The core of the proposed scientific activities of HiDEA consists of cutting edge research in econometrics and financial economics. Uncovering the new dynamic properties of market prices (staleness, flash crashes, commonalities) establishes a significant advancement in the state-of-art. Regarding drift bursts, they seem to be particularly relevant since the occurrence of flash crashes is increasing in very recent times, signaling a change in market microstructure which needs to be fully understood. The tools studied in this project could help in anticipating flash crashes and put control of the markets back in the hands of regulators at relatively low cost. Regarding price staleness, a new stylized fact, overlooked by the literature, can be uncovered by this research agenda, helping in understanding price formation in greater detail, and providing policy makers with quantitative tools which could help in understanding the liquidity of the market. The project will finally help policy makers in taking decisions about the regulation of markets in which a large portion of trades is executed by machines, in particular in deciding whether to leave the role of market making to High Frequency Traders or to restore it back to designated specialists.
Abstract: An insidious form of market inefficiency, by which prices lose their informativeness and wealth is distributed arbitrarily, translates into V-shapes, that is sudden changes of the sign of the price drift. We use this insight to develop a new tool for the detection of reverting drift, the V-statistic. We apply this tool to (i) quantify the extent of this kind of market inefficiency in the U.S. stock market during the Covid-19 pandemic; and (ii) show the harmful consequences of V-shapes on financial stability by estimating the huge loss suffered by Italian taxpayers (0.45B euros) in May 2018, when a transient crash hit the secondary bond market during a Treasury auction.
Abstract: We show that High Frequency Traders (HFTs) are not beneficial to the stock market during flash crashes. They actually consume liquidity when it is most needed, even when they are rewarded by the exchange to provide immediacy. The behavior of HFTs exacerbate the transient price impact, unrelated to fundamentals, typically observed during a flash crash. Slow traders provide liquidity instead of HFTs, taking advantage of the discounted price. We thus uncover a trade-off between the greater liquidity and efficiency provided by HFTs in normal times, and the disruptive consequences of their trading activity during distressed times.
Abstract: During the COVID-19 crisis, the combined effect of ECB communications, concerns on sovereigns’ stability, illiquidity and market expectations led to a flight to quality. This produced a sell-off of peripheral sovereign bonds that drove the repo rates of core and peripheral countries out-of-sync. Two ECB announcements affected the repo market, namely (i) the Press Conference of the ECB Governing Council on March 12, 2020 and (ii) the announcement of a €750 billion Pandemic Emergency Purchase Program (PEPP). These two announcements had heterogeneous effects in the European repo market which we shall investigate.
Abstract: Time series of counts are often characterized by high overdispersion and persistence. These extreme features challenge the existing models. We approach this problem by combining the framework of INAR with a latent Markov structure. We call it HMM-INAR since it belongs to the class of hidden Markov models. We study the probabilistic properties of HMM-INAR model and illustrate conditions for the existence of an ergodic and stationary solution. We show that the HMM-INAR model is identifiable and can be estimated by maximum likelihood via an efficient expectation-maximization (EM) algorithm with steps available in closed form. The HMM-INAR well predicts the distributional and dynamic features of the time series of counts under investigation, i.e. the number of monthly bankruptcies in South Korea, and the number of trades and volume of several NYSE stocks observed at high frequency. Finally, the model proves empirically superior to other INAR specifications.
Abstract: The tick structure of the financial markets entails that price changes observed at very high frequency are discrete. Departing from this empirical evidence we develop a new model to describe the dynamic properties of multivariate time-series of high frequency price changes, including the high probability of observing no variations (price staleness). We assume the existence of two independent latent/hidden Markov processes determining the dynamic properties of the price changes and the excess probability of the occurrence of zeros. We study the probabilistic properties of the model that generates a zero-inflated mixture of Skellam distributions and we develop an EM estimation procedure with closed-form M step. In the empirical application, we study the joint distribution of the price changes of four assets traded on NYSE. Particular focus is dedicated to the precision of the univariate and multivariate density forecasts, to the quality of the predictions of quantities like the volatility and correlations across assets, and to the possibility of disentangling the different sources of zero price variation as generated by absence of news, microstructural frictions or by the offsetting positions taken by the traders.
Abstract: Asset prices are stale. We define a measure of systematic (market-wide) staleness as the percentage of small price adjustments over multiple assets. A notion of idiosyncratic (asset-specific) staleness is also established. For both systematic and idiosyncratic staleness, we provide a limit theory based on joint asymptotics relying on increasingly-frequent observations over a fixed time span and an increasing number of assets. Using systematic and idiosyncratic staleness as moment conditions, we introduce novel structural estimates of market liquidity and funding liquidity based on transaction prices only. The estimates yield revealing information about the dynamics of the two notions of liquidity and their interaction.
Abstract: This paper studies the impact of environmental, social, and governance (ESG) ratings on investors’ preferences and stock prices. We exploit a change in ESG rating methodology that non-linearly shifted ESG ratings for firms as a natural experiment. We show that the ‘pseudo’-changes in the ESG ratings induced by the change in methodology are unrelated to potential fundamental changes in firm’s sustainability. Yet, we find that an exogenous change in a stock’s ESG rating exerts a transitory price pressure and alters the composition of stock ownership. Individual investors are especially sensitive to the ‘pseudo’-changes in the ESG ratings. They (dis)invest in stocks that they misconceive as ESG (down-) upgraded. Short sellers act as arbitrageurs and take the other side of retail investors’ trades. Overall, we find that a one standard deviation quasi-increase in the ESG ratings translates into 1pp drop in stock monthly abnormal return.
Abstract: The centrality of the United States in the global financial system is taken for granted, but its response to recent political and epidemiological events has suggested that China now holds a comparable position. Using minute-by-minute data from 2012 to 2020 on the financial performance of twelve country-specific exchange-traded funds, we construct daily snapshots of the global financial network and analyze them for the centrality and connectedness of each country in our sample. We find evidence that the U.S. was central to the global financial system into 2018, but that the U.S.-China trade war of 2018–2019 diminished its centrality, and the Covid-19 outbreak of 2019–2020 increased the centrality of China. These indicators may be the first signals that the global financial system is moving from a unipolar to a bipolar world.