Journal Article10.1016/J.JECONOM.2004.09.005
Neglecting parameter changes in GARCH models
373
TL;DR: In this paper, the authors show that the sum of the estimated autoregressive parameters of the conditional variance converges to one for all common estimators of the GARCH model.
read more
About: This article is published in Journal of Econometrics. The article was published on 01 Nov 2005. The article focuses on the topics: Autoregressive conditional heteroskedasticity & Conditional variance.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns
TL;DR: This paper proposed a new generalized autoregressive conditionally heteroskedastic (GARCH) process, the asymmetric generalized dynamic conditional correlation (AG-DCC) model, which allows for series-specific news impact and smoothing parameters and permits conditional asymmetries in correlation dynamics.
Realized Volatility: A Review
TL;DR: In this article, the authors present a general univariate framework for estimating realized volatilities, and a simple discrete time model is presented in order to motivate the main results in this literature.
549
•Posted Content
Realized volatility: a review
TL;DR: In this article, the authors present a general univariate framework for estimating realized volatilities, a simple discrete time model is presented in order to motivate the main results, and the most important methods for providing consistent estimators are presented, and a critical exposition of different techniques is given.
455
Volatility transmission between gold and oil futures under structural breaks
Bradley T. Ewing,Farooq Malik +1 more
TL;DR: This paper employed univariate and bivariate GARCH models to examine the volatility of gold and oil futures incorporating structural breaks using daily returns from July 1, 1993 to June 30, 2010.
317
Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns
Lorenzo Cappiello,Robert F. Engle,Kevin Sheppard +2 more
TL;DR: Asymmetric dynamics in the correlations of global equity and bond returns. The AG-DCC model finds evidence of asymmetry in conditional variances and correlations, particularly for equity returns. The model also finds increased correlation among regional equity groups during periods of financial turmoil.
316
References
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.
23.7K
Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev,Tim Bollerslev +1 more
TL;DR: 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.
23.2K
Numerical recipes in C
William H. Press,Saul A. Teukolsky,William T. Vetterling,Brian P. Flannery +3 more
- 01 Jan 1994
TL;DR: The Diskette v 2.06, 3.5''[1.44M] for IBM PC, PS/2 and compatibles [DOS] Reference Record created on 2004-09-07, modified on 2016-08-08.
A long memory property of stock market returns and a new model
TL;DR: In this paper, a Monte-Carlo analysis of stock market returns was conducted and it was found that not only there is substantially more correlation between absolute returns than returns themselves, but the power transformation of the absolute return also has quite high autocorrelation for long lags.
3.9K
Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances
TL;DR: In this paper, the authors study the properties of the quasi-maximum likelihood estimator and related test statistics in dynamic models that jointly parameterize conditional means and conditional covariances, when a normal log-likelihood is maximized but the assumption of normality is violated.