Julio A. Crego
I am an Assistant Professor of Finance at Tilburg University. I received my PhD from CEMFI, Madrid in 2017.
My research interests cover topcis in Market microstructure and Econometrics.
jacrego (at) uvt (dot) nl
Finance Department - Office I 502
5037 AB Tilburg - The Netherlands
The arrival of a public signal worsens the adverse selection problem if informed investors are risk-averse. To illustrate the mechanism, I propose a dynamic model with a public signal and endogenous participation. In this set-up, the public signal reduces uncertainty which boosts informed investors' participation leading to a more toxic order flow. I confirm the model's empirical predictions by estimating the effect of the publication of the weekly change in oil inventories on liquidity via a difference-in-difference strategy. Precisely, the mean bid-ask spread doubles immediately after the release and volume increases by 32 percent regardless of the report's content.
We reconcile opposing evidence found in previous literature about the tail neutrality of market-neutral hedge funds (MNHFs) using US data from 2003 to 2013. In particular, we estimate a regime-switching copula model to show the existence of a common macroeconomic regime that affects the distributions of both MNHF returns and the market index; this, in turn, creates a non-linear dependence, which can be confounded with tail dependence. Moreover, we provide evidence of positive (negative) linear correlation between the market index and MNHFs during bull (bear) periods that coincide with the US business cycle. We show with simulated data from our model that sample tail-based tests do not reject the tail dependence hypothesis, even if the tail dependence parameter is set to zero.
Since September 2008 regulators from different countries, motivated by suspicions regarding an increase in investors aggressiveness, have implemented several temporary short selling restrictions. In this paper, I study the effect of such policies in the context of the 2012 Spanish short selling ban. The results of this paper highlight an important policy trade-off: on the one hand, I provide evidence that, in line with regulator beliefs, investor aggressiveness is extremely high prior to the ban and, it reverts just after the ban implementation. On the other hand, using a novel identification strategy, I find that this policy increases the bid-ask spread. The causal interpretation of these results is obtained under the assumption that the exact time of the implementation is random. Specifically, using intraday data, I apply a methodology inspired in a regression discontinuity design in the time dimension. While the results obtained using this methodology are qualitatively similar to the ones found in previous literature, the quantitative effect is much smaller.
Health dynamics and its associated medical and care costs have been identified by the macro literature as a major concern of the elderly. Due to its multidimensionality, however, a difficult task faced by researchers is to summarize health parsimoniously into a single state variable. We propose a panel Markov switching model to identify patterns of health heterogeneity where individuals can move across health groups as they age. To estimate the model, we use Markov chain Monte Carlo techniques to exploit information from both the cross-sectional and time series dimensions. We identify health groups for individuals in the Health and Retirement Survey for the US. Results show that there exists four clearly differentiated groups depending on individual's physical and mental disabilities. Furthermore, we show that health groups outperform other measures of health commonly used in the literature at explaining the variance in the use of nursing homes, home health care, out of pocket medical expenses and predicted mortality.
WORK IN PROGRESS
Competition and Liquidity
In the last decade hundreds of millions of dollars have been invested in reducing the communication time between the CME and NYSE. While some investors argue that the fastest the markets, the more efficient is the price; others claim that differences in speed erode liquidity through an increase in asymmetric information. I estimate the causal effect of one of these technologies, Microwave beams, on liquidity. I identify the causal effect using rain as an exogenous instrument under two mild assumptions: rain along the path between Chicago and New York does not affect liquidity and, microwave signals are attenuated by rain.
This paper studies learning in the stock market. Our contribution is to propose a model to illustrate the endogenous timing decision on trading, taking into account the incentive of learning from others about the fundamental value. The model is similar to Easley and O'Hara (1992), except that we introduce less-informed traders whose private information is inferior to fully-informed traders, but superior to that of random noise traders, and a zero-profit market maker. We also allow both types of informed traders to optimize timing of trading. We show that fully-informed traders act as early birds because it is optimal for them to buy or sell at the earliest possible time; meanwhile, less-informed traders could be better off as second mice by delaying transactions to learn from previous trades. The greater information asymmetry between the less-informed traders and the market maker, the larger profits the former could make even though the latter is learning from all trades.