Financial Risk Modelling and Portfolio Optimization with R (Statistics in Practice)

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Measuring risks -- 4. Synopsis of risk measures -- 4. Portfolio risk concepts -- References -- 5. Modern portfolio theory -- 5. Markowitz portfolios -- 5. Empirical mean-variance portfolios -- References -- pt. Suitable distributions for returns -- 6. The generalized hyperbolic distribution -- 6. The generalized lambda distribution -- 6. Synopsis of R packages for the GHD -- 6.

The package fBasics -- 6. The package GeneralizedHyperbolic -- 6. The package ghyp -- 6.

The package QRM -- 6. The package SkewHyperbolic -- 6. The package VarianceGamma -- 6. Synopsis of R packages for GLD -- 6. The package Davies -- 6. The package gld -- 6.

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The package lmomco -- 6. Applications of the GHD to risk modelling -- 6. Fitting stock returns to the GHD -- 6.

Risk assessment with the GHD -- 6. Stylized facts revisited -- 6. Applications of the GLD to risk modelling and data analysis -- 6. VaR for a single stock -- 6. Extreme value theory -- 7. Extreme value methods and models -- 7. The block maxima approach -- 7. The peaks-over-threshold approach -- 7.

Financial risk modelling and portfolio optimization with R (eBook, ) [www.farmersmarketmusic.com]

Synopsis of R packages -- 7. The package evd -- 7. The package evdbayes -- 7. The package evir -- 7. The package fExtremes -- 7. The packages ismev and extRemes -- 7. The package POT -- 7. The package QRM -- 7. The package Renext -- 7. Empirical applications of EVT -- 7.

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Section outline -- 7. Block maxima model for Siemens -- 7. POT method for Boeing -- References -- 8. Modelling volatility -- 8.

The class of ARCH models -- 8. Synopsis of R packages -- 8. The package ccgarch -- 8. The package fGarch -- 8. The package gogarch -- 8. The packages rugarch and rmgarch -- 8.

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The package tseries -- 8. Empirical application of volatility models -- References -- 9. Modelling dependence -- 9. Correlation, dependence and distributions -- 9. Correlations and dependence revisited -- 9. Classification of copulae -- 9. Synopsis of R packages -- 9. The packages copula and nacopula -- 9. The package fCopulae -- 9. The package gumbel -- 9. The package QRM -- 9.

Financial risk modelling and portfolio optimization with R

Empirical applications of copulae -- 9. GARCH-copula model -- 9. Mixed copula approaches -- References -- pt. Robust portfolio optimization -- Robust statistics -- Selected robust estimators -- Robust optimization -- Uncertainty sets and problem formulation -- Synopsis of R packages -- The package covRobust -- The package fPortfolio -- The package MASS -- The package robustbase -- The package robust -- The package rrcov -- The package Rsocp -- Empirical applications -- Robust versus classical statistics -- Robust optimization -- References -- Diversification reconsidered -- Most diversified portfolio -- Risk contribution constrained portfolios -- Optimal tail-dependent portfolios -- The package PortfolioAnalytics -- Comparison of approaches -- Optimal tail-dependent portfolio against benchmark -- Limiting contributions to expected shortfall -- References -- Risk-optimal portfolios -- Mean-VaR portfolios -- Optimal CVaR portfolios -- Optimal draw-down portfolios -- Packages for linear programming -- The package PerformanceAnalytics -- Yield Curve Modeling and Forecasting.

Statistical Models in Epidemiology. Introduction to R for Quantitative Finance. Introducing Survival and Event History Analysis. Computational Intelligence Techniques for Trading and Investment. Regression Modeling with Actuarial and Financial Applications. Mathematical Statistics with Resampling and R. Using the Weibull Distribution. An Introduction to High-Frequency Finance. Applied Survival Analysis Using R. Artificial Intelligence in Financial Markets.

Analysis of Financial Time Series. Methods for Applied Macroeconomic Research. Handbook of Financial Econometrics. Computation and Modelling in Insurance and Finance. Handbook of Volatility Models and Their Applications. Regression Analysis with R. Essentials of Monte Carlo Simulation.

Practical Time Series Analysis. Handbook of Computational Economics. Applied Time Series Econometrics. Statistical Modeling for Biomedical Researchers. Technical Analysis for Algorithmic Pattern Recognition. Statistics in a Nutshell. A Gentle Introduction to Optimization. Introduction to Probability and Statistics for Engineers and Scientists. Mathematical Statistics with Applications. Generalized Linear Models for Insurance Data. Economic Forecasting and Policy.

Econometric Methods with Applications in Business and Economics. Data Mining for Business Analytics. Fisher, Neyman, and the Creation of Classical Statistics. Stochastic Optimization Methods in Finance and Energy. Computational Methods in Biometric Authentication. Bootstrap Methods and their Application.

Econometrics of Financial High-Frequency Data. Performance Analysis of Complex Networks and Systems. Bayesian Models for Astrophysical Data. Handbook of Monte Carlo Methods.

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