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Wilmott, 2023
In this article, we will focus on the time series data which is the basis of all financial analysis, and which can be encapsulated with the functions TimeSeries, for single series, and TemporalData, for a collection of time series.
Patrick Woitschig, Wolfgang Karl Härdle
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In this article, we will focus on the time series data which is the basis of all financial analysis, and which can be encapsulated with the functions TimeSeries, for single series, and TemporalData, for a collection of time series.
Patrick Woitschig, Wolfgang Karl Härdle
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Financial Time Series Forecasting With the Deep Learning Ensemble Model
Mathematics, 2023 With the continuous development of financial markets worldwide to tackle rapid changes such as climate change and global warming, there has been increasing recognition of the importance of financial time series forecasting in financial market operation ...
Kaijian He, Jingcheng Pan
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WIREs Computational Statistics, 2009
AbstractThe evolution of financial markets is a complicated real‐world phenomenon that ranks at the top in terms of difficulty of modeling and/or prediction. One reason for this difficulty is the well‐documented nonlinearity that is inherent at work.
Politis, Dimitris Nicolas +1 more
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AbstractThe evolution of financial markets is a complicated real‐world phenomenon that ranks at the top in terms of difficulty of modeling and/or prediction. One reason for this difficulty is the well‐documented nonlinearity that is inherent at work.
Politis, Dimitris Nicolas +1 more
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Component analysis in financial time series
Proceedings of the IEEE/IAFE 1999 Conference on Computational Intelligence for Financial Engineering (CIFEr) (IEEE Cat. No.99TH8408), 2003We discuss the application of principal component analysis and independent component analysis for blind source separation of univariate financial time series. In order to perform single-channel versions of these techniques, we work within the embedding framework, using delay coordinate vectors to obtain a multidimensional representation of the system ...
Ragnar H. Lesch +2 more
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Detection of jumps in financial time series
Communications in Statistics - Simulation and Computation, 2020In this article, we propose a new test method to detect the jumps in financial time series. The main effort in the test is devoted to using the MaxEWMA chart to detect the jumps in the asset price ...
Yanhua Wu, Yufeng Shi
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Nonparametric Modeling in Financial Time Series
2009In this chapter, we deal with nonparametric methods for discretely observed financial data. The main ideas of nonparametric kernel smoothing are explained in the rather simple situation of density estimation and regression. For financial data, a rather relevant topic is nonparametric estimation of a volatility function in a continuous-time model such ...
Franke, Jürgen +2 more
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Essentials of Time Series for Financial Applications
2018Essentials of Time Series for Financial Applications serves as an agile reference for upper level students and practitioners who desire a formal, easy-to-follow introduction to the most important time series methods applied in financial applications (pricing, asset management, quant strategies, and risk management).
Guidolin, Massimo, Pedio, Manuela
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Structural Breaks in Financial Time Series
SSRN Electronic Journal, 2006This paper reviews the literature on structural breaks in financial time series. The second section discusses the implications of structural breaks in financial time series for statistical inference purposes. In the third section we discuss change-point tests in financial time series, including historical and sequential tests as well as single and ...
Elena Andreou, Eric Ghysels
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