A Fast Literature Search Engine based on top-quality journals, by Dr. Mingze Gao.

• Zviadadze, I. (2021). Term structure of consumption risk premia in the cross-section of currency returns. Review of Financial Studies, 34, 6032–6086.

  This paper develops a methodology to test structural asset pricing models based on their implications for the multiperiod risk-return trade-off. A new measure, the term structure of risk, captures the sensitivities of multiperiod expected returns to structural shocks. The level and slope of the term structure of risk can indicate misspecification in equilibrium models. I evaluate the performance of asset pricing models with long-run risk, consumption disasters, and variance shocks. I find that only a model with multiple shocks in the variance of consumption growth is consistent with the propagation of and compensation for risk in the aggregate stock market.

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• Zingales, L. (1994). The Value of the Voting Right: A Study of the Milan Stock Exchange Experience. Review of Financial Studies, 7, 125–148.

  I study the large premium (82 percent) attributed to voting shares on the Milan Stock Exchange. The premium varies according to the ownership structure and the concentration of the voting rights, and it can be rationalized in the presence of enormous private benefits of control. A case study seems to indicate that in Italy private benefits of control can easily be worth more than 60 percent of the value of nonvoting equity. A tentative explanation for these findings is provided.

• Ziegler, A. (2007). Why Does Implied Risk Aversion Smile? Review of Financial Studies, 20, 859–904.

  Implied risk aversion estimates reported in the literature are strongly U-shaped. This article explores different potential explanations for these “smile” patterns: (i) preference aggregation, both with and without stochastic volatility and jumps in returns, (ii) misestimation of investors’ beliefs caused by stochastic volatility, jumps, or a Peso problem, and (iii) heterogeneous beliefs. The results reveal that preference aggregation and misestimation of investors’ beliefs caused by stochastic volatility and jumps are unlikely to be the explanation for the smile. Although a Peso problem can account for the smile, the required probability of a market crash is unrealistically large. Heterogeneous beliefs cause sizable distortions in implied risk aversion, but the degree of heterogeneity required to explain the smile is implausibly large.

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• Zhu, H. (2014). Do Dark Pools Harm Price Discovery? Review of Financial Studies, 27, 747–789.

  Dark pools are equity trading systems that do not publicly display orders. Dark pools offer potential price improvements but do not guarantee execution. Informed traders tend to trade in the same direction, crowd on the heavy side of the market, and face a higher execution risk in the dark pool, relative to uninformed traders. Consequently, exchanges are more attractive to informed traders, and dark pools are more attractive to uninformed traders. Under certain conditions, adding a dark pool alongside an exchange concentrates price-relevant information into the exchange and improves price discovery. Improved price discovery coincides with reduced exchange liquidity.

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• Zhu, H. (2012). Finding a Good Price in Opaque Over-the-Counter Markets. Review of Financial Studies, 25, 1255–1285.

  This article offers a dynamic model of opaque over-the-counter markets. A seller searches for an attractive price by visiting multiple buyers, one at a time. The buyers do not observe contacts, quotes, or trades elsewhere in the market. A repeat contact with a buyer reveals the seller's reduced outside options and worsens the price offered by the revisited buyer. When the asset value is uncertain and common to all buyers, a visit by the seller suggests that other buyers could have quoted unattractive prices and thus worsens the visited buyer's inference regarding the asset value.

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• Zhu, C. (2019). Big Data as a Governance Mechanism. Review of Financial Studies, 32, 2021–2061.

  This study empirically investigates two effects of alternative data availability: stock price informativeness and its disciplining effect on managers’ actions. Recent computing advancements have enabled technology companies to collect real-time, granular indicators of fundamentals to sell to investment professionals. These data include consumer transactions and satellite images. The introduction of these data increases price informativeness through decreased information acquisition costs, particularly in firms in which sophisticated investors have higher incentives to uncover information. I document two effects on managers. First, managers reduce their opportunistic trading. Second, investment efficiency increases, consistent with price informativeness improving managers’ incentives to invest and divest efficiently.

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• Zhou, G. (1994). Analytical GMM Tests: Asset Pricing with Time-Varying Risk Premiums. Review of Financial Studies, 7, 687–709.

  We propose alternative generalized method of moments (GMM) tests that are analytically solvable in many econometric models, yielding in particular analytical GMM tests for asset pricing models with time-varying risk premiums. We also provide simulation evidence showing that the proposed tests have good finite sample properties and that their asymptotic distribution is reliable for the sample size commonly used. We apply our tests to study the number of latent factors in the predictable variations of the returns on portfolios grouped by industries. Using data from October 1941 to September 1986 and two sets of instrumental variables, we find that the tests reject a one-factor model but not a two-factor one.

• Zhou, C. (2001). An Analysis of Default Correlations and Multiple Defaults. Review of Financial Studies, 14, 555–576.

  Evaluating default correlations or the probabilities of default by more than one firm is an important task in credit analysis, derivatives pricing, and risk management. However, default correlations cannot be measured directly, multiple-default modeling is technically difficult, and most existing credit models cannot be applied to analyze multiple defaults. This article develops a first-passage-time model, providing an analytical formula for calculating default correlations that is easily implemented and conveniently used for a variety of financial applications. The model also provides a theoretical justification for several empirical regularities in the credit risk literature.

• Zhang, B. Y., Zhou, H., & Zhu, H. (2009). Explaining Credit Default Swap Spreads with the Equity Volatility and Jump Risks of Individual Firms. Review of Financial Studies, 22, 5099–5131.

  This paper attempts to explain the credit default swap (CDS) premium, using a novel approach to identify the volatility and jump risks of individual firms from high-frequency equity prices. Our empirical results suggest that the volatility risk alone predicts 48% of the variation in CDS spread levels, whereas the jump risk alone forecasts 19%. After controlling for credit ratings, macroeconomic conditions, and firms' balance sheet information, we can explain 73% of the total variation. We calibrate a Merton-type structural model with stochastic volatility and jumps, which can help to match credit spreads after controlling for the historical default rates. Simulation evidence suggests that the high-frequency-based volatility measures can help to explain the credit spreads, above and beyond what is already captured by the true leverage ratio.

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• Zevelev, A. A. (2021). Does Collateral Value Affect Asset Prices? Evidence from a Natural Experiment in Texas. Review of Financial Studies, 34, 4373–4411.

  Does the ability to pledge an asset as collateral, after purchase, affect its price? This paper identifies the impact of collateral service flows on house prices, exploiting a plausibly exogenous constitutional amendment in Texas that legalized home equity loans in 1998. The law change increased Texas house prices 4 %; this is price-based evidence that households are credit-constrained and value home equity loans to facilitate consumption smoothing. Prices rose more in locations with inelastic supply, higher prelaw house prices, higher income, and lower unemployment. These estimates reveal that richer households value the option to pledge their home as collateral more strongly.

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