FinTS: Companion to Tsay (2005) Analysis of Financial Time Series. R companion to Tsay (2005) Analysis of Financial Time Series, 2nd ed. (Wiley). Includes data sets, functions and script files required to work some of the examples. Version 0.3-x includes R objects for all data files used in the text and script files to recreate most of the analyses in chapters 1-3 and 9 plus parts of chapters 4 and 11.

References in zbMATH (referenced in 189 articles , 1 standard article )

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  1. Gangammanavar, Harsha; Liu, Yifan; Sen, Suvrajeet: Stochastic decomposition for two-stage stochastic linear programs with random cost coefficients (2021)
  2. Zhang, Danna; Wu, Wei Biao: Convergence of covariance and spectral density estimates for high-dimensional locally stationary processes (2021)
  3. Zhou, Kenneth Q.; Li, Johnny Siu-Hang: Longevity Greeks: what do insurers and capital market investors need to know? (2021)
  4. Akyildiz, Ömer Deniz; Míguez, Joaquín: Nudging the particle filter (2020)
  5. Ataei, Masoud; Chen, Shengyuan; Yang, Zijiang; Peyghami, M. Reza: Time-homogeneous top-(K) ranking using tensor decompositions (2020)
  6. Ayele, Amare Wubishet; Gabreyohannes, Emmanuel; Edmealem, Hayimro: Generalized autoregressive conditional heteroskedastic model to examine silver price volatility and its macroeconomic determinant in Ethiopia market (2020)
  7. Cretarola, Alessandra; Figà-Talamanca, Gianna; Patacca, Marco: Market attention and Bitcoin price modeling: theory, estimation and option pricing (2020)
  8. Divisekara, Roshani W.; Nawarathna, Ruwan D.; Nawarathna, Lakshika S.: Forecasting of global market prices of major financial instruments (2020)
  9. Hai, Tran Hoang: Estimation of volatility causality in structural autoregressions with heteroskedasticity using independent component analysis (2020)
  10. Hallin, Marc; La Vecchia, Davide: A simple R-estimation method for semiparametric duration models (2020)
  11. Li, Z. Merrick; Laeven, Roger J. A.; Vellekoop, Michel H.: Dependent microstructure noise and integrated volatility estimation from high-frequency data (2020)
  12. Mazzarisi, P.; Barucca, P.; Lillo, F.; Tantari, D.: A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market (2020)
  13. Mohammed, Geleta T.; Aduda, Jane A.; Kube, Ananda O.: Improving forecasts of the EGARCH model using artificial neural network and fuzzy inference system (2020)
  14. Pandolfo, Giuseppe; Iorio, Carmela; Siciliano, Roberta; D’Ambrosio, Antonio: Robust mean-variance portfolio through the weighted (L^p) depth function (2020)
  15. Wang, Site; Gangammanavar, Harsha; Ekşioğlu, Sandra; Mason, Scott J.: Statistical estimation of operating reserve requirements using rolling horizon stochastic optimization (2020)
  16. Wang, Wei; Zhang, Hanyu; Li, Tong; Guo, Jianhua; Huang, Wei; Wei, Yun; Cao, Jinde: An interpretable model for short term traffic flow prediction (2020)
  17. Bodnar, Taras; Okhrin, Ostap; Parolya, Nestor: Optimal shrinkage estimator for high-dimensional mean vector (2019)
  18. Bose, Mahua; Mali, Kalyani: Designing fuzzy time series forecasting models: a survey (2019)
  19. Cascone, Marcos H.; Hotta, Luiz K.: Quasi-maximum likelihood estimation of GARCH models in the presence of missing values (2019)
  20. Chen, Likai; Wu, Wei Biao: Testing for trends in high-dimensional time series (2019)

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