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Jump Clustering, Information Flows and Stock Price Efficiency
Abstract
We study the clustering behaviour of stock return jumps modelled by a self/cross-exciting process embedded in a stochastic volatility model. Based on the model estimates, we propose a novel measurement of stock price efficiency characterised by the extent of jump clustering that stock returns exhibit. This measurement demonstrates the capability of capturing the speed at which stock prices assimilate new information, especially at the firm-specific level. Furthermore, we assess the predictability of self-exciting (clustered) jumps in stock returns. We employ a particle filter to sample latent states in the out-of-sample period and perform one-step-ahead probabilistic forecasting on upcoming jumps. We introduce a new statistic derived from predicted probabilities of positive and negative jumps, which has been shown to be effective in return predictions.
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We examine the clustering behaviour of price and variance jumps using high-frequency data, modelled as a marked Hawkes process embedded in a bivariate jump-diffusion model with intraday periodic effects. We find that the jumps of both individual stocks and a broad index exhibit self-exciting behaviour. The three dimensions of the model, namely positive price jumps, negative price jumps and variance jumps, impact one another in an asymmetric fashion. We estimate model parameters using Bayesian inference by Markov Chain Monte Carlo, and find that the inclusion of the jump parameters improves the fit of the model. When we quantify the jump intensity and study the characteristics of jump clusters, we find that in high-frequency settings, jump clustering can last between 2.5 to 6 hours on average. We also find that the marked Hawkes process generally outperforms other models in terms of reproducing two cluster-related characteristics found in the actual data.
We develop a nonparametric test for the temporal dependence of jump occurrences in the population. The test is consistent against all pairwise serial dependence, and is robust to the jump activity level and the choice of sampling scheme. We establish asymptotic normality and local power property for a rich set of local alternatives, including both self-exciting and/or self-inhibitory jumps. Simulation study confirms the robustness of the test and reveals its competitive size and power performance over existing tests. In an empirical study on high-frequency stock returns, our procedure uncovers a wide array of autocorrelation profiles of jump occurrences for different stocks in different time periods.
Material news events can be potentially important sources of jumps in stock returns. We collect 21 million news articles associated with more than 9,000 publicly-traded companies and use textual analyses to derive measures to summarize the news. We find that stock return jumps (including time-variation in jump-size distributions and jump intensity) are significantly related to news flow frequency and content and those effects increase substantially over the last few decades. The sensitivity of jump probability to news is stronger for firms with higher media visibility, analyst coverage, and institutional ownership. This sensitivity also varies across different news categories.
We propose a methodology to measure the parameter estimation risk and model specification risk of pricing models, as well as model selection risk of model classes, based on realized payoffs, for products in the over-the-counter market. Lévy jump models and affine jump-diffusion models are applied in estimating the fair variance strikes of variance swaps and forward starting option prices. Our results show that both parameter estimation risk and model specification risk are significant for variance swaps, while model specification risk is dominant when pricing forward starting options. We also find that the size of the model selection risk is substantial for both products.
A potentially important source of jumps in stock returns can be material news events. We collect 21 million news articles associated with more than 9000 publicly-traded companies and use textual analyses to derive measures summarizing those news. We find that the measures of news flow content are significantly related to nonparametric measures of jumps; and that the explanatory power of news flow on jumps has increased in the last few decades. Moreover, by modelling the observable news process explicitly and jointly with a latent jump process, we find news are important drivers of the jumps in the estimated model.
Keywords: Jumps, news flow, textual analysis
JEL Classification: G1
Forecasting the risk of extreme losses is an important issue in the management of financial risk and has attracted a great deal of research attention. However, little attention has been paid to extreme losses in a higher frequency intraday setting. This paper proposes a novel marked point process model to capture extreme risk in intraday returns, taking into account a range of trading activity and liquidity measures. A novel approach is proposed for defining the threshold upon which extreme events are identified taking into account the diurnal patterns in intraday trading activity. It is found that models including covariates, mainly relating to trading intensity and spreads offer the best in-sample fit, and prediction of extreme risk, in particular at higher quantiles.
S ummary
The point spectral matrix is obtained for a class of mutually exciting point processes. The solution makes use of methods similar to those used in solving Wiener–Hopf integral equations.
This article presents a study of how stock price efficiency and return distributions are affected by short-sale constraints. The study is based on a global dataset, from 2005 to 2008, that includes more than 12,600 stocks from 26 countries. We present two main findings. First, lending supply has a significant impact on efficiency. Stocks with higher short-sale constraints, measured as low lending supply, have lower price efficiency. Second, relaxing short-sales constraints is not associated with an increase in either price instability or the occurrence of extreme negative returns. The Author 2011. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.
This paper shows that over time, expected market illiquidity positively affects ex ante stock excess return, suggesting that expected stock excess return partly represents an illiquidity premium. This complements the cross-sectional positive return–illiquidity relationship. Also, stock returns are negatively related over time to contemporaneous unexpected illiquidity. The illiquidity measure here is the average across stocks of the daily ratio of absolute stock return to dollar volume, which is easily obtained from daily stock data for long time series in most stock markets. Illiquidity affects more strongly small firm stocks, thus explaining time series variations in their premiums over time.
This paper introduces new econometric tests to identify stochastic intensity jumps in high-frequency data. Our approach exploits the behavior of a time-varying stochastic intensity and allows us to assess how intensely stock market reacts to news. We describe the asymptotic properties of our test statistics, derive the associated central limit theorem and show in simulations that the tests have good size and reasonable power in finite-sample cases. Implementing our testing procedures on the S&P 500 exchange-traded fund data, we find strong evidence for the presence of intensity jumps surrounding the scheduled Federal Open Market Committee (FOMC) policy announcements. Intensity jumps occur very frequently, trigger sharp increases in realized volatility and arrive when differences in opinion among market participants are large at times of FOMC press releases. Unlike intensity jumps, volatility jumps fail to explain the variation in news-induced realized volatility.
Motivated by the recent availability of extensive electronic news databases and advent of new empirical methods, there has been renewed interest in investigating the impact of financial news on market outcomes for individual stocks. We develop the information processing hypothesis of return volatility to investigate the relation between firm-specific news and volatility. We propose a novel dynamic econometric specification and test it using time series regressions employing a machine learning model selection procedure. Our empirical results are based on a comprehensive dataset comprised of more than 3 million news items for a sample of 28 large U.S. companies. Our proposed econometric specification for firm-specific return volatility is a simple mixture model with two components: public information and private processing of public information. The public information processing component is defined by the contemporaneous relation with public information and volatility, while the private processing of public information component is specified as a general autoregressive process corresponding to the sequential price discovery mechanism of investors as additional information, previously not publicly available, is generated and incorporated into prices. Our results show that changes in return volatility are related to public information arrival and that including indicators of public information arrival explains on average 26% (9–65%) of changes in firm-specific return volatility.
Macroeconomic news announcements are elaborate and multidimensional. We consider a framework in which jumps in asset prices around announcements reflect both the response to observed surprises in headline numbers and to latent factors, reflecting other news in the release. Non-headline news, for which there are no expectations surveys, is unobservable to the econometrician but nonetheless elicits a market response. We estimate the model by the Kalman filter, which efficiently combines OLS and heteroskedasticity-based event study estimators in one step. With the inclusion of a single latent surprise factor, essentially all yield curve variance in event windows are explained by news. (JEL C51, E43, E52, G12, G14)
The link between news and investor decision making is widely discussed in the literature. Utilising unique U.S. firm-level news data between 1979 and 2016, we document a cross-sectional difference in the speed of the diffusion of information contained in news. We distinguish news articles as being either slowly or quickly incorporated into contemporaneous stock prices. The return spread between stocks classified according to these two types of news yields a statistically significant profit of 139 basis points per month. This abnormal return cannot be explained by other well-known risk factors and is robust when allowing for trading costs. Overall, our research refines the role of news regarding information dissemination in the financial markets.
Investor preferences for holding speculative assets are likely to be more pronounced ahead of firms’ earnings announcements, probably because of lower inventory costs and immediate payoffs or because of enhanced investor attention. We show that the demand for lottery-like stocks is stronger ahead of earnings announcements, leading to a price run-up for these stocks. In sharp contrast to the standard underperformance of lottery-like stocks, lottery-like stocks outperform non-lottery stocks by about 52 basis points in the 5-day window ahead of earnings announcements. However, this return spread is reversed by 80 basis points in the 5-day window after the announcements. Moreover, this inverted-V-shaped pattern on cumulative return spreads is more pronounced among firms with a greater retail order imbalance, among firms with low institutional ownership, and in regions with a stronger gambling propensity, and it is also robust after controlling for past 12-month returns and various proxies for investor attention.
The recent literature provides conflicting empirical evidence about the pricing of idiosyncratic risk. This paper sheds new light on the matter by exploiting the richness of option data. First, we find that idiosyncratic risk explains 28% of the variation in the risk premium on a stock. Second, we show that the contribution of idiosyncratic risk to the equity premium arises exclusively from jump risk. Third, we document that the commonality in idiosyncratic tail risk is much stronger than that in total idiosyncratic risk documented in the literature. Tail risk thus plays a central role in the pricing of idiosyncratic risk.
Received May 15, 2017; editorial decision September 12, 2018 by Editor Stijn Van Nieuwerburgh. Authors have furnished code and an Internet Appendix, which are available on the Oxford University PressWeb site next to the link to the final published paper online.
We examine stock index futures and Treasury futures around the release time of 30 U.S. macroeconomic announcements. Nine of the 20 announcements that move markets show evidence of substantial informed trading before the official release time. Prices begin to move in the “correct” direction approximately 30 minutes before the release time. The preannouncement price drift accounts on average for approximately 40% of the total price adjustment. This implies that some traders have private information about macroeconomic fundamentals. Preannouncement drift might originate from a combination of information leakage and superior forecasting that incorporates proprietary data.
We consider a univariate semimartingale model for (the logarithm of) an asset price, containing jumps having possibly infinite activity. The nonparametric threshold estimator IV̂n of the integrated variance IV≔∫0Tσs2ds proposed in Mancini (2009) is constructed using observations on a discrete time grid, and precisely it sums up the squared increments of the process when they are below a threshold, which depends on the observation time step and, sometimes, model parameters or latent variables, that need to be estimated. All the threshold functions satisfying given conditions allow asymptotically consistent estimates of IV, however the finite sample properties of IV̂n can depend on the specific choice of the threshold. We aim here at optimally selecting the threshold by minimizing either the estimation mean squared error (MSE) or the conditional mean squared error (cMSE). The last criterion allows to reach a threshold which is optimal not in mean but for the specific volatility and jumps paths at hand.
A parsimonious characterization of the optimum is established, which turns out to be asymptotically proportional to the Lévy’s modulus of continuity of the underlying Brownian motion. Moreover, minimizing the cMSE enables us to propose a novel implementation scheme for approximating the optimal threshold. Monte Carlo simulations illustrate the superior performance of the proposed method.
This paper investigates how jump risks are priced in currency markets. We find that currencies whose changes are more sensitive to negative market jumps provide significantly higher expected returns. The positive risk premium constitutes compensation for the extreme losses during periods of market turmoil. Using the empirical findings, we propose a jump modified carry trade strategy, which has approximately two-percentage-point (per annum) higher returns than the regular carry trade strategy. These findings result from the fact that negative jump betas are significantly related to the riskiness of currencies and business conditions.
We investigate the relationship between uncertainty about monetary policy and its transmission mechanism, and economic fluctuations. We propose a new term structure model where the second moments of macroeconomic variables and yields can have a first-order effect on their dynamics. The data favor a model with two unspanned volatility factors that capture uncertainty about monetary policy and the term premium. Uncertainty contributes negatively to economic activity. Two dimensions of uncertainty react in opposite directions to a shock to the real economy, and the response of inflation to uncertainty shocks varies across different historical episodes.
We examine stock index and Treasury futures markets around releases of U.S. macroeconomic announcements. Seven out of 21 market-moving announcements show evidence of substantial informed trading before the official release time. Prices begin to move in the \correct" direction about 30 minutes before the release time. The pre-announcement price drift accounts on average for about half of the total price adjustment. These results imply that some traders have private information about macroeconomic fundamentals. The evidence suggests that the pre-announcement drift likely comes from a combination of information leakage and superior forecasting based on proprietary data collection and reprocessing of public information. JEL Classification: E44, G14, G15
This paper examines the impact of stock liquidity on firm bankruptcy risk. Using the Securities and Exchange Commission decimalization regulation as a shock to stock liquidity, we establish that enhanced liquidity decreases default risk. Stocks with the highest default risk experience the largest improvements. We find two mechanisms through which stock liquidity reduces firm default risk: improving stock price informational efficiency and facilitating corporate governance by blockholders. Of the two mechanisms, the informational efficiency channel has higher explanatory power than the corporate governance channel.
We examine the impact of actual share repurchases on stock prices using several measures of price efficiency and manually collected data on U.S. repurchases. We find that share repurchases make prices more efficient and reduce idiosyncratic risk. Further analyses reveal that the effects are primarily driven by repurchases in down markets. We conclude that share repurchases help to maintain accurate stock prices by providing price support at fundamental values. We find no evidence that managers use share repurchases to manipulate stock prices when selling their equity holdings or exercising stock options.
Using jumps in stock prices as a proxy for large information shocks, we provide evidence consistent with short-term underreaction in the US equity market. Strategies long (short) stocks with positive (negative) lagged jump returns earn significantly positive returns over the next one- to three-month horizons. The results based on intraday jumps, especially overnight jumps, provide further evidence consistent with underreaction. The underreaction is robust to controlling for other firm characteristics, extends stock return momentum over intermediate to short horizons, and captures market underreaction to information shocks beyond earnings surprises. We further show that limited investor attention contributes to short-term underreaction.
We examine the impact of actual share repurchases on stock prices using several measures of price efficiency and manually collected data on U.S. repurchases. We find that share repurchases make prices more efficient and reduce idiosyncratic risk. Further analyses reveal that the effects are primarily driven by repurchases in down markets. We conclude that share repurchases help to maintain accurate stock prices by providing price support at fundamental values. We find no evidence that managers use share repurchases to manipulate stock prices when selling their equity holdings or exercising stock options.
Received June 1, 2015; accepted June 1, 2016, by Editor David Denis.
Since 2012, all European Union countries have required disclosure of large short positions. This reduces short interest, bid-ask spreads, and the informativeness of prices. After specific disclosures, short-run abnormal returns are insignificantly negative, but 90-day cumulative abnormal returns are a statistically significant 5.23%. We find disclosures are likely to be followed by other disclosures, especially when the initial discloser is large or centrally located. However, there is no subsequent increase in short interest, and prices do not subsequently reverse. These results indicate that large short sellers are well informed, and that disclosures are not being used to coordinate manipulative attacks.
Received June 30, 2014; accepted June 24, 2016 by Editor Itay Goldstein.
Applying tests for jumps to financial data sets can lead to an important number of spurious detections. Bursts of volatility are often incorrectly identified as jumps when the sampling is too sparse. At a higher frequency, methods robust to microstructure noise are required. We argue that whatever the jump detection test and the sampling frequency, a large number of spurious detections remain because of multiple testing issues. We propose a formal treatment based on an explicit thresholding on available test statistics. We prove that our method eliminates asymptotically all remaining spurious detections. In Dow Jones stocks between 2006 and 2008, spurious detections can represent up to 90% of the jumps detected initially. For the stocks considered, jumps are rare events, they do not cluster in time, and no cojump affects all stocks simultaneously, suggesting jump risk is diversifiable. We relate the remaining jumps to macroeconomic news, prescheduled company-specific announcements, and stories from news agencies that include a variety of unscheduled and uncategorized events. The vast majority of news does not cause jumps but may generate a market reaction in the form of bursts of volatility.
We exploit a unique natural experiment—recent restrictions of dark trading in Canada and Australia—and proprietary trade-level data to analyze the effects of dark trading. Disaggregating two types of dark trading, we find that dark limit order markets are beneficial to market quality, reducing quoted, effective, and realized spreads and increasing informational efficiency. In contrast, we do not find consistent evidence that dark midpoint crossing systems significantly affect market quality. Our results support recent theory that dark limit order markets encourage aggressive competition in liquidity provision. We discuss implications for the regulation of dark trading and tick sizes.
Government agencies routinely allow pre-release access to information to accredited news agencies under embargo agreements. Using high-frequency data, we find evidence consistent with informed trading during embargoes of Federal Open Market Committee (FOMC) scheduled announcements. The E-mini Standard & Poor’s 500 futures’ abnormal order imbalances are in the direction of subsequent policy surprises and contain information that predicts the market reaction to the policy announcements. The estimated informed trades’ profits are arguably large. Notably, we find no evidence of informed trading prior to the start of FOMC news embargoes or during lockups ahead of nonfarm payroll, US Producer Price Index, and gross domestic product data releases.
We attempt to answer two questions in this paper: (i) How did jumps in equity returns change after the 2008–2009 financial crisis—in particular, were there significant changes in jump rates or in jump sizes, or both? (ii) Can the performance of affine jump-diffusion models be improved if jump sizes are larger, i.e., jumps with tails heavier than those of the normal distribution? To answer the second question, we find that a simple affine jump-diffusion model with both stochastic volatility and double-exponential jumps fits both the S&P 500 and the NASDAQ-100 daily returns from 1980 to 2013 well; the model outperforms existing ones (e.g., models with variance-gamma jumps or jumps in volatility) during the crisis and is at least comparable before the crisis. For the first question, on the basis of the model and the data sets, we observe that during the crisis, negative jump rate increased significantly, although there was little change in the average negative jump size.
This paper was accepted by Jerome Detemple, finance.
We investigate whether and how an exogenous and unprecedented improvement in non-U.S. firms' financial-reporting quality affects
post-earnings-announcement drift (PEAD). We find that PEAD declines after the information shock, and this decrease is more
pronounced for firms with fewer concurrent earnings announcements, greater institutional holdings, and lower limits to arbitrage.
In addition, the decrease in PEAD is driven by firms with greater changes in financial reporting, an increase in analyst forecast
accuracy and institutional ownership, and a decrease in limits to arbitrage. These findings support the mispricing explanation
of PEAD, in particular the limited attention hypothesis, in an international setting.
In recent years methods of data analysis for point processes have received some attention, for example, by Cox & Lewis (1966) and Lewis (1964). In particular Bartlett (1963 a,b) has introduced methods of analysis based on the point spectrum. Theoretical models are relatively sparse. In this paper the theoretical properties of a class of processes with particular reference to the point spectrum or corresponding covariance density functions are discussed. A particular result is a self-exciting process with the same second-order properties as a certain doubly stochastic process. These are not distinguishable by methods of data analysis based on these properties.
We parsimoniously characterize the severity of market frictions affecting a stock using the delay with which its price responds
to information. The most delayed firms command a large return premium not explained by size, liquidity, or microstructure
effects. Moreover, delay captures part of the size effect, idiosyncratic risk is priced only among the most delayed firms,
and earnings drift is monotonically increasing in delay. Frictions associated with investor recognition appear most responsible
for the delay effect. The very small segment of delayed firms, comprising only 0.02% of the market, generates substantial
variation in average returns, highlighting the importance of frictions.
We develop new procedures for maximum likelihood estimation of affine term structure models with spanned or unspanned stochastic volatility. Our approach uses linear regression to reduce the dimension of the numerical optimization problem yet it produces the same estimator as maximizing the likelihood. It improves the numerical behavior of estimation by eliminating parameters from the objective function that cause problems for conventional methods. We find that spanned models capture the cross-section of yields well but not volatility while unspanned models fit volatility at the expense of fitting the cross-section.
This paper investigates the dynamic behaviour of jumps in financial prices
and volatility. The proposed model is based on a standard jump diffusion
process for price and volatility augmented by a bivariate Hawkes process for
the two jump components. The latter process specifies a joint dynamic structure
for the price and volatility jump intensities, with the intensity of a
volatility jump also directly affected by a jump in the price. The impact of
certain aspects of the model on the higher-order conditional moments for
returns is investigated. In particular, the differential effects of the jump
intensities and the random process for latent volatility itself, are measured
and documented. A state space representation of the model is constructed using
both financial returns and non-parametric measures of integrated volatility and
price jumps as the observable quantities. Bayesian inference, based on a Markov
chain Monte Carlo algorithm, is used to obtain a posterior distribution for the
relevant model parameters and latent variables, and to analyze various
hypotheses about the dynamics in, and the relationship between, the jump
intensities. An extensive empirical investigation using data based on the
S&P500 market index over a period ending in early-2013 is conducted.
Substantial empirical support for dynamic jump intensities is documented, with
predictive accuracy enhanced by the inclusion of this type of specification. In
addition, movements in the intensity parameter for volatility jumps are found
to track key market events closely over this period.
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process.
This paper serves as an introduction and survey for economists to the field of sequential Monte Carlo methods which are also known as particle filters. Sequential Monte Carlo methods are simulation based algorithms used to compute the high-dimensional and/or complex integrals that arise regularly in applied work. These methods are becoming increasingly popular in economics and finance; from dynamic stochastic general equilibrium models in macro-economics to option pricing. The objective of this paper is to explain the basics of the methodology, provide references to the literature, and cover some of the theoretical results that justify the methods in practice.
The recent increase in large scale textual analysis along with the SEC’s plain English initiative makes it important to identify a proxy for measuring readability in financial disclosures. Using a large sample of 10 K filings during 1994–2011 we show that of the two components comprising the most commonly applied readability measure — the Fog Index — one is difficult to implement and the other is misspecified. Using post-filing date return volatility, standardized unexpected earnings, and analyst dispersion to measure the information environment, we find that 10-K document file size provides a simple proxy for readability that outperforms the Fog Index, does not require any parsing of the document, facilitates replication, and is broadly correlated with alternative readability constructs.
We test a frog-in-the-pan (FIP) hypothesis that predicts investors are inattentive to information arriving continuously in
small amounts. Intuitively, we hypothesize that a series of frequent gradual changes attracts less attention than infrequent
dramatic changes. Consistent with the FIP hypothesis, we find that continuous information induces strong persistent return
continuation that does not reverse in the long run. Momentum decreases monotonically from 5.94% for stocks with continuous
information during their formation period to –2.07% for stocks with discrete information but similar cumulative formation-period
returns. Higher media coverage coincides with discrete information and mitigates the stronger momentum following continuous
information.
We demonstrate that time stamps reported in I/B/E/S for analysts’ recommendations released during trading hours are systematically delayed. Using newswire-reported time stamps, we find 30-minute returns of 1.83% (-2.10%) for upgrades (downgrades), but for this subset of recommendations we find corresponding returns of -0.07% (-0.09%) using I/B/E/S-reported time stamps. We also examine the information content of recommendations relative to management guidance and earnings announcements. Our evidence suggests that analysts’ recommendations are the most important information disclosure channel examined.
This paper focuses on stocks that experience major price changes. Using analyst reports as a proxy, I study how information presence affects these stocks' post-event performance. Both regression and portfolio analyses show that price events accompanied by information are followed by drift, while no-information ones in contrast result in reversals. Potential profits available to investors trading on these two phenomena are economically very large. One interpretation of these results is that investors underreact to news about fundamentals and overreact to other shocks that move stock prices. Consistent with this hypothesis, I find that information-based price changes are more strongly correlated with future earnings surprises than no-information ones. Furthermore, drift is observed only when the direction of the price move and of the change in analyst recommendations have the same sign. Finally, I find that the ratio of no-information to information-based price shocks is strongly correlated with aggregate implied volatility and also forecasts future momentum returns.
We introduce a class of nonparametric spot volatility estimators based on delta sequences and conceived to include many of the existing estimators in the field as special cases. The full limit theory is first derived when unevenly sampled observations under infill asymptotics and fixed time-horizon are considered, and the state variable is assumed to follow a Brownian semimartingale. We then extend our class of estimators to include Poisson jumps or financial microstructure noise in the observed price process. As an application of our results, we relate the Fourier estimator to a specific delta sequence obtained with the Fejer function. The proposed estimators are applied to data from the S&P500 stock index futures market.
We analyze cross-sectional and time-series information from 46 equity markets around the world to consider whether short sales restrictions affect the efficiency of the market and the distributional characteristics of returns to individual stocks and market indices. We find some evidence that prices incorporate negative information faster in countries where short sales are allowed and practiced. A common conjecture by regulators is that short sales restrictions can reduce the relative severity of a market panic. We find strong evidence that in markets where short selling is either prohibited or not practiced, market returns display significantly less negative skewness.
This article investigates the predictability of jump arrivals in U.S. stock markets. Using a new test that identifies jump predictors up to the intraday level, I find that jumps are likely to occur shortly after macroeconomic information releases, such as the Federal Reserve announcements, nonfarm payroll reports, and jobless claims, as well as market index jumps. I also find firm-specific jump predictors related to earnings releases, analyst recommendations, past stock jumps, and dividend dates. Evidence suggests that distinguishing systematic jumps from idiosyncratic jumps is possible using the characteristics of jump predictors. Finally, I present a short-term jump size clustering. The Author 2011. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.
Short-horizon return predictability from order flows is an inverse indicator of market efficiency. We find that such predictability is diminished when bid-ask spreads are narrower, and has declined over time with the minimum tick size. Variance ratio tests suggest that prices were closer to random walk benchmarks in the more liquid decimal regime than in other ones. These findings indicate that liquidity stimulates arbitrage activity, which, in turn, enhances market efficiency. Further, as the tick size decreased, open-close/close-open return variance ratios increased, while return autocorrelations decreased. This suggests an increased incorporation of private information into prices during more liquid regimes.
Adverse shocks to stock markets propagate across the world, with a jump in one region of the world seemingly causing an increase in the likelihood of a different jump in another region of the world. To capture this effect mathematically, we introduce a model for asset return dynamics with a drift component, a volatility component and mutually exciting jumps known as Hawkes processes. In the model, a jump in one region of the world or one segment of the market increases the intensity of jumps occurring both in the same region (self-excitation) as well as in other regions (cross-excitation). The model generates the type of jump clustering that is observed empirically. Jump intensities then mean-revert until the next jump. We develop and implement an estimation procedure for this model. Our estimates provide evidence for self-excitation both in the US market as well as in other world markets. Furthermore, we find that US jumps tend to get reflected quickly in most other markets, while statistical evidence for the reverse transmission is much less pronounced. Implications of the model for measuring market stress, risk management and optimal portfolio choise are also investigated.