Effective measure of endogeneity for the Autoregressive Conditional Duration point processes via mapping to the self-excited Hawkes process

Filimonov, V. A.; Wheatley, Spencer; Sornette, Didier

doi:10.1016/j.cnsns.2014.08.042

Cited by 7 publications

(4 citation statements)

References 102 publications

(174 reference statements)

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“…A fifth stream has been concerned with analysing and modelling asset dynamics at the transaction level [64,65]. In this vein, the continuous-time random walk formalism is a quite pure physics native proposal for modeling tick-by-tick data [66][67][68][69][70][71], and there has also been a strong growth of the use of self-excited Hawkes models to describe volatility clustering as well as trade dynamics [72][73][74][75][76]. There is a derivation where the mid-price is conceptualized as the Brownian particle surrounding by 'solvent' particles (all the limit orders on the ask and on the bid sides), which is fundamental for understanding the microscopic origin of random walks in finance [77,78].…”

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confidence: 99%

Multifractal analysis of financial markets: a review

et al. 2019

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Multifractality is ubiquitously observed in complex natural and socioeconomic systems. Multifractal analysis provides powerful tools to understand the complex nonlinear nature of time series in diverse fields. Inspired by its striking analogy with hydrodynamic turbulence, from which the idea of multifractality originated, multifractal analysis of financial markets has bloomed, forming one of the main directions of econophysics. We review the multifractal analysis methods and multifractal models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. We survey the cumulating evidence for the presence of multifractality in financial time series in different markets and at different time periods and discuss the sources of multifractality. The usefulness of multifractal analysis in quantifying market inefficiency, in supporting risk management and in developing other applications is presented. We finally discuss open problems and further directions of multifractal analysis.

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confidence: 99%

Multifractal analysis of financial markets: a review

et al. 2019

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“…An interesting feature for a model of asset evolution is the possibility of distinguishing between long-memory and short-memory contributions to the volatility. Since part of the short-memory random effects may be attributed to the impact of external information on the asset's time evolution, such a distinction is also related to attempts in separating the endogenous and exogenous contributions to the volatility [64][65][66][67][68][69][70]. Indeed, although this should not be regarded as a clear cut distinction, one may reasonably expect that long-memory contributions could be ascribed to cooperative influences among the agents, whereas random volatility switches may also come from news reaching the market.…”

Section: Long-memory and Short-memory Volatilitymentioning

confidence: 99%

Scaling symmetry, renormalization, and time series modeling: The case of financial assets dynamics

et al. 2013

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We present and discuss a stochastic model of financial assets dynamics based on the idea of an inverse renormalization group strategy. With this strategy we construct the multivariate distributions of elementary returns based on the scaling with time of the probability density of their aggregates. In its simplest version the model is the product of an endogenous autoregressive component and a random rescaling factor designed to embody also exogenous influences. Mathematical properties like increments' stationarity and ergodicity can be proven. Thanks to the relatively low number of parameters, model calibration can be conveniently based on a method of moments, as exemplified in the case of historical data of the S&P500 index. The calibrated model accounts very well for many stylized facts, like volatility clustering, power-law decay of the volatility autocorrelation function, and multiscaling with time of the aggregated return distribution. In agreement with empirical evidence in finance, the dynamics is not invariant under time reversal, and, with suitable generalizations, skewness of the return distribution and leverage effects can be included. The analytical tractability of the model opens interesting perspectives for applications, for instance, in terms of obtaining closed formulas for derivative pricing. Further important features are the possibility of making contact, in certain limits, with autoregressive models widely used in finance and the possibility of partially resolving the long- and short-memory components of the volatility, with consistent results when applied to historical series.

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“…Casual observation indicates that stock prices respond to news articles reporting on new developments concerning companies' circumstances. Market reactions to news have been extensively studied by researchers in several different fields [1]- [13], with some researchers attempting to construct models that capture static and/or dynamic responses to endogenous and exogenous shocks [14], [15]. The starting point for neoclassical financial economists typically is what they refer to as the "efficient market hypothesis," which implies that stock prices respond at the very moment that news is delivered to market participants.…”

Section: Introductionmentioning

confidence: 99%

Novel and topical business news and their impact on stock market activity

2017

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We propose an indicator to measure the degree to which a particular news article is novel, as well as an indicator to measure the degree to which a particular news item attracts attention from investors. The novelty measure is obtained by comparing the extent to which a particular news article is similar to earlier news articles, and an article is regarded as novel if there was no similar article before it. On the other hand, we say a news item receives a lot of attention and thus is highly topical if it is simultaneously reported by many news agencies and read by many investors who receive news from those agencies. The topicality measure for a news item is obtained by counting the number of news articles whose content is similar to an original news article but which are delivered by other news agencies. To check the performance of the indicators, we empirically examine how these indicators are correlated with intraday financial market indicators such as the number of transactions and price volatility. Specifically, we use a dataset consisting of over 90 million business news articles reported in English and a dataset consisting of minute-by-minute stock prices on the New York Stock Exchange and the NASDAQ Stock Market from 2003 to 2014, and show that price volatility, transaction volumes, and the number of transactions exhibited a significant response to a news article when it was novel and topical.

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Effective measure of endogeneity for the Autoregressive Conditional Duration point processes via mapping to the self-excited Hawkes process

Cited by 7 publications

References 102 publications

Multifractal analysis of financial markets: a review

Multifractal analysis of financial markets: a review

Scaling symmetry, renormalization, and time series modeling: The case of financial assets dynamics

Novel and topical business news and their impact on stock market activity

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