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 34 articles , 1 standard article )

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  2. Njenga, Carolyn Ndigwako; Sherris, Michael: Modeling mortality with a Bayesian vector autoregression (2020)
  3. Shang, Han Lin: Dynamic principal component regression for forecasting functional time series in a group structure (2020)
  4. Deb, S.: VAR model based clustering method for multivariate time series data (2019)
  5. Polanco-Martínez, Josué M.: Dynamic relationship analysis between NAFTA stock markets using nonlinear, nonparametric, non-stationary methods (2019)
  6. Erhardt, Robert; Engler, David: An extension of spatial dependence models for estimating short-term temperature portfolio risk (2018)
  7. Bilokon, Paul; Gwinnutt, James; Jones, Daniel: Stochastic filtering methods in electronic trading (2017)
  8. Pravilovic, Sonja; Bilancia, Massimo; Appice, Annalisa; Malerba, Donato: Using multiple time series analysis for geosensor data forecasting (2017)
  9. Shang, Han Lin; Haberman, Steven: Grouped multivariate and functional time series forecasting: an application to annuity pricing (2017)
  10. El Assaad, Hani; Samé, Allou; Govaert, Gérard; Aknin, Patrice: A variational expectation-maximization algorithm for temporal data clustering (2016)
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  12. Beran, Jan; Feng, Yuanhua; Ghosh, Sucharita: Modelling long-range dependence and trends in duration series: an approach based on EFARIMA and ESEMIFAR models (2015)
  13. Christou, Vasiliki; Fokianos, Konstantinos: Estimation and testing linearity for non-linear mixed Poisson autoregressions (2015)
  14. Konstantakis, Konstantinos N.; Papageorgiou, Theofanis; Michaelides, Panayotis G.; Tsionas, Efthymios G.: Economic fluctuations and fiscal policy in Europe: a political business cycles approach using panel data and clustering (1996--2013) (2015)
  15. Lu, Yunfan; Wang, Jun; Niu, Hongli: Nonlinear multi-analysis of agent-based financial market dynamics by epidemic system (2015)
  16. Yang, Ge; Wang, Jun; Fang, Wen: Numerical analysis for finite-range multitype stochastic contact financial market dynamic systems (2015)
  17. Zevallos, Mauricio; Hotta, Luiz Koodi: Slope influence diagnostics in conditional heteroscedastic time series models (2015)
  18. Huang, Ting Ting; Sun, Bruce Qiang; Chen, Xinfu: Re-specification of affine term structure models: the linkage to empirical investigations (2014)
  19. Lopes, Sílvia R. C.; Prass, Taiane S.: Theoretical results on fractionally integrated exponential generalized autoregressive conditional heteroskedastic processes (2014)
  20. Abo-Hammour, Za’er; Alsmadi, Othman; Momani, Shaher; Arqub, Omar Abu: A genetic algorithm approach for prediction of linear dynamical systems (2013) ioport

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