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Quasi-likelihood and indirect inference in non-Gaussian stochastic volatility models for univariate and multivariate financial data. For univariate data both quasi-likelihood estimation and indirect inference are implemented, while for the bivariate data a quasi-likelihood method is implemented.


Download stock data, build single index, constant correlation, and multigroup models, and estimate optimal stock portfolios. Plotting functions for the portfolio possibilities curve and portfolio cloud are included. A function to test a portfolio on a data set is also provided.


Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.


This package is partly based on Markowitz (1952), however considers empirical distributions. There's a strong emphasis on modelling conditional portfolio returns as functions of weight. Various "conditional parameters" are considered, including expected returns and quantile returns.


Five core functions evaluate the efficacy of a technical trading rule. - Conditional return statistics - Bootstrap resampling statistics - Reality Check for data snooping bias among parameter choices - Robustness, or Persistence, of parameter choices - Parameter Domain Correlation Test


Modelling the yield curve with some parametric models. The models implemented are: Nelson-Siegel, Diebold-Li and Svensson. The package also includes the data of the term structure of interest rate of Federal Reserve Bank and European Central Bank.


Functions and S3 classes for time indexes and time indexed series, which are compatible with FAME frequencies.


Environment for teaching "Financial Engineering and Computational Finance"


Environment for teaching "Financial Engineering and Computational Finance"


Portfolio optimization typically requires an estimate of a covariance matrix of asset returns. There are many approaches for constructing such a covariance matrix, some using the sample covariance matrix as a starting point. This package provides implementations for two such methods: random matrix theory and shrinkage estimation. Each method attempts to clean or remove noise related to the sampling process from the sample covariance matrix.