Modelling Stock Market Volatilityby Peter H. Rossi
Pub. Date: 11/01/1996
Publisher: Elsevier Science
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/b>… See more details below
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
- Elsevier Science
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- 1.25(w) x 9.00(h) x 6.00(d)
Table of ContentsUnderstanding 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.
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