Financial time series analysis

Author: (USA) Cai Zhu, Pan Jiazi (Translator)
Publisher:
Publish Date: 2006-04-01
Features: This book introduces the new developments in financial econometrics methods found in the econometrics and statistics literature, emphasizing examples and data analysis. It particularly covers current research hotspots such as Value at Risk (VaR), high-frequency data analysis, and Markov Chain Monte Carlo (MCMC) methods. The main contents include: the basic characteristics of financial time series data, neural networks, nonlinear methods, derivative pricing using jump diffusion equations, VaR calculation using extreme value theory, multivariate volatility models with time-varying correlation coefficients, and Bayesian inference. This book can serve as a textbook for senior undergraduate or graduate students in finance and related fields for time series analysis, or as a reference for researchers in related fields. The book provides a comprehensive introduction to financial econometric models and their applications in modeling and forecasting financial time series data, helping readers understand the basic characteristics of financial data, master the application of financial econometric models, and gain experience in analyzing financial time series.
An overview of the new developments in financial econometrics methods found in econometrics and statistics literature is a prominent feature of this book. These developments include current research hotspots such as VaR, high-frequency data analysis, and MCMC methods. Some new results that have not yet been published in academic journals are also included, such as derivative pricing using jump diffusion equations, VaR calculation based on non-homogeneous two-dimensional Poisson processes using extreme value theory, and multivariate volatility models with time-varying correlation coefficients. Additionally, the book introduces Bayesian inference for financial data using MCMC methods. Emphasizing examples and data analysis is another outstanding feature of this book. The entire book uses actual financial data to illustrate the application of the discussed models and methods. Linear time series models are built using SCA; volatility models are estimated using RATS (Regression Analysis of Time Series); neural networks are implemented and graphs are drawn using S-Plus. Simple option pricing, estimation of extreme value models, VaR calculation, and Bayesian analysis are performed using Fortran programs.
This book introduces the new developments in financial econometrics methods found in econometrics and statistics literature, emphasizing examples and data analysis. It particularly covers current research hotspots such as VaR, high-frequency data analysis, and MCMC methods. The main contents include: the basic characteristics of financial time series data, neural networks, nonlinear methods, derivative pricing using jump diffusion equations, VaR calculation using extreme value theory, multivariate volatility models with time-varying correlation coefficients, and Bayesian inference. This book can serve as a textbook for senior undergraduate or graduate students in finance and related fields for time series analysis, or as a reference for researchers in related fields.