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R in Finance and Economics-A Beginner's Guide

February 2017

February 2017

DOI:10.1142/10151

Publisher: World Scientific
ISBN: 978-981-3144-46-0

Authors:

Abhay Kumar Singh

Macquarie University

David Edmund Allen

Edith Cowan University

This book provides an introduction to the statistical software R and its application with an empirical approach in finance and economics. It is specifically targeted towards undergraduate and graduate students. It provides beginner-level introduction to R using RStudio and reproducible research examples. It will enable students to use R for data cleaning, data visualization and quantitative model building using statistical methods like linear regression, econometrics (GARCH etc), Copulas, etc. Moreover, the book demonstrates latest research methods with applications featuring linear regression, quantile regression, panel regression, econometrics, dependence modelling, etc. using a range of data sets and examples Preface and TOC: http://www.rforresearch.com/r-in-finance-economics URL: http://www.worldscientific.com/worldscibooks/10.1142/10151 Presale: https://www.amazon.com/R-Finance-Economics-Beginners-Guide/dp/9813144467 Now you can download the supplementary material (Code & Data) from here http://www.rforresearch.com/r-in-finance-economics

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Abhay Kumar Singh · David Edmund Allen

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Abhay Kumar Singh · David Edmund Allen

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Article

Testing the bivariate distribution of daily equity returns using copulas. An application to the Span...

November 2006 · Computational Statistics & Data Analysis

The problem of the identification of dependencies between time series of equity returns is analyzed. Marginal distribution functions are assumed to be known, and a bivariate chi-square test of fit is applied in a fully parametric copula approach. Several marginal models and families of copulas are fitted and compared with Spanish stock market data. The results show the difficulty in adjusting the ... [Show full abstract] bivariate distribution of raw returns, and highlight the effect of a GARCH filtering in the selection of the best fitting copula.

Article

Return-Volatility Relationship: Insights from Linear and Non-Linear Quantile Regression

April 2013 · SSRN Electronic Journal

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Article

The Structure and Degree of Dependence - A Quantile Regression Approach

April 2011 · Journal of Banking & Finance

Dirk G. Baur

The copula function defines (i) the degree of dependence and (ii) the structure of dependence. This paper proposes an alternative semi-parametric framework to decompose the dependence. It is demonstrated that the methodology provides a detailed picture of dependence including asymmetric and non-linear relationships. In addition, changes in the degree or structure of dependence can be modelled and ... [Show full abstract] tested for each quantile of the distribution. The empirical part applies the methodology to different sets of financial time-series, that is, two exchange rates, two commodity indices and a large number of financial sector equity indices of major emerging and developed countries. The financial sector application entails an analysis of contagion and shows that financial turmoil changes both the degree and the structure of dependence. The empirical results generally demonstrate that the degree and the structure of dependence vary substantially across asset classes and across time.

Article

A goodness-of-fit test for copulas based on martingale transformation

September 2019 · Journal of Econometrics

This paper proposes an asymptotically distribution-free test for copulas with dynamic marginal distributions, such as GARCH and ARMA processes. The test is based on the empirical copula process with parametrically estimated marginal distributions. By applying the Khmaladze (1982, 1988, 1993) martingale transformation method, the transformed empirical process converges to a standard Gaussian ... [Show full abstract] process, so the resulting test statistics are asymptotically distribution-free. Monte Carlo simulations show that the test performs well in finite samples. An empirical application to test copulas between EUR/USD and GBP/USD exchange rates is provided.