Modelling Stock Market Volatility
Bridging the Gap to Continuous Time
Free Global ShippingNo minimum order
This essay collection focuses on the relationship between continuous time models and Autoregressive Conditionally Heteroskedastic (ARCH) models and applications. For the first time, Modelling Stock Market Volatility provides new insights about the links between these two models and new work on practical estimation methods for continuous time models. Featuring the pioneering scholarship of Daniel Nelson, the text presents research about the discrete time model, continuous time limits and optimal filtering of ARCH models, and the specification and estimation of continuous time processes. This work will lead to a rapid growth in their empirical application as they are increasingly subjected to routine specification testing.
- Provides for the first time new insights on the links between continuous time and ARCH models
- Collects seminal scholarship by some of the most renowned researchers in finance and econometrics
- Captures complex arguments underlying the approximation and proper statistical modelling of continuous time volatility dynamics
Table of Contents
- Understanding And Specifying The Discrete Time Model:
D.B. Nelson, Modelling Stock Market Volatility Changes.
D.B. Nelson, Stationarity and Persistence in the GARCH(I,I) Model.
D.B. Nelson, Conditional Heteroskedasticity in Asset Returns: A New Approach.
P.A. Braun, D.B. Nelson and A.M. Sunier, Good News, Bad News, Volatility, and Betas.
Continuous Time Limits And Optimal Filtering For ARCH Models:
D.B. Nelson, ARCH Models as Diffusion Approximations.
D.B. Nelson, Filtering and Forecasting with Misspecified ARCH Models I: Getting the Right Variance with the Wrong Model.
D.B. Nelson and D.P. Foster, Filtering and Forecasting with Misspecified ARCH Models II: Making the Right Forecast with the Wrong Model.
D.B. Nelson and D.P. Foster, Asymptotic Filtering Theory for Univariate ARCH Models.
D.B. Nelson, Asymptotic Filtering Theory for Multivariate ARCH Models.
D.B. Nelson and D.B. Nelson, Continuous Record Asymptotics for Rolling Sample Variance Estimators.
Specification and Estimation of Continuous Time Processes:
R.F. Engle and G.G.J. Lee, Estimating Diffusion Models of Stochastic Volatility.
A.R. Gallant and G. Tauchen, Specification Analysis of Continuous Time Models in Finance.
L.P. Hansen and J.A. Scheinkman, Back to the Future: Generating Moment Implications for Continuous-Time Markov Processes.
Y.Ait-Sahalia, Nonparametric Pricing of Interest Rate Derivative Securities.
- No. of pages: 485
- Language: English
- Copyright: © Academic Press 1996
- Published: November 4, 1996
- Imprint: Academic Press
- eBook ISBN: 9780080511870
About the Editor
Professor of Econometrics, Marketing, and Statistics at the University of Chicago's Graduate School of Business, Peter Rossi has made significant contributions to the fields of finance, microeconomics, and econometrics. Dr. Rossi held the Kellogg Research Chair at Northwestern University, was the IBM Scholar in the Graduate School of Business at Chicago, and has won a number of awards for his work.
Affiliations and Expertise
University of Massachusetts, Amherst, U.S.A.
Ratings and Reviews
There are currently no reviews for "Modelling Stock Market Volatility"