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Articles de revues sur le sujet « Ultra-high-frequency financial data »

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Auteur : Grafiati
Publié le 11 mars 2023

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1

Brownlees, C. T., et G. M. Gallo. « Financial econometric analysis at ultra-high frequency : Data handling concerns ». Computational Statistics & ; Data Analysis 51, no 4 (décembre 2006) : 2232–45. http://dx.doi.org/10.1016/j.csda.2006.09.030.

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2

Giampaoli, Iacopo, Wing Lon Ng et Nick Constantinou. « Analysis of ultra-high-frequency financial data using advanced Fourier transforms ». Finance Research Letters 6, no 1 (mars 2009) : 47–53. http://dx.doi.org/10.1016/j.frl.2008.11.002.

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3

Koike, Yuta. « Inference for time-varying lead–lag relationships from ultra-high-frequency data ». Japanese Journal of Statistics and Data Science 4, no 1 (8 février 2021) : 643–96. http://dx.doi.org/10.1007/s42081-021-00106-2.

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AbstractA new approach for modeling lead–lag relationships in high-frequency financial markets is proposed. The model accommodates non-synchronous trading and market microstructure noise as well as intraday variations of lead–lag relationships, which are essential for empirical applications. A simple statistical methodology for analyzing the proposed model is presented, as well. The methodology is illustrated by an empirical study to detect lead–lag relationships between the S&P 500 index and its two derivative products.
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4

Dai, Wei, Yuan An et Wen Long. « Price change prediction of Ultra high frequency financial data based on temporal convolutional network ». Procedia Computer Science 199 (2022) : 1177–83. http://dx.doi.org/10.1016/j.procs.2022.01.149.

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5

Bundick, Brent, Noah Rhee et Yong Zeng. « Bayes estimation via filtering equation through implicit recursive algorithms for financial ultra-high frequency data ». Statistics and Its Interface 6, no 4 (2013) : 487–98. http://dx.doi.org/10.4310/sii.2013.v6.n4.a7.

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6

Zuccolotto, Paola. « Quantile estimation in ultra-high frequency financial data : a comparison between parametric and semiparametric approach ». Statistical Methods and Applications 12, no 2 (décembre 2003) : 243–57. http://dx.doi.org/10.1007/s10260-003-0058-y.

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7

Chen, Feng, et Peter Hall. « Inference for a Nonstationary Self-Exciting Point Process with an Application in Ultra-High Frequency Financial Data Modeling ». Journal of Applied Probability 50, no 4 (décembre 2013) : 1006–24. http://dx.doi.org/10.1239/jap/1389370096.

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Self-exciting point processes (SEPPs), or Hawkes processes, have found applications in a wide range of fields, such as epidemiology, seismology, neuroscience, engineering, and more recently financial econometrics and social interactions. In the traditional SEPP models, the baseline intensity is assumed to be a constant. This has restricted the application of SEPPs to situations where there is clearly a self-exciting phenomenon, but a constant baseline intensity is inappropriate. In this paper, to model point processes with varying baseline intensity, we introduce SEPP models with time-varying background intensities (SEPPVB, for short). We show that SEPPVB models are competitive with autoregressive conditional SEPP models (Engle and Russell 1998) for modeling ultra-high frequency data. We also develop asymptotic theory for maximum likelihood estimation based inference of parametric SEPP models, including SEPPVB. We illustrate applications to ultra-high frequency financial data analysis, and we compare performance with the autoregressive conditional duration models.
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8

Chen, Feng, et Peter Hall. « Inference for a Nonstationary Self-Exciting Point Process with an Application in Ultra-High Frequency Financial Data Modeling ». Journal of Applied Probability 50, no 04 (décembre 2013) : 1006–24. http://dx.doi.org/10.1017/s0021900200013760.

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Self-exciting point processes (SEPPs), or Hawkes processes, have found applications in a wide range of fields, such as epidemiology, seismology, neuroscience, engineering, and more recently financial econometrics and social interactions. In the traditional SEPP models, the baseline intensity is assumed to be a constant. This has restricted the application of SEPPs to situations where there is clearly a self-exciting phenomenon, but a constant baseline intensity is inappropriate. In this paper, to model point processes with varying baseline intensity, we introduce SEPP models with time-varying background intensities (SEPPVB, for short). We show that SEPPVB models are competitive with autoregressive conditional SEPP models (Engle and Russell 1998) for modeling ultra-high frequency data. We also develop asymptotic theory for maximum likelihood estimation based inference of parametric SEPP models, including SEPPVB. We illustrate applications to ultra-high frequency financial data analysis, and we compare performance with the autoregressive conditional duration models.
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9

Centanni, S., et M. Minozzo. « Estimation and filtering by reversible jump MCMC for a doubly stochastic Poisson model for ultra-high-frequency financial data ». Statistical Modelling : An International Journal 6, no 2 (juillet 2006) : 97–118. http://dx.doi.org/10.1191/1471082x06st112oa.

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10

Szóstakowski, Robert. « The use of the Hurst exponent to investigate the quality of forecasting methods of ultra-high-frequency data of exchange rates ». Przegląd Statystyczny 65, no 2 (30 janvier 2019) : 200–223. http://dx.doi.org/10.5604/01.3001.0014.0536.

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Over the last century a variety of methods have been used for forecasting financial time data series with different results. This article explains why most of them failed to provide reasonable results based on fractal theory using one day tick data series from the foreign exchange market. Forecasting AMAPE errors and forecasting accuracy ratios were calculated for statistical and machine learning methods for currency time series which were divided into sub-segments according to Hurst ratio. This research proves that the forecasting error decreases and the forecasting accuracy increases for all of the forecasting methods when the Hurt ratio increases. The approach which was used in the article can be successfully applied to time series forecasting by indicating periods with the optimal values of the Hurst exponent.
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11

Sobchenko, Yuriy A., Aleksandr A. Belov et Akylbek N. Omarov. « The Three-Factor Experiment on Microwave Micronization of Grain Feed ». Elektrotekhnologii i elektrooborudovanie v APK 3, no 44 (septembre 2021) : 116–23. http://dx.doi.org/10.22314/2658-4859-2021-68-3-116-123.

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To obtain the most reliable information about the object of research with minimal time and financial costs, it is necessary to search for a rational sequence of data collection on the characteristics of micronization of grain feeds. (Research purpose) The research purpose is in conducting experimental studies of UHF micronization of grain feeds. (Materials and methods) The article presents a full factor experiment as the method of planning and conducting the experiment, this method serves to find the optimal conditions and rules of processes occurring during ultrahigh-frequency processing of grain feed raw materials. Micronization was carried out in the developed installation, which provides the addition of magnetron powers to reduce the cost of the installation and the operating costs of micronization. Authors conducted experiments on feed barley of the second production class. During the experiment there was used the grain moisture meter "Fauna-M", ultra-high-frequency magnetron generators 1 kW Daewoo M 218 and caliper ShTs-1. (Results and discussion) A complete three-factor experiment was performed, as a result of which the influence of design parameters and installation modes on the properties of grain feed was studied on the example of feed barley when adding the magnetron powers in the device. The article presents experimental data on the measurement of the exchange energy content in samples of feed barley grain. Its increase was revealed, which is the main physical and chemical characteristic of the process of micronization of grain feed. (Conclusions) The article presents optimal ratios of variable essential factors, compliance with which can contribute to an increase in the exchange energy content in feed barley grain samples from 12 to 13.1 megajoules per kilogram: humidity of 16.5 percent, the power level of the second device, the height of the radio-transparent end of the waveguide relative to the grain layer level of 41.7 millimeters.
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12

Amaro de Matos, Joao, et Marcelo Fernandes. « Testing the Markov Property With Ultra-High Frequency Financial Data ». SSRN Electronic Journal, 2004. http://dx.doi.org/10.2139/ssrn.882457.

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13

Brownlees, Christian T., et Giampiero M. Gallo. « Financial Econometric Analysis at Ultra-High Frequency : Data Handling Concerns ». SSRN Electronic Journal, 2006. http://dx.doi.org/10.2139/ssrn.886204.

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14

Holý, Vladimír, et Petra Tomanová. « Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data ». Computational Economics, 10 octobre 2021. http://dx.doi.org/10.1007/s10614-021-10210-w.

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15

Noel, Dorian M. « The Application of SAS® Hash Object to Ultra-High Frequency Financial Data : A Case Study in Limit Order Book Reconstruction ». SSRN Electronic Journal, 2011. http://dx.doi.org/10.2139/ssrn.2100608.

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