Volatility and Time Series Econometrics

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出版者:Oxford Univ Pr (Sd)
作者:Bollerslev, Tim; Russell, Jeffrey R.; Watson, Mark W.
出品人:
頁數:432
译者:
出版時間:2009-12-15
價格:USD 99.00
裝幀:Hardcover
isbn號碼:9780199549498
叢書系列:
圖書標籤:
  • 經濟
  • 計量經濟學
  • 時間序列分析
  • 波動率
  • 金融經濟學
  • 風險管理
  • GARCH模型
  • 時間序列模型
  • 金融市場
  • 統計建模
  • 經濟預測
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具體描述

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,這是一本關於金融計量經濟學、時間序列分析與波動性建模的深度教材的簡介。 --- 書名:金融時間序列分析與波動性建模:理論、方法與實證應用 簡介 本書是一部全麵而深入探討現代金融時間序列分析和波動性建模的專著。本書旨在為計量經濟學、金融學、統計學以及相關領域的學生、研究人員和從業者提供一個堅實而係統的理論基礎和豐富實用的工具箱。 金融市場數據以其獨特的復雜性而著稱,錶現齣波動性的集聚性、非對稱性、厚尾性和時變性等顯著特徵。傳統的計量模型往往難以有效捕捉這些動態特徵。本書正是為瞭應對這些挑戰而構建,係統地梳理瞭從經典計量模型到前沿波動性模型的發展脈絡,並強調瞭其在實際金融數據分析中的應用。 第一部分:基礎與背景 本書的開篇部分緻力於為讀者打下堅實的計量經濟學和時間序列分析基礎。我們首先迴顧瞭時間序列分析的核心概念,包括平穩性、自相關函數(ACF)和偏自相關函數(PACF)。隨後,我們將重點介紹如何識彆和構建描述金融數據特徵的基礎模型,例如自迴歸(AR)、移動平均(MA)以及兩者的結閤——自迴歸移動平均(ARMA)模型。 在金融應用中,我們特彆強調瞭時間序列數據的平穩性檢驗在模型估計和推斷中的關鍵作用。本書詳細講解瞭傳統的基於OLS的檢驗方法,並引入瞭更為穩健的單位根檢驗(如ADF、PP檢驗)及其在實際應用中的注意事項。我們還討論瞭協整理論的基礎,為處理非平穩金融變量之間的長期均衡關係奠定瞭理論基礎。 第二部分:波動性建模的理論與實踐 本書的核心部分聚焦於波動性建模,這是理解金融市場風險和定價機製的關鍵。波動性,即金融資産迴報率的條件方差,通常是時變的。本書從經典模型齣發,係統地介紹瞭波動性建模的演進。 GARCH族模型詳解: 我們深入探討瞭廣義自迴歸條件異方差(GARCH)模型的原理、估計與檢驗。從最基礎的ARCH(q)模型到標準GARCH(p,q)模型,本書詳細闡述瞭其數學結構和經濟學含義。重點在於,本書對GARCH模型的識彆、估計(主要采用極大似然估計法)以及模型診斷進行瞭詳盡的論述,並提供瞭實用的檢驗方法來評估模型的擬閤優度。 超越標準模型: 鑒於金融數據中普遍存在的波動率聚集與杠杆效應,本書詳細介紹瞭多重擴展模型: 1. EGARCH (Exponential GARCH): 專門用於捕捉波動率對資産收益衝擊(好消息或壞消息)的非對稱反應,即杠杆效應。本書解釋瞭EGARCH如何在對數尺度上建模條件方差,從而確保瞭波動率的非負性。 2. GJR-GARCH (Glosten, Jagannathan, Runkle GARCH): 另一種捕捉杠杆效應的模型,通過引入一個指示變量來區分正負衝擊的影響。 3. Integrated GARCH (IGARCH): 探討瞭波動率過程是否具有長期記憶性,即條件方差是否會隨時間推移而永久性地被衝擊改變的現象。 多變量波動性建模: 現代金融分析往往涉及多個資産之間的相互作用。本書拓展到多變量(Multivariate)波動性建模領域,介紹瞭如何有效建模和預測資産收益之間的動態協方差結構。這部分內容包括多元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,後者在處理高維度數據和保持模型可行性方麵具有顯著優勢。 第三部分:高級主題與前沿方法 為瞭應對更復雜的金融場景,本書引入瞭更高級和前沿的建模技術。 隨機波動性模型(Stochastic Volatility, SV): 與GARCH模型將波動性視為一個確定性函數不同,SV模型將波動性視為一個不可直接觀測的潛在隨機過程。本書詳細介紹瞭SV模型的基礎結構、基於狀態空間錶示的估計方法(如卡爾曼濾波)以及現代的馬爾可夫鏈濛特卡洛(MCMC)方法在SV模型中的應用。 期權定價與波動率微笑: 波動率是期權定價的核心輸入。本書將波動性模型與Black-Scholes框架相結閤,探討瞭如何利用時間序列估計的波動率來改進期權定價模型。我們探討瞭波動率微笑(Volatility Smile)和波動率偏度(Skew)的現象,並討論瞭如何使用局部波動模型(Local Volatility Models)和隨機局部波動模型(Stochastic Local Volatility, SLV)來更好地擬閤市場觀察到的價格結構。 高頻數據與微觀結構: 在金融計量學的前沿,高頻數據的應用日益重要。本書簡要介紹瞭如何處理和利用高頻數據來估計更真實的實時波動率,如使用基於極值或近乎極值的估計量(如平方根過程RV)。 第四部分:模型應用與實證案例 本書不僅關注理論推導,更強調模型在實際問題中的應用。我們通過豐富的實證案例,指導讀者如何選擇、估計和檢驗適用於不同類型金融數據(如股票、匯率、利率和商品價格)的模型。案例分析將貫穿風險管理(如VaR和ES的計算)、投資組閤優化以及宏觀經濟政策分析等多個領域。 總結 《金融時間序列分析與波動性建模:理論、方法與實證應用》力求成為一本嚴謹、全麵且富有洞察力的參考書。它不僅涵蓋瞭計量經濟學和時間序列分析的經典內容,更聚焦於金融市場特有的波動性動態。通過對理論的深入剖析和對實證方法的細緻講解,本書旨在培養讀者運用尖端計量工具解決復雜金融問題的能力。

作者簡介

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.

目錄資訊

•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
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