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

• Li, H., Wells, M. T., & Yu, C. L. (2008). A Bayesian Analysis of Return Dynamics with Lévy Jumps. Review of Financial Studies, 21, 2345–2378.

  We have developed Bayesian Markov chain Monte Carlo (MCMC) methods for inferences of continuous-time models with stochastic volatility and infinite-activity Lévy jumps using discretely sampled data. Simulation studies show that (i) our methods provide accurate joint identification of diffusion, stochastic volatility, and Lévy jumps, and (ii) the affine jump-diffusion (AJD) models fail to adequately approximate the behavior of infinite-activity jumps. In particular, the AJD models fail to capture the "infinitely many" small Lévy jumps, which are too big for Brownian motion to model and too small for compound Poisson process to capture. Empirical studies show that infinite-activity Lévy jumps are essential for modeling the S&P 500 index returns.

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• Heston, S. L., & Nandi, S. (2000). A Closed-Form GARCH Option Valuation Model. Review of Financial Studies, 13, 585–625.

  This paper develops a closed-form option valuation formula for a spot asset whose variance follows a GARCH(p, q) process that can be correlated with the returns of the spot asset. It provides the first readily computed option formula for a random volatility model that can be estimated and implemented solely on the basis of observables. The single lag version of this model contains Heston's (1993) stochastic volatility model as a continuous-time limit. Empirical analysis on S&P500 index options shows that the out-of-sample valuation errors from the single lag version of the GARCH model are substantially lower than the ad hoc Black-Scholes model of Dumas, Fleming and Whaley (1998) that uses a separate implied volatility for each option to fit to the smirk/smile in implied volatilities. The GARCH model remains superior even though the parameters of the GARCH model are held constant and volatility is filtered from the history of asset prices while the ad hoc Black-Scholes model is updated every period. The improvement is largely due to the ability of the GARCH model to simultaneously capture the correlation of volatility, with spot returns and the path dependence in volatility.

• Heston, S. L. (1993). A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options. Review of Financial Studies, 6, 327–343.

  I use a new technique to derive a closed-form solution for the price of a European call option on an asset with stochastic volatility. The model allows arbitrary correlation between volatility and spot-asset returns. I introduce stochastic interest rates and show how to apply the model to bond options and foreign currency options. Simulations show that correlation between volatility and the spot asset's price is important for explaining return skewness and strike-price biases in the Black-Scholes (1973) model. The solution technique is based on characteristic functions and can be applied to other problems.

• Goyal, A., & Welch, I. (2008). A Comprehensive Look at The Empirical Performance of Equity Premium Prediction. Review of Financial Studies, 21, 1455–1508.

  Our article comprehensively reexamines the performance of variables that have been suggested by the academic literature to be good predictors of the equity premium. We find that by and large, these models have predicted poorly both in-sample (IS) and out-of-sample (OOS) for 30 years now; these models seem unstable, as diagnosed by their out-of-sample predictions and other statistics; and these models would not have helped an investor with access only to available information to profitably time the market.

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• Toda, A. A., Van Nieuwerburgh, S., & Walsh, K. J. (2020). A comprehensive look at the empirical performance of equity premium prediction. Review of Financial Studies, 33, 3583–3623.

  We show that in a general equilibrium model with heterogeneity in risk aversion or belief, shifting wealth from an agent who holds comparatively fewer stocks to one who holds more reduces the equity premium. From an empirical view, the rich hold more stocks, so inequality should predict excess stock market returns. Consistent with our theory, we find that when the U.S. top (, 1%) income share rises, subsequent 1-year excess market returns significantly decline. This negative relation is robust to controlling for classic return predictors, predicting out-of-sample, and instrumenting inequality with estate tax rate changes. It also holds in international markets.Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

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• Berk, J. B. (1995). A Critique of Size-Related Anomalies. Review of Financial Studies, 8, 275–286.

  This article argues that the size-related regularities in asset prices should not be regarded as anomalies. Indeed, the opposite result is demonstrated. Namely, a truly anomalous regularity would be if an inverse relation between size and return was not observed. We show theoretically (1) that the size-related regularities should be observed in the economy and (2) why size will in general explain the part of the cross-section of expected returns left unexplained by an incorrectly specified asset pricing model. In light of these results we argue that size-related measures should be used in cross-sectional tests to detect model misspecifications.

• Chua, C. T., Foster, D., Ramaswamy, K., & Stine, R. (2008). A Dynamic Model for the Forward Curve. Review of Financial Studies, 21, 265–310.

  This article develops and estimates a dynamic arbitrage-free model of the current forward curve as the sum of (i) an unconditional component, (ii) a maturity-specific component and (iii) a date-specific component. The model combines features of the Preferred Habitat model, the Expectations Hypothesis (ET) and affine yield curve models; it permits a class of low-parameter, multiple state variable dynamic models for the forward curve. We show how to construct alternative parametric examples of the three components from a sum of exponential functions, verify that the resulting forward curves satisfy the Heath-Jarrow-Morton (HJM) conditions, and derive the risk-neutral dynamics for the purpose of pricing interest rate derivatives. We select a model from alternative affine examples that are fitted to the Fama-Bliss Treasury data over an initial training period and use it to generate out-of-sample forecasts for forward rates and yields. For forecast horizons of 6 months or longer, the forecasts of this model significantly outperform those from common benchmark models.

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• Rosu, I. (2009). A Dynamic Model of the Limit Order Book. Review of Financial Studies, 22, 4601–4641.

  This paper presents a model of an order-driven market where fully strategic, symmetrically informed liquidity traders dynamically choose between limit and market orders, trading off execution price and waiting costs. In equilibrium, the bid and ask prices depend only on the numbers of buy and sell orders in the book. The model has a number of empirical predictions: (i) higher trading activity and higher trading competition cause smaller spreads and lower price impact; (ii) market orders lead to a temporary price impact larger than the permanent price impact, therefore to price overshooting; (iii) buy and sell orders can cluster away from the bid-ask spread, generating a hump-shaped order book; (iv) bid and ask prices display a comovement effect: after, e.g., a sell market order moves the bid price down, the ask price also falls, by a smaller amount, so the bid-ask spread widens; (v) when the order book is full, traders may submit quick, or fleeting, limit orders.

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• Li, L., Li, Y., Macchiavelli, M., & Zhou, X. (2021). A dynamic theory of mutual fund runs and liquidity management. Review of Financial Studies, 34, 5402–5437.

  Liquidity restrictions on investors, like the redemption gates and liquidity fees introduced in the 2016 money market fund (MMF) reform, are meant to improve financial stability. However, we find evidence that such liquidity restrictions exacerbated the run on prime MMFs during the COVID-19 crisis. Our results indicate that gates and fees could generate strategic complementarities among investors in crisis times. Severe outflows from prime MMFs led the Federal Reserve to intervene with the Money Market Mutual Fund Liquidity Facility (MMLF). Using MMLF microdata, we show how the provision of “liquidity of last resort” stabilized prime funds.

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• Hirshleifer, D., & Jiang, D. (2010). A Financing-Based Misvaluation Factor and the Cross-Section of Expected Returns. Review of Financial Studies, 23, 3401–3436.

  Behavioral theories suggest that investor misperceptions and market mispricing will be correlated across firms. We use equity and debt financing to identify common misvaluation across firms. A zero-investment portfolio (UMO, undervalued minus overvalued) built from repurchase and issue firms captures comovement in returns beyond that in some standard multifactor models, and substantially improves the Sharpe ratio of the tangency portfolio. Loadings on UMO incrementally predict the cross-section of returns on both portfolios and individual stocks, even among firms not recently involved in external financing activities. Further evidence suggests that UMO loadings proxy for the common component of a stock's misvaluation.

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