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Добірка наукової літератури з теми "Ultra-high-frequency financial data"

Автор: Grafiati
Опубліковано: 11 березня 2023

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Статті в журналах з теми "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 (December 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 (March 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 (February 8, 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 (December 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 (December 2013): 1006–24. http://dx.doi.org/10.1239/jap/1389370096.

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Анотація:
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 (December 2013): 1006–24. http://dx.doi.org/10.1017/s0021900200013760.

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Анотація:
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 (July 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 (January 30, 2019): 200–223. http://dx.doi.org/10.5604/01.3001.0014.0536.

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Анотація:
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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Дисертації з теми "Ultra-high-frequency financial data"

1

Shahtahmassebi, Golnaz. "Bayesian modelling of ultra high-frequency financial data." Thesis, University of Plymouth, 2011. http://hdl.handle.net/10026.1/894.

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Анотація:
The availability of ultra high-frequency (UHF) data on transactions has revolutionised data processing and statistical modelling techniques in finance. The unique characteristics of such data, e.g. discrete structure of price change, unequally spaced time intervals and multiple transactions have introduced new theoretical and computational challenges. In this study, we develop a Bayesian framework for modelling integer-valued variables to capture the fundamental properties of price change. We propose the application of the zero inflated Poisson difference (ZPD) distribution for modelling UHF data and assess the effect of covariates on the behaviour of price change. For this purpose, we present two modelling schemes; the first one is based on the analysis of the data after the market closes for the day and is referred to as off-line data processing. In this case, the Bayesian interpretation and analysis are undertaken using Markov chain Monte Carlo methods. The second modelling scheme introduces the dynamic ZPD model which is implemented through Sequential Monte Carlo methods (also known as particle filters). This procedure enables us to update our inference from data as new transactions take place and is known as online data processing. We apply our models to a set of FTSE100 index changes. Based on the probability integral transform, modified for the case of integer-valued random variables, we show that our models are capable of explaining well the observed distribution of price change. We then apply the deviance information criterion and introduce its sequential version for the purpose of model comparison for off-line and online modelling, respectively. Moreover, in order to add more flexibility to the tails of the ZPD distribution, we introduce the zero inflated generalised Poisson difference distribution and outline its possible application for modelling UHF data.
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2

Meitz, Mika. "Five contributions to econometric theory and the econometrics of ultra-high-frequency data." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögskolan i Stockholm] (EFI), 2006. http://www2.hhs.se/EFI/summary/694.htm.

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3

Amado, Cristina. "Four essays on the econometric modelling of volatility and durations." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-1325.

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Анотація:
The thesis "Four Essays on the Econometric Modelling of Volatility and Durations" consists of four research papers in the area of financial econometrics on topics of the modelling of financial market volatility and the econometrics of ultra-high-frequency data. The aim of the thesis is to develop new econometric methods for modelling and hypothesis testing in these areas. The second chapter introduces a new model, the time-varying GARCH (TV-GARCH) model, in which volatility has a smooth time-varying structure of either additive or multiplicative type. To characterize smooth changes in the (un)conditional variance we assume that the parameters vary smoothly over time according to the logistic transition function. A data-based modelling technique is used for specifying the parametric structure of the TV-GARCH models. This is done by testing a sequence of hypotheses by Lagrange multiplier tests presented in the chapter. Misspecification tests are also provided for evaluating the adequacy of the estimated model. The third chapter addresses the issue of modelling deterministic changes in the unconditional variance over a long return series. The modelling strategy is illustrated with an application to the daily returns of the Dow Jones Industrial Average (DJIA) index from 1920 until 2003. The empirical results sustain the hypothesis that the assumption of constancy of the unconditional variance is not adequate over long return series and indicate that deterministic changes in the unconditional variance may be associated with macroeconomic factors. In the fourth chapter we propose an extension of the univariate multiplicative TV-GARCH model to the multivariate Conditional Correlation GARCH (CC-GARCH) framework. The variance equations are parameterized such that they combine the long-run and the short-run dynamic behaviour of the volatilities. In this framework, the long-run behaviour is described by the individual unconditional variances, and it is allowed to vary smoothly over time according to the logistic transition function. The effects of modelling the nonstationary variance component are examined empirically in several CC-GARCH models using pairs of seven daily stock return series from the S&P 500 index. The results show that the magnitude of such effect varies across different stock series and depends on the structure of the conditional correlation matrix. An important feature of financial durations is the evidence of a strong diurnal variation over the trading day. In the fifth chapter we propose a new parameterization for describing the diurnal pattern of trading activity. The parametric structure of the diurnal component allows the duration process to change smoothly over the time-of-day according to the logistic transition function. The empirical results suggest that the diurnal variation may not always have the inverted U-shaped pattern for the trade durations as documented in earlier studies.
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4

CALVORI, FRANCESCO. "Financial modeling with ultra-high frequency data." Doctoral thesis, 2013. http://hdl.handle.net/2158/794647.

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Анотація:
La tesi affronta tre diverse problematiche relative alla modellazione della volatilià finanziaria utilizzando dati ad altissima frequenza. This thesis is composed of three different essays, all related to the issue of financial volatility modeling with ultra-high frequency data.
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5

"On the modelling of ultra high frequency financial data on the Johannesburg Stock Exchange." Thesis, 2008. http://hdl.handle.net/10210/760.

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Анотація:
This thesis considers the modelling of ultra high frequency (UHF) …nancial data from South African markets. The approach to be taken is that such irregularly spaced data can be viewed as a realization of a marked point process. We propose a statistical model that incorporates both the unequally spaced transaction times (the points) as well as the movements of the associated returns (the marks). In all data sets investigated, no change in the value of the mark accounts for more that half the observations. If “no change” is considered as the censoring of some underlying process, we can explicitly model both the censoring of marks and the underlying process by utilizing methods for Markov chains and missing values. All models considered hitherto in the literature assume homogeneity of structure within a UHF data set. Data analyses indicate strongly that such an assumption is not justi…ed. The proposed model aims to exploit this observation. The diurnal (time of day) e¤ect is a form of non-stationarity commonly found in UHF data sets. We show that the method currently considered standard practice is inadequate and we will propose modi…cations of it. Consideration is given to the classi…cation of heterogeneous subsets that arises naturally in UHF data, for instance daily subsets of a UHF data set. We …nd evidence in support of some market microstructure theories, but no theory is supported by all data sets considered. We pay attention to technical issues surrounding the application of certain tests to large samples. As large samples are common in UHF data sets methods that are sensitive to large sample size, for example the Ljung-Box test, are not suitable.
Professor Freek Lombard
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Частини книг з теми "Ultra-high-frequency financial data"

1

Yan, Sibo, and Da Yan. "Volatility Estimation in the Era of High-Frequency Finance." In FinTech as a Disruptive Technology for Financial Institutions, 99–141. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7805-5.ch006.

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Анотація:
Over the last two decades, ultra-high frequency (or tick-by-tick) transaction data has become increasingly available. This surge of high-frequency finance data has brought disruptive revolution that makes modeling asset prices as continuous-time processes more possible than ever before. This is because we can now witness market microstructures and stock market volatility over tiny time intervals. This chapter reviews some general frameworks like realized volatility (RV) in estimating the latent volatility and their recent developments in the era of high-frequency finance. New empirical facts are presented to help lay the foundation for creating intraday volatility models that can overcome noise interferences in high-frequency finance data. These facts also help explain some stock market anomalies like volatility jumps and flash crashes, which favor intraday RV over the traditionally used daily RV as a reliable physical measure of market risk.
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Тези доповідей конференцій з теми "Ultra-high-frequency financial data"

1

Sewell, Martin Victor, and Wei Yan. "Ultra high frequency financial data." In the 2008 GECCO conference companion. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1388969.1388988.

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2

Lipinski, Piotr. "Evolutionary approach to optimization of data representation for classification of patterns in financial ultra-high frequency time series." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3071178.3071341.

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