Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev,Tim Bollerslev +1 more
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TLDR
In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.About:Â
This article is published in Journal of Econometrics.The article was published on 1986-04-01 and is currently open access. It has received 17555 citations till now. The article focuses on the topics: Conditional variance & Autoregressive conditional heteroskedasticity.read more
Citations
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Journal ArticleDOI
Conditional heteroskedasticity in asset returns: a new approach
TL;DR: In this article, an exponential ARCH model is proposed to study volatility changes and the risk premium on the CRSP Value-Weighted Market Index from 1962 to 1987, which is an improvement over the widely-used GARCH model.
Book ChapterDOI
Time Series Analysis
TL;DR: This paper provides a concise overview of time series analysis in the time and frequency domains with lots of references for further reading.
Journal ArticleDOI
On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks
TL;DR: In this article, a modified GARCH-M model was used to find a negative relation between conditional expected monthly return and conditional variance of monthly return, using seasonal patterns in volatility and nominal interest rates to predict conditional variance.
Journal ArticleDOI
Multivariate Simultaneous Generalized ARCH
TL;DR: In this paper, a new parameterization of the multivariate ARCH process is proposed and equivalence relations are discussed for the various ARCH parameterizations, and conditions suffcient to guarantee the positive deffniteness of the covariance matrices are developed.
Journal ArticleDOI
Expected stock returns and volatility
TL;DR: In this article, the authors examined the relation between stock returns and stock market volatility and found that the expected market risk premium (the expected return on a stock portfolio minus the Treasury bill yield) is positively related to the predictable volatility of stock returns.
References
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Journal ArticleDOI
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Journal ArticleDOI
Time Series Analysis: Forecasting and Control
TL;DR: Time Series Analysis and Forecasting: principles and practice as mentioned in this paper The Oxford Handbook of Quantitative Methods, Vol. 3, No. 2: Statistical AnalysisTime-Series ForecastingPractical Time-Series AnalysisApplied Bayesian Forecasting and Time Series AnalysisSAS for Forecasting Time SeriesApplied Time Series analysisTime Series analysisElements of Nonlinear Time Series analyses and forecastingTime series analysis and forecasting by Example.
Journal ArticleDOI
Maximum likelihood estimation of misspecified models
TL;DR: In this article, the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference are examined, and the properties of the quasi-maximum likelihood estimator and the information matrix are exploited to yield several useful tests.
Journal ArticleDOI
A Simple Test for Heteroscedasticity and Random Coefficient Variation.
Trevor Breusch,Adrian Pagan +1 more
TL;DR: In this paper, a simple test for heteroscedastic disturbances in a linear regression model is developed using the framework of the Lagrangian multiplier test, and the criterion is given as a readily computed function of the OLS residuals.