RMetrics

Rmetrics Open Source Project: With hundreds of functions built on modern methods, the Rmetrics open source software combines exploratory data analysis, statistical modelling and rapid model prototyping. The R/Rmetrics packages are embedded in R, building an environment which creates a first class system for applications in teaching statistics and finance. Rmetrics covers Time Series Econometrics, Hypothesis Testing, GARCH Modelling and Volatility Forecasting, Extreme Value Theory and Copulae, Pricing of Derivatives, Portfolio Analysis, Design and Optimization, and much more.


References in zbMATH (referenced in 26 articles )

Showing results 1 to 20 of 26.
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  1. Arratia, Argimiro; Dorador, Albert: On the efficacy of stop-loss rules in the presence of overnight gaps (2019)
  2. Hušková, Marie; Neumeyer, Natalie; Niebuhr, Tobias; Selk, Leonie: Specification testing in nonparametric AR-ARCH models (2019)
  3. Nagler, T.; Bumann, C.; Czado, C.: Model selection in sparse high-dimensional vine copula models with an application to portfolio risk (2019)
  4. Davis, Richard A.; Drees, Holger; Segers, Johan; Warchoł, Michał: Inference on the tail process with application to financial time series modeling (2018)
  5. Ebner, Bruno; Klar, Bernhard; Meintanis, Simos G.: Fourier inference for stochastic volatility models with heavy-tailed innovations (2018)
  6. Stübinger, Johannes; Endres, Sylvia: Pairs trading with a mean-reverting jump-diffusion model on high-frequency data (2018)
  7. Stübinger, Johannes; Mangold, Benedikt; Krauss, Christopher: Statistical arbitrage with vine copulas (2018)
  8. Mirzaei Talarposhti, Fatemeh; Javedani Sadaei, Hossein; Enayatifar, Rasul; Gadelha Guimarães, Frederico; Mahmud, Maqsood; Eslami, Tayyebeh: Stock market forecasting by using a hybrid model of exponential fuzzy time series (2016)
  9. Schlägel, Ulrike E.; Lewis, Mark A.: A framework for analyzing the robustness of movement models to variable step discretization (2016)
  10. Chen, Yining: Semiparametric time series models with log-concave innovations: maximum likelihood estimation and its consistency (2015)
  11. Jiménez-Gamero, M. Dolores; Kim, Hyoung-Moon: Fast goodness-of-fit tests based on the characteristic function (2015)
  12. Matilainen, Markus; Nordhausen, Klaus; Oja, Hannu: New independent component analysis tools for time series (2015)
  13. Arratia, Argimiro: Computational finance. An introductory course with R (2014)
  14. Coin, Daniele: A method to estimate power parameter in exponential power distribution via polynomial regression (2013)
  15. Gilleland, Eric; Ribatet, Mathieu; Stephenson, Alec G.: A software review for extreme value analysis (2013)
  16. Gneiting, Tilmann; Ranjan, Roopesh: Combining predictive distributions (2013)
  17. Honda, Toshio: Nonparametric quantile regression with heavy-tailed and strongly dependent errors (2013)
  18. Rubio, F. J.; Johansen, Adam M.: A simple approach to maximum intractable likelihood estimation (2013)
  19. Tsay, Ruey S.: An introduction to analysis of financial data with R. (2013)
  20. Araújo Santos, Paulo; Fraga Alves, M. Isabel: A new class of independence tests for interval forecasts evaluation (2012)

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