[On August 12-13, 2005, the department of finance at the Kelley School of Business, Indiana University, collaborated with the "Review of Financial Studies" to host a conference titled "The Causes and Consequences of Recent Financial Market Bubbles." This article begins with our overview of the themes and findings of the conference, and it ends with the questions that the literature has yet to answer.]

A four-factor model with two "mispricing" factors, in addition to market and size factors, accommodates a large set of anomalies better than notable four- and five-factor alternative models. Moreover, our size factor reveals a small-firm premium nearly twice usual estimates. The mispricing factors aggregate information across 11 prominent anomalies by averaging rankings within two clusters exhibiting the greatest return co-movement. Investor sentiment predicts the mispricing factors, especially their short legs, consistent with a mispricing interpretation and the asymmetry in ease of buying versus shorting. A three-factor model with a single mispricing factor also performs well, especially in Bayesian model comparisons.

Abstract Decentralized exchanges (DEXs) allow traders to express their willingness to pay for quick execution through a public priority fee bidding mechanism. We provide evidence that high-fee DEX trades are more informative and contribute more to price discovery. Using address-level blockchain transaction data, we show that informed traders persistently bid higher fees to secure early execution, revealing a strong willingness to pay for execution priority. Further, analysis of Ethereum mempool data demonstrates that informed traders employ a “jump bidding” strategy, placing high initial bids to deter potential competitors.

The paper proposes a self-exciting asset pricing model that takes into account co-jumps between prices and volatility and self-exciting jump clustering. We employ a Bayesian learning approach to implement real-time sequential analysis. We find evidence of self-exciting jump clustering since the 1987 market crash, and its importance becomes more obvious at the onset of the 2008 global financial crisis. We also find that learning affects the tail behaviors of the return distributions and has important implications for risk management, volatility forecasting, and option pricing.

[A new methodology is proposed to estimate theoretical prices of financial contingent claims whose values are dependent on some other underlying financial assets. In the literature, the preferred choice of estimator is usually maximum likelihood (ML). ML has strong asymptotic justification but is not necessarily the best method in finite samples. This paper proposes a simulation-based method. When it is used in connection with ML, it can improve the finite-sample performance of the ML estimator while maintaining its good asymptotic properties. The method is implemented and evaluated here in the Black-Scholes option pricing model and in the Vasicek bond and bond option pricing model. It is especially favored when the bias in ML is large due to strong persistence in the data or strong nonlinearity in pricing functions. Monte Carlo studies show that the proposed procedures achieve bias reductions over ML estimation in pricing contingent claims when ML is biased. The bias reductions are sometimes accompanied by reductions in variance. Empirical applications to U. S. Treasury bills highlight the differences between the bond prices implied by the simulation-based approach and those delivered by ML. Some consequences for the statistical testing of contingent-claim pricing models are discussed.]

Prices of interest rate derivative securities depend crucially on the mean reversion parameters of the underlying diffusions. These parameters are subject to estimation bias when standard methods are used. The estimation bias can be substantial even in very large samples and much more serious than the discretization bias, and it translates into a bias in pricing bond options and other derivative securities that is important in practical work. This article proposes a very general and computationally inexpensive method of bias reduction that is based on Quenouille's (1956; Biometrika, 43, 353-360) jackknife. We show how the method can be applied directly to the options price itself as well as the coefficients in the models. We investigate its performance in a Monte Carlo study. Empirical applications to U.S. dollar swap rates highlight the differences between bond and option prices implied by the jackknife procedure and those implied by the standard approach. These differences are large and suggest that bias reduction in pricing options is important in practical applications.

[We conduct an analysis of the risk and return characteristics of a number of widely used fixed-income arbitrage strategies. We find that the strategies requiring more "intellectual capital" to implement tend to produce significant alphas after controlling for bond and equity market risk factors. These positive alphas remain significant even after taking into account typical hedge fund fees. In contrast with other hedge fund strategies, many of the fixed-income arbitrage strategies produce positively skewed returns. These results suggest that there may be more economic substance to fixedincome arbitrage than simply "picking up nickels in front of a steamroller."]

[We use a cost of carry model with nonzero transaction costs to motivate estimation of a nonlinear dynamic relationship between the S&P 500 futures and cash indexes. Discontinuous arbitrage suggests that a threshold error correction mechanism may characterize many aspects of the relationship between the futures and cash indexes. We use minute-by-minute data on the S&P 500 futures and cash indexes. The results indicate that nonlinear dynamics are important and related to arbitrage, and suggest that arbitrage is associated with more rapid convergence of the basis to the cost of carry than would be indicated by a linear model.]

We introduce uncertainty into Holmstrom and Milgrom (1987) to study optimal long-term contracting with learning. In a dynamic relationship, the agent's shirking not only reduces current performance, but also increases the agent's information rent due to the persistent belief manipulation effect. We characterize the optimal contract using the dynamic programming technique in which information rent is the unique state variable. In the optimal contract, the optimal effort is front-loaded and stochastically decreases over time. Furthermore, the optimal contract exhibits an option-like feature in that incentives increase after good performance. Implications about managerial incentives and asset management compensations are discussed.

We hypothesize that cognitive limitation may be manifested in a disproportionately large volume of limit orders submitted at round-number prices if investors use these numbers as cognitive shortcuts. Using detailed limit order data in the Taiwan Futures Exchange, we find that investors with lower cognitive abilities, defined as higher limit order submission ratios at round numbers, suffer greater losses in their round-numbered and non-round-numbered limit orders, market orders, and round-trip trades. The positive correlation between cognitive ability and investment performance is monotonic and robust across futures and options markets. In addition, past trading experience helps mitigate cognitive limitation.