Relevant bibliographies by topics / Ultra-high-frequency financial data

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Academic literature on the topic 'Ultra-high-frequency financial data'

Author: Grafiati
Published: 11 March 2023

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Journal articles on the topic "Ultra-high-frequency financial data"

1

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

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2

Giampaoli, Iacopo, Wing Lon Ng, and Nick Constantinou. "Analysis of ultra-high-frequency financial data using advanced Fourier transforms." Finance Research Letters 6, no. 1 (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 (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, and 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, and 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 (2003): 243–57. http://dx.doi.org/10.1007/s10260-003-0058-y.

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7

Chen, Feng, and 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 (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, and 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 (2013): 1006–24. http://dx.doi.org/10.1017/s0021900200013760.

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Abstract:
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., and 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 (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 (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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