Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Artificial intelligence. Robotics. Simulation. Graphics --- Financial management --- Mathematical statistics --- Quantitative methods (economics) --- Finance --- Time-series analysis --- Econometrics --- R (Computer program language) --- Econometric models --- AA / International- internationaal --- 305.91 --- 305.970 --- 304.0 --- Econometrie van de financiële activa. Portfolio allocation en management. CAPM. Bubbles. --- Algemeenheden: Autoregression and moving average representation. ARIMA. ARMAX. Lagrange multiplier. Wald. Function (mis) specification. Autocorrelation. Homoscedasticity. Heteroscedasticity. ARCH. GARCH. Integration and co-integration. Unit roots. --- Zuivere statistische analyse (algemene naslagwerken). Tijdreeksen. --- Analysis of time series --- Autocorrelation (Statistics) --- Harmonic analysis --- Probabilities --- GNU-S (Computer program language) --- Domain-specific programming languages --- Economics, Mathematical --- Statistics --- Zuivere statistische analyse (algemene naslagwerken). Tijdreeksen --- Econometrie van de financiële activa. Portfolio allocation en management. CAPM. Bubbles --- Algemeenheden: Autoregression and moving average representation. ARIMA. ARMAX. Lagrange multiplier. Wald. Function (mis) specification. Autocorrelation. Homoscedasticity. Heteroscedasticity. ARCH. GARCH. Integration and co-integration. Unit roots --- Finance - Econometric models

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

[3rd ed.] This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering three main topics: Analysis and application of univariate financial time series The return series of multiple assets Bayesian inference in finance methods Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets. The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.

Mathematical statistics --- Time-series analysis --- Econometrics --- Risk management --- Série chronologique --- Econométrie --- Gestion du risque --- 519.2 --- 330.115 --- 519.246 --- econometrie --- forecasting --- markov-processen --- regressie-analyse --- risk management --- stochastische modellen --- tijdreeksanalyse --- 332.015195 --- Insurance --- Management --- Economics, Mathematical --- Statistics --- Analysis of time series --- Autocorrelation (Statistics) --- Harmonic analysis --- Probabilities --- Wiskundige statistiek --- Econometrie --- Statistics of stochastic processes. Estimation of stochastic processes. Hypothesis testing. Statistics of point processes. Time series analysis. Auto-correlation. Regression --- 519.246 Statistics of stochastic processes. Estimation of stochastic processes. Hypothesis testing. Statistics of point processes. Time series analysis. Auto-correlation. Regression --- 330.115 Econometrie --- Série chronologique --- Econométrie

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

A complete set of statistical tools for beginning financial analysts from a leading authority Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including: Linear time series analysis, with coverage of exponential smoothing for forecasting and methods for model comparison Different approaches to calculating asset volatility and various volatility models High-frequency financial data and simple models for price changes, trading intensity, and realized volatility Quantitative methods for risk management, including value at risk and conditional value at risk Econometric and statistical methods for risk assessment based on extreme value theory and quantile regression Throughout the book, the visual nature of the topic is showcased through graphical representations in R, and two detailed case studies demonstrate the relevance of statistics in finance. A related website features additional data sets and R scripts so readers can create their own simulations and test their comprehension of the presented techniques. An Introduction to Analysis of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate level. The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets.

Econometrics. --- Finance --- R (Computer program language) --- Time-series analysis. --- Econometric models.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

"This book presents methods useful for analyzing and understanding large data sets that are dynamically dependent. The book will begin with examples of multivariate dependent data and tools for presenting descriptive statistics of such data. It then introduces some useful statistical methods for univariate time series analysis emphasizing on statistical procedures for modeling and forecasting. Both linear and nonlinear models are discussed. Special attention is given to analysis of high-frequency dependent data. The second part of the book considers joint dependency, both contemporaneous and dynamical dependence, among multiple series of dependent data. Special attention will be given to graphical methods for large data, to handling heterogeneity in time series (such as outliers, missing values, and changes in the covariance matrices), and to time-varying parameters for multivariate time series. The third part of the book is devoted to analysis of high-dimensional dependent data. The focus is on topics that are useful when the number of time series is large. The selected topics include clustering time series, high-dimensional linear regression for dependent data and its applications, and reducing the dimension with dynamic principal components and factor models. Throughout the book, advantages and disadvantages of the methods discussed are given and real examples are used in demonstration. The book will be of interest to graduate students, researchers, and practitioners in business, economics, engineering, and science who are interested in statistical methods for analyzing big dependent data and forecasting"--

Big data --- Time-series analysis. --- Data mining --- Forecasting --- Mathematics. --- Statistical methods. --- Statistical methods.