Modeling financial time series with S-Plus. This book can be considered as a users guide for the StFinMetrics software. This software is an S-Plus module developed at the Insightful Corporation for the statistical analysis and modelling of financial time series. But the book can also be used as an introduction to the time series analysis which covers computational and interpretational aspects of the topics. A basic familiarity with S-Plus is needed for the reader. The authors consider such problems as testing for nonstationarity and cointegration; analysis of vector autoregressive and multivariate GARCH models; modelling of long memory time series (including fractional ARIMA and GARCH); time series regression modelling and systems of regression equations; state space models; factor models for asset returns; rolling analysis and change-point detection; modelling extreme values and risk measures. The book contains many examples of algorithms and S-Plus computational codes. Usage of algorithms and graphical tools is described with applications to real data.

References in zbMATH (referenced in 42 articles , 1 standard article )

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  1. Goswami, Suchismita: Influential nodes and anomalous topic activities in social networks using multivariate time series and topic modeling (2022)
  2. Yang, Yang; Yang, Yanrong; Shang, Han Lin: Feature extraction for functional time series: theory and application to NIR spectroscopy data (2022)
  3. Ofori-Boateng, Dorcas; Gel, Yulia R.; Cribben, Ivor: Nonparametric anomaly detection on time series of graphs (2021)
  4. Altun, Emrah: A new approach to value-at-risk: GARCH-TSLx model with inference (2020)
  5. Civantos, I.; García-Algarra, J.: Analysis of telecom service operation behavior with time series (2020)
  6. Marrs, Frank W.; Campbell, Benjamin W.; Fosdick, Bailey K.; Cranmer, Skyler J.; Böhmelt, Tobias: Inferring influence networks from longitudinal bipartite relational data (2020)
  7. Njenga, Carolyn Ndigwako; Sherris, Michael: Modeling mortality with a Bayesian vector autoregression (2020)
  8. Shang, Han Lin: Dynamic principal component regression for forecasting functional time series in a group structure (2020)
  9. Deb, S.: VAR model based clustering method for multivariate time series data (2019)
  10. Polanco-Martínez, Josué M.: Dynamic relationship analysis between NAFTA stock markets using nonlinear, nonparametric, non-stationary methods (2019)
  11. Zhang, Bo; Wang, Guochao; Wang, Yiduan; Zhang, Wei; Wang, Jun: Multiscale statistical behaviors for Ising financial dynamics with continuum percolation jump (2019)
  12. Erhardt, Robert; Engler, David: An extension of spatial dependence models for estimating short-term temperature portfolio risk (2018)
  13. Wang, Jie; Wang, Jun; Stanley, H. Eugene: Multiscale multifractal DCCA and complexity behaviors of return intervals for Potts price model (2018)
  14. Bilokon, Paul; Gwinnutt, James; Jones, Daniel: Stochastic filtering methods in electronic trading (2017)
  15. Pravilovic, Sonja; Bilancia, Massimo; Appice, Annalisa; Malerba, Donato: Using multiple time series analysis for geosensor data forecasting (2017)
  16. Shang, Han Lin; Haberman, Steven: Grouped multivariate and functional time series forecasting: an application to annuity pricing (2017)
  17. El Assaad, Hani; Samé, Allou; Govaert, Gérard; Aknin, Patrice: A variational expectation-maximization algorithm for temporal data clustering (2016)
  18. Laurini, Márcio Poletti; Hotta, Luiz Koodi: Generalized moment estimation of stochastic differential equations (2016)
  19. Ling, Hui `Fox’; Stone, Douglas B.: Time-varying forecasts by variational approximation of sequential Bayesian inference (2016)
  20. Beran, Jan; Feng, Yuanhua; Ghosh, Sucharita: Modelling long-range dependence and trends in duration series: an approach based on EFARIMA and ESEMIFAR models (2015)

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