Collective synchronization and high frequency systemic instabilities in financial markets

We present some empirical evidence on the dynamics of price instabilities in financial markets and propose a new Hawkes modelling approach. Specifically, analysing the recent high frequency dynamics of a set of US stocks, we find that since 2001 the level of synchronization of large price movements across assets has significantly increased. We find that only a minor fraction of these systemic events can be connected with the release of pre-announced macroeconomic news. Finally, the larger is the multiplicity of the event \u2014 i.e. how many assets have swung together \u2014 the larger is the probability of a new event occurring in the near future, as well as its multiplicity. To reproduce these facts, due to the self- and cross-exciting nature of the event dynamics, we propose an approach based on Hawkes processes. For each event, we directly model the multiplicity as a multivariate point process, neglecting the identity of the specific assets. This allows us to introduce a parsimonious parametrization of the kernel of the process and to achieve a reliable description of the dynamics of large price movements for a high-dimensional portfolio

Pricing financial derivatives with neural networks

Abstract Neural network algorithms are applied to the problem of option pricing and adopted to simulate the nonlinear behavior of such financial derivatives. Two different kinds of neural networks, i.e. multi-layer perceptrons and radial basis functions, are used and their performances compared in detail. The analysis is carried out both for standard European options and American ones, including evaluation of the Greek letters, necessary for hedging purposes. Detailed numerical investigation show that, after a careful phase of training, neural networks are able to predict the value of options and Greek letters with high accuracy and competitive computational time

Multiple photon corrections to the neutral-current Drell-Yan process

Precision studies of single W and Z production processes at hadron colliders require progress in the calculation of electroweak radiative corrections. To this end, higher-order QED corrections to the neutral-current Drell-Yan process, due to multiple photon radiation in Z leptonic decays, are calculated. Particular attention is paid to the effects induced by such corrections on the experimental observables which are relevant for high-precision measurements of the W-boson mass at the Tevatron Run II and the LHC. The calculation is implemented in the Monte Carlo event generator HORACE, which is available for data analysis.Comment: 16 pages, 4 figures, 3 tables, JHEP3 styl

Modelling systemic price cojumps with Hawkes factor models

Instabilities in the price dynamics of a large number of financial assets are a clear sign of systemic events. By investigating portfolios of highly liquid stocks, we find that there are a large number of high-frequency cojumps. We show that the dynamics of these jumps is described neither by a multivariate Poisson nor by a multivariate Hawkes model. We introduce a Hawkes one-factor model which is able to capture simultaneously the time clustering of jumps and the high synchronization of jumps across assets

Matching matrix elements and shower evolution for top-quark production in hadronic collisions

We study the matching of multijet matrix elements and shower evolution in the case of top production in hadronic collisions at the Tevatron and at the LHC. We present the results of the matching algorithm implemented in the ALPGEN Monte Carlo generator, and compare them with results obtained at the parton level, and with the predictions of the MC@NLO approach. We highlight the consistency of the matching algorithm when applied to these final states, and the excellent agreement obtained with MC@NLO for most inclusive quantities. We nevertheless identify also a remarkable difference in the rapidity spectrum of the leading jet accompanying the top quark pair, and comment on the likely origin of this discrepancy.Comment: 22 pages, 13 figures, 5 tables. JHEP styl

Combination of electroweak and QCD corrections to single W production at the Fermilab Tevatron and the CERN LHC

Precision studies of the production of a high-transverse momentum lepton in association with missing energy at hadron colliders require that electroweak and QCD higher-order contributions are simultaneously taken into account in theoretical predictions and data analysis. Here we present a detailed phenomenological study of the impact of electroweak and strong contributions, as well as of their combination, to all the observables relevant for the various facets of the p\smartpap \to {\rm lepton} + X physics programme at hadron colliders, including luminosity monitoring and Parton Distribution Functions constraint, $W$ precision physics and search for new physics signals. We provide a theoretical recipe to carefully combine electroweak and strong corrections, that are mandatory in view of the challenging experimental accuracy already reached at the Fermilab Tevatron and aimed at the CERN LHC, and discuss the uncertainty inherent the combination. We conclude that the theoretical accuracy of our calculation can be conservatively estimated to be about 2% for standard event selections at the Tevatron and the LHC, and about 5% in the very high $W$ transverse mass/lepton transverse momentum tails. We also provide arguments for a more aggressive error estimate (about 1% and 3%, respectively) and conclude that in order to attain a one per cent accuracy: 1) exact mixed ${\cal O}(\alpha \alpha_s)$ corrections should be computed in addition to the already available NNLO QCD contributions and two-loop electroweak Sudakov logarithms; 2) QCD and electroweak corrections should be coherently included into a single event generator.Comment: One reference added. Final version to appear in JHE

Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users’ behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012–2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a “wisdom-of-the-crowd” effect that allows to exploit users’ activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment