980s to the stochastic-volatility extensions of the SABR model. I argue that how the theory developed can be understood only by taking into account the institutional setting of derivatives trading and that the modeling choices were motivated to a surprisingly large extent by how the market evolved. I conclude with an assessment of which of these theoretical contributions have had a lasting and meaningful effect on the financial theory of asset pricing.
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Smart Contracts and Decentralized Finance
Kose John, Leonid Kogan, and Fahad SalehVol. 15 (2023), pp. 523–542More LessWe explain the mechanics of smart contracts. We then highlight the benefits of smart contracts, such as overcoming commitment problems. We also discuss limitations, such as the difficulty for smart contracts to access information external to the blockchain and the difficulty of integrating smart contract code with traditional legal enforcement. We further highlight how the absence of a trusted intermediary inflates implementation costs for blockchain applications. We conclude with a discussion of the most prominent smart contract applications in decentralized finance: token issuance (e.g., initial coin offerings, nonfungible tokens), decentralized exchanges, and protocols for loanable funds. Our survey covers both institutional details and relevant literature.
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Robo-Advice: Transforming Households into Rational Economic Agents
Vol. 15 (2023), pp. 543–563More LessRobo-advice uses big and open data to provide consumers with fully informed and rational-expectation benchmarks in all realms of household finance, including consumption, saving, investment, and debt management choices. It also minimizes the monetary, cognitive, and psychological costs that households face in economic transactions. We review recent research on the features and effects of robo-advice on individual and aggregate economic outcomes through the lens of its differences from traditional human advice. We discuss the distributional implications of robo-advice, its potential role in increasing the effectiveness of economic policies, the role of providers’ incentives, and several questions that are still wide open for researchers in finance, economics, social psychology, and related fields.
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Algorithmic Fairness
Vol. 15 (2023), pp. 565–593More LessThis article reviews the recent literature on algorithmic fairness, with a particular emphasis on credit scoring. We discuss human versus machine bias, bias measurement, group versus individual fairness, and a collection of fairness metrics. We then apply these metrics to the US mortgage market, analyzing Home Mortgage Disclosure Act data on mortgage applications between 2009 and 2015. We find evidence of group imbalance in the dataset for both gender and (especially) minority status, which can lead to poorer estimation/prediction for female/minority applicants. Loan applicants are handled mostly fairly across both groups and individuals, though we find that some local male (nonminority) neighbors of otherwise similar rejected female (minority) applicants were granted loans, something that warrants further study. Finally, modern machine learning techniques substantially outperform logistic regression (the industry standard), though at the cost of being substantially harder to explain to denied applicants, regulators, or the courts.
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IPOs and SPACs: Recent Developments
Vol. 15 (2023), pp. 595–615More LessAfter two decades of low initial public offering (IPO) activity and a number of regulatory changes, the number of IPOs of both operating companies and special purpose acquisition companies (SPACs) boomed in the United States in 2021 before collapsing in 2022. In recent years, surging valuations have resulted in many private companies achieving “unicorn” status, a valuation of $1 billion or more, partly fueled by investments from mutual funds. Many of the unicorns that have gone public have done so with dual-class share structures. We compare three alternative mechanisms for going public, including traditional IPOs, mergers with SPACs, and direct listings. The most common exit for successful venture capital–backed companies, however, continues to be by merging with a larger company.
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Swing Pricing: Theory and Evidence
Vol. 15 (2023), pp. 617–640More LessOpen-end mutual funds offer investors same-day liquidity while holding assets that in some cases take several days to sell. This liquidity transformation creates a potentially destabilizing first-mover advantage: When asset prices fall, investors who exit a fund earlier may pass the liquidation costs generated by their share redemptions to investors who remain in the fund. This incentive becomes particularly acute in periods of market stress, and it can amplify fire-sale spillover losses to other market participants. Swing pricing is a liquidity management tool that targets this first-mover advantage. It allows a fund to adjust or “swing” its net asset value in response to large flows out of or into a mutual fund. This article discusses the industry and regulatory context for swing pricing, and it reviews theory and empirical evidence on the design and effectiveness of swing pricing. The article concludes with directions for further research.
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Pari-Mutuel Betting Markets: Racetracks and Lotteries Revisited
Vol. 15 (2023), pp. 641–662More LessThis survey discusses the state of the art in research in racetrack and lottery investment markets. Market efficiency and the pricing of various wagers are studied along with new developments since the Thaler & Ziemba (1988) review. The weak form inefficient market pricing approach using stochastic programming optimization models changed racetrack betting from handicapping to a financial market allowing professional syndicates to operate as hedge funds. Topics discussed include arbitrage and risk arbitrage, syndicates, betting exchange rebates, behavioral biases, and fundamental and mispricing information in racetrack and lottery markets. Similar models can be used to successfully trade stock market anomalies. Supplemental Materials are included online.
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