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 180 articles , 1 standard article )

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  1. Akyildiz, Ömer Deniz; Míguez, Joaquín: Nudging the particle filter (2020)
  2. 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)
  3. Cretarola, Alessandra; Figà-Talamanca, Gianna; Patacca, Marco: Market attention and Bitcoin price modeling: theory, estimation and option pricing (2020)
  4. Divisekara, Roshani W.; Nawarathna, Ruwan D.; Nawarathna, Lakshika S.: Forecasting of global market prices of major financial instruments (2020)
  5. Hai, Tran Hoang: Estimation of volatility causality in structural autoregressions with heteroskedasticity using independent component analysis (2020)
  6. Li, Z. Merrick; Laeven, Roger J. A.; Vellekoop, Michel H.: Dependent microstructure noise and integrated volatility estimation from high-frequency data (2020)
  7. 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)
  8. Mohammed, Geleta T.; Aduda, Jane A.; Kube, Ananda O.: Improving forecasts of the EGARCH model using artificial neural network and fuzzy inference system (2020)
  9. Pandolfo, Giuseppe; Iorio, Carmela; Siciliano, Roberta; D’Ambrosio, Antonio: Robust mean-variance portfolio through the weighted (L^p) depth function (2020)
  10. Wang, Site; Gangammanavar, Harsha; Ekşioğlu, Sandra; Mason, Scott J.: Statistical estimation of operating reserve requirements using rolling horizon stochastic optimization (2020)
  11. Bodnar, Taras; Okhrin, Ostap; Parolya, Nestor: Optimal shrinkage estimator for high-dimensional mean vector (2019)
  12. Bose, Mahua; Mali, Kalyani: Designing fuzzy time series forecasting models: a survey (2019)
  13. Cascone, Marcos H.; Hotta, Luiz K.: Quasi-maximum likelihood estimation of GARCH models in the presence of missing values (2019)
  14. Chen, Likai; Wu, Wei Biao: Testing for trends in high-dimensional time series (2019)
  15. Choi, Ji-Eun; Shin, Dong Wan: Quantile forecasts for financial volatilities based on parametric and asymmetric models (2019)
  16. Goel, Anubha; Sharma, Amita; Mehra, Aparna: Robust optimization of mixed CVaR STARR ratio using copulas (2019)
  17. Jonker, Jonathan; Aravkin, Aleksandr; Burke, James V.; Pillonetto, Gianluigi; Webster, Sarah: Fast robust methods for singular state-space models (2019)
  18. Majda, Andrew J.; Tong, Xin T.: Simple nonlinear models with rigorous extreme events and heavy tails (2019)
  19. Manikas, Theodoros; Papavasiliou, Anastasia: Diffusion parameter estimation for the homogenized equation (2019)
  20. Mazzarisi, Piero; Lillo, Fabrizio; Marmi, Stefano: When panic makes you blind: a chaotic route to systemic risk (2019)

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