Diego Amaya

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.

We decompose traditional betas into semibetas based on the signed covariation between the returns of individual stocks in an international market and the returns of three risk factors: local, global, and foreign exchange. Using high-frequency data, we empirically assess stock return co-movements with these three risk factors and find novel relationships between these factors and future returns. Our analysis shows that only semibetas derived from negative risk factor and stock return downturns command significant risk premia. Global downside risk is negatively priced in the international market and local downside risk is positively priced.

We examine the impact of pre-initial public offering (IPO) media coverage on the decision to withdraw an IPO. Using a sample of 2592 U.S. completed and withdrawn IPOs between January 1998 and December 2011, we find that media coverage and media tone are significantly negatively related to the probability of withdrawal. We also find that even when media coverage does not supply new information during the IPO process, it plays a significant role in disseminating investors’ beliefs about firm valuations, which relate to the final outcome of this process. We examine the price range changes during the filing process as a proxy for changes in investors’ initial demand and find that downward price changes are related to the intensity of media coverage.

We propose the option realized variance as an observable variable to summarize the information from high-frequency option data. This variable aggregates intraday option returns from midquote prices to compute an option’s total variability for a given day, providing additional information about the jump activity in the data generating process. Using the S&P 500 index time series and options data, this paper documents the performance of this realized measure in predicting the index realized variance as well as the equity and variance risk premiums. We estimate an option pricing model and analyze its parameter estimates. Our results show that excluding high-frequency option information produces significant differences in variance jump parameters, estimated risk premiums, and option pricing errors.

In this paper, we adopt a flexible filtering procedure to extract information from high-frequency data. Specifically, we provide a parsimonious framework to integrate realized measures from high-frequency index and derivative prices. In a simulation study, we document the incremental information offered by realized measures and show that even though high-frequency index prices help identify spot variance and jump price dynamics, it is the addition of high-frequency option prices that enables variance jumps to be identified. A series of empirical studies based on the S&P 500 index and options show that estimation precision improves with the addition of information from intraday option prices.

IPOs of small early-stage companies have largely declined in the last few decades. Governments and exchanges have responded with new regulations to encourage access for small firms to public markets. Critics caution, however, that lower standards for going public may be worse for investors. In this paper, we document one exchange’s approach to encourage small IPOs: founders establish shell companies through which to scout and promote funding of early-stage companies. We find that founders earn compensatory returns for their search role, and that the average long-term performance of these companies is similar to that of small conventional IPOs, underscoring that conventional IPOs may fail to identify or screen companies of similar quality asymmetrically. Consistent with prior literature on small IPOs, the long-term post-IPO performance of both the conventional and alternative funding processes are highly right-skewed and poor, on average, but not statistically different.

The survivorship bias in credit risk modeling is the bias that results in parameter estimates when the survival of a company is ignored. We study the statistical properties of the maximum likelihood estimator (MLE) accounting for survivorship bias for models based on the first-passage of the geometric Brownian motion. We find that if we neglect the survivorship bias, then the drift has a positive bias that may not disappear asymptotically. We show that correcting the survivorship bias by conditioning on survival in the likelihood function underestimates the drift. Therefore, we propose a bias correction method for non-iid samples that is first-order unbiased and second-order efficient. The economic impact of neglecting or miscorrecting for the survivorship bias is studied empirically based on a sample of more than 13,000 companies over the period 1980 through 2016 inclusive. Our results point to the important risk of misclassifying a company as solvent or insolvent due to biases in the estimates.

This paper proposes a method to compute ex-ante trading costs at the intraday level from limit order books. Using nearly 500 of the largest traded companies in the NYSE ArcaBook, we show that these costs have nontrivial intraday dynamics, are negatively related to volume and positively related to volatility. When ex-ante trading costs are incorporated into price impact specifications, the results show that this measure provides relevant information about price changes of the market at a high frequency level. Our evidence suggests that ex-ante trading costs constitute a new source of information for the study of intraday liquidity.

We use intraday data to compute weekly realized moments for equity returns and study their time-series and cross-sectional properties. Buying stocks in the lowest realized skewness decile and selling stocks in the highest realized skewness decile generates an average return of 19 basis points the following week with a t-statistic of 3.70. This result is robust across a wide variety of implementations and is not captured by the Fama-French and Carhart factors. The relation between realized kurtosis and next week’s stock returns is positive but not always significant. We do not find a strong relation between realized volatility and next week’s stock returns.

This article develops a dynamic risk management model to determine a firm’s optimal risk management strategy. This strategy has two elements. First, for low-leverage values, the firm fully hedges its operating cash flow exposure, due to the convexity of its cost of capital. When leverage exceeds a very high threshold, the firm gambles for resurrection and stops hedging. Second, the firm manages its capital structure through dividend distributions and investment. When leverage is low, the firm replaces depreciated assets, fully invests in opportunities if they arise, and distributes dividends, all of these together to achieve its optimal capital structure. As leverage increases, the firm stops paying dividends, while fully investing. After a certain leverage, the firm also reduces investment until it stops investing completely. The model predictions are consistent with empirical observations.

This article examines the communication of the European Central Bank (ECB) at press conferences and its impact on financial markets. We compare consecutive central banker conference speeches and document that the similitude of these speeches has been increasing over time. We find evidence that the similarity of ECB communication has helped stock markets learn from ECB monetary policy.

A previously unrecognized channel through which index options trading impacts the underlying market is that delta hedges of expiring cash-settled index options are unwound at expiration. This contrasts with physically-settled options, where the delta hedge positions in the underlier are used to settle the options position. We use proprietary data to determine market maker positions in expiring S&P 500 index options, and show that hedge unwinding trades have important impacts on index futures returns and volatility during the 30 minutes before options expiration. We also study the impact of options market maker hedge rebalancing trades on market volatility.

Trading in SPX 0DTE options spikes at fixed times. Using OPRA transaction records from May 2022 through January 2026, we show that the bursts begin within seconds of each half-hour mark, finish within a minute, and grow each year. They consist of a small set of defined-risk strategies whose strikes, widths, and credits resemble templates of retail automation platforms, and can be identified from those characteristics alone, without the clock. At spike times quoted spreads narrow while effective spreads widen: predictability attracts liquidity, and simultaneity consumes it. The order flow is synchronized though the algorithms never learn, communicate, or interact.

Mobile brokerage platforms such as Robinhood have attracted millions of new investors by reducing transaction costs and offering simple, easy-to-use interfaces. Beyond lowering trading frictions, however, the design of these platforms can shape investor behavior through automated rules and risk-management protocols. We study one such mechanism: the automatic liquidation of option positions by brokerage platforms when customers lack the cash or shares required to exercise contracts or accept assignment at expiration. Using transaction-level option data, we identify bursts of trading generated by Robinhood’s forced liquidations shortly before the market close.

The large volume of trading in 0DTE S&P 500 index options suggests that options market makers (OMMs) have large positions in these options. Because short-dated options have large gammas, OMMs’ hedge rebalancing trades might be large enough to impact the index. We estimate the maximum impact of OMM gamma on index volatility using proprietary trade data to determine the aggregate position of OMMs, and their gamma.

Recent research claims that retail options traders experience losses on average. Notably, Bryzgalova, Pavlova and Sikorskaya (2023) (BPS) claim trades executed in single-leg improvement mechanisms (SLIMs), their proxy for retail trades, suffer losses. The BPS performance estimates are sensitive to their estimates of trade direction and use of quotes prior to expiration, rather than expiration values, to compute performance. Using expiration values and Cboe data that include trade directions, we find that Cboe customer SLIM trades are on average profitable. The similarity between trades on the Cboe and non-Cboe exchanges suggests that SLIM trades on non-Cboe exchanges also are profitable.

How do informed investors trade in the option markets? Using the S&P 500 index options market, we analyze how price discovery varies across option contract types, moneyness levels, and maturities. Our findings suggest that informed traders predominantly favor put, short-term, and at-the-money options. These preferences are not fixed; they adapt according to the current risk environment and in response to index return and volatility shocks. Additionally, we identify a structural shift that aligns with the growing popularity of weekly options. Our analysis reveals that informed traders are increasingly using weekly options to convey market information and have also intensified their preference for put options, short-term options, and out-of-the-money options towards the end of our sample period.