Volatility and Time Series Econometrics

Volatility and Time Series Econometrics pdf epub mobi txt 电子书 下载 2026

出版者:Oxford Univ Pr (Sd)
作者:Bollerslev, Tim; Russell, Jeffrey R.; Watson, Mark W.
出品人:
页数:432
译者:
出版时间:2009-12-15
价格:USD 99.00
装帧:Hardcover
isbn号码:9780199549498
丛书系列:
图书标签:
  • 经济
  • 计量经济学
  • 时间序列分析
  • 波动率
  • 金融经济学
  • 风险管理
  • GARCH模型
  • 时间序列模型
  • 金融市场
  • 统计建模
  • 经济预测
想要找书就要到 小美书屋
立刻按 ctrl+D收藏本页
你会得到大惊喜!!

具体描述

Robert Engle received the Nobel Prize for Economics in 2003 for his work in time series econometrics. This book contains 16 original research contributions by some the leading academic researchers in the fields of time series econometrics, forecasting, volatility modelling, financial econometrics and urban economics, along with historical perspectives related to field of time series econometrics more generally. Engle's Nobel Prize citation focuses on his path-breaking work on autoregressive conditional heteroskedasticity (ARCH) and the profound effect that this work has had on the field of financial econometrics. Several of the chapters focus on conditional heteroskedasticity, and develop the ideas of Engle's Nobel Prize winning work. Engle's work has had its most profound effect on the modelling of financial variables and several of the chapters use newly developed time series methods to study the behavior of financial variables. Each of the 16 chapters may be read in isolation, but they all importantly build on and relate to the seminal work by Nobel Laureate Robert F. Engle.

好的,这是一本关于金融计量经济学、时间序列分析与波动性建模的深度教材的简介。 --- 书名:金融时间序列分析与波动性建模:理论、方法与实证应用 简介 本书是一部全面而深入探讨现代金融时间序列分析和波动性建模的专著。本书旨在为计量经济学、金融学、统计学以及相关领域的学生、研究人员和从业者提供一个坚实而系统的理论基础和丰富实用的工具箱。 金融市场数据以其独特的复杂性而著称,表现出波动性的集聚性、非对称性、厚尾性和时变性等显著特征。传统的计量模型往往难以有效捕捉这些动态特征。本书正是为了应对这些挑战而构建,系统地梳理了从经典计量模型到前沿波动性模型的发展脉络,并强调了其在实际金融数据分析中的应用。 第一部分:基础与背景 本书的开篇部分致力于为读者打下坚实的计量经济学和时间序列分析基础。我们首先回顾了时间序列分析的核心概念,包括平稳性、自相关函数(ACF)和偏自相关函数(PACF)。随后,我们将重点介绍如何识别和构建描述金融数据特征的基础模型,例如自回归(AR)、移动平均(MA)以及两者的结合——自回归移动平均(ARMA)模型。 在金融应用中,我们特别强调了时间序列数据的平稳性检验在模型估计和推断中的关键作用。本书详细讲解了传统的基于OLS的检验方法,并引入了更为稳健的单位根检验(如ADF、PP检验)及其在实际应用中的注意事项。我们还讨论了协整理论的基础,为处理非平稳金融变量之间的长期均衡关系奠定了理论基础。 第二部分:波动性建模的理论与实践 本书的核心部分聚焦于波动性建模,这是理解金融市场风险和定价机制的关键。波动性,即金融资产回报率的条件方差,通常是时变的。本书从经典模型出发,系统地介绍了波动性建模的演进。 GARCH族模型详解: 我们深入探讨了广义自回归条件异方差(GARCH)模型的原理、估计与检验。从最基础的ARCH(q)模型到标准GARCH(p,q)模型,本书详细阐述了其数学结构和经济学含义。重点在于,本书对GARCH模型的识别、估计(主要采用极大似然估计法)以及模型诊断进行了详尽的论述,并提供了实用的检验方法来评估模型的拟合优度。 超越标准模型: 鉴于金融数据中普遍存在的波动率聚集与杠杆效应,本书详细介绍了多重扩展模型: 1. EGARCH (Exponential GARCH): 专门用于捕捉波动率对资产收益冲击(好消息或坏消息)的非对称反应,即杠杆效应。本书解释了EGARCH如何在对数尺度上建模条件方差,从而确保了波动率的非负性。 2. GJR-GARCH (Glosten, Jagannathan, Runkle GARCH): 另一种捕捉杠杆效应的模型,通过引入一个指示变量来区分正负冲击的影响。 3. Integrated GARCH (IGARCH): 探讨了波动率过程是否具有长期记忆性,即条件方差是否会随时间推移而永久性地被冲击改变的现象。 多变量波动性建模: 现代金融分析往往涉及多个资产之间的相互作用。本书拓展到多变量(Multivariate)波动性建模领域,介绍了如何有效建模和预测资产收益之间的动态协方差结构。这部分内容包括多元GARCH(MGARCH)模型的框架,如VEC模型,以及更具操作性的CCC-GARCH和DCC-GARCH模型,后者在处理高维度数据和保持模型可行性方面具有显著优势。 第三部分:高级主题与前沿方法 为了应对更复杂的金融场景,本书引入了更高级和前沿的建模技术。 随机波动性模型(Stochastic Volatility, SV): 与GARCH模型将波动性视为一个确定性函数不同,SV模型将波动性视为一个不可直接观测的潜在随机过程。本书详细介绍了SV模型的基础结构、基于状态空间表示的估计方法(如卡尔曼滤波)以及现代的马尔可夫链蒙特卡洛(MCMC)方法在SV模型中的应用。 期权定价与波动率微笑: 波动率是期权定价的核心输入。本书将波动性模型与Black-Scholes框架相结合,探讨了如何利用时间序列估计的波动率来改进期权定价模型。我们探讨了波动率微笑(Volatility Smile)和波动率偏度(Skew)的现象,并讨论了如何使用局部波动模型(Local Volatility Models)和随机局部波动模型(Stochastic Local Volatility, SLV)来更好地拟合市场观察到的价格结构。 高频数据与微观结构: 在金融计量学的前沿,高频数据的应用日益重要。本书简要介绍了如何处理和利用高频数据来估计更真实的实时波动率,如使用基于极值或近乎极值的估计量(如平方根过程RV)。 第四部分:模型应用与实证案例 本书不仅关注理论推导,更强调模型在实际问题中的应用。我们通过丰富的实证案例,指导读者如何选择、估计和检验适用于不同类型金融数据(如股票、汇率、利率和商品价格)的模型。案例分析将贯穿风险管理(如VaR和ES的计算)、投资组合优化以及宏观经济政策分析等多个领域。 总结 《金融时间序列分析与波动性建模:理论、方法与实证应用》力求成为一本严谨、全面且富有洞察力的参考书。它不仅涵盖了计量经济学和时间序列分析的经典内容,更聚焦于金融市场特有的波动性动态。通过对理论的深入剖析和对实证方法的细致讲解,本书旨在培养读者运用尖端计量工具解决复杂金融问题的能力。

作者简介

Mark Watson is the Howard Harrison and Gabrielle Snyder Beck Professor of Economics and Public Affairs at Princeton University and a research associate at the National Bureau of Economic Research. He is a fellow of the American Academy of Arts and Sciences and of the Econometric Society. His research focuses on time-series econometrics, empirical macroeconomics, and macroeconomic forecasting. He has published articles in these areas and is the author (with James Stock) of Introduction to Econometrics, a leading undergraduate textbook. Watson has served on the editorial board of several journals including the American Economic Review, Journal of Applied Econometrics, Econometrica, the Journal of Business and Economic Statistics, the Journal of Monetary Economics, and Macroeconomic Dynamics. He currently serves as a Co-Editor of the Review of Economics and Statistics. He has served as a consultant for the Federal Reserve Banks of Chicago and Richmond. Tim Bollerslev is the first Juanita and Clifton Kreps Distinguished Professor of Economics at Duke University, and Professor of Finance at the Fuqua School of Business at Duke University. He is an elected Fellow of the Econometric Society, a Fellow of the Journal of Econometrics, and a long time Research Associate at the National Bureau of Economic Research. He is also affiliated with the Center for Research in Econometric Analysis of Time Series at the University of Aarhus, Denmark. Bollerslev is particularly well-known for his invention of the GARCH model and his work on financial market volatility and high-frequency financial data. He is a co-editor for the Journal of Applied Econometrics, and has previously served on the editorial board for more than ten other academic journals. Professor Bollerslev received his M.S. degree in economics and mathematics from the University from the University of Aarhus, Denmark, and his Ph.D. degree in economics from the University of California, San Diego. Jeffrey R. Russell is Professor of Econometrics and Statistics at the University of Chicago Booth School of Economics. He conducts research on financial econometrics, time series, applied econometrics, empirical market microstructure, and high-frequency financial data. Russell's recent research has focused on using intraday price data to measure and predict financial asset volatility. His work has appeared in the Review of Economic Studies, Journal of Financial Economics and Econometrica. His research is supported by a Morgan Stanley Equity Microstructure Grant and he is the recipient of an Alfred P. Sloan Doctoral Dissertation Fellowship. In addition to teaching and research, Russell is an associate editor of the Journal of Applied Econometrics and the Journal of Financial Econometrics and he also serves on the NASDAQ Board of Economic Advisors.

目录信息

•Introduction
•1: Ole E. Barndorff-Nielsen, Solja Kinnebrock and Neil Shephard: Measuring Downside Risk- Realized Semivariance
•2: Gianna Boero, Jeremy Smith and Kenneth F. Wallis: Modelling UK Inflation Uncertainty, 1958-2006
•3: Tim Bollerslev: Glossary to ARCH
•4: Jacob Boudoukh, Christopher Downing, Matthew Richardson, Richard Stanton and Robert F. Whitelaw: A Multifactor Nonlinear, Continuous-time Model of Interest Rate Volatility
•5: Luis Catão and Allan Timmerman: Volatility Regimes and Global Equity Returns
•6: N. Edward Coulson: The Long Run Shift-Share: Modelling the Sources of Metropolitan Sectoral Fluctuations
•7: Francis X. Diebold and Kamil Yilmaz: Macroeconomic Volatility and Stock Market Volatility, Worldwide
•8: Stephen Figlewski: Estimating the Implied Risk Neutral Density for the U.S. Market Portfolio
•9: Gloria González-Rivera and Emre Yoldas: Multivariate Autocontours for Specification Testing in Multivariate GARCH Models
•10: Clive W.J. Granger: A History of Econometrics at the University of California, San Diego, A Personal Viewpoint
•11: James D. Hamilton: Macroeconomics and ARCH
•12: David F. Hendry and Carlos Santos: An Automatic test of Super Exogeneity
•13: James H. Stock and Mark W. Watson: Changes in the Volatility of Residential Investment in the United States
•14: Andrew J. Patton and Allan Timmerman: Generalized Forecast Errors, A Change of Measure and Forecast Optimality Conditions
•15: Jeffrey Russell: Trade by Trade, Financial Transaction Price Dynamics and Limit Order Placement
•16: Halbert White, Tae-Hwan Kim and Simone Manganelli: Modelling Autoregressive Conditional Skewness and Kurtosis with Multi-Quantile CAViaR
· · · · · · (收起)

读后感

评分

评分

评分

评分

评分

用户评价

评分

评分

评分

评分

评分

本站所有内容均为互联网搜索引擎提供的公开搜索信息,本站不存储任何数据与内容,任何内容与数据均与本站无关,如有需要请联系相关搜索引擎包括但不限于百度google,bing,sogou

© 2026 book.quotespace.org All Rights Reserved. 小美书屋 版权所有