Journal ArticleDOI
Intraday periodicity and volatility persistence in financial markets
TLDR
In this article, a formal integration of standard volatility models with market microstructure variables to allow for a more comprehensive empirical investigation of the fundamental determinants behind the volatility clustering phenomenon is presented.About:
This article is published in Journal of Empirical Finance.The article was published on 1997-06-01. It has received 1388 citations till now. The article focuses on the topics: Forward volatility & Implied volatility.read more
Citations
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Answering the skeptics: yes, standard volatility models do provide accurate forecasts*
TL;DR: In this article, a voluminous literature has emerged for modeling the temporal dependencies in financial market volatility using ARCH and stochastic volatility models and it has been shown that volatility models produce strikingly accurate inter-daily forecasts for the latent volatility factor that would be of interest in most financial applications.
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Empirical properties of asset returns: stylized facts and statistical issues
TL;DR: In this paper, the authors present a set of stylized empirical facts emerging from the statistical analysis of price variations in various types of financial markets, including distributional properties, tail properties and extreme fluctuations, pathwise regularity, linear and nonlinear dependence of returns in time and across stocks.
Posted Content
Modeling and Forecasting Realized Volatility
TL;DR: In this article, the authors provide a general framework for integration of high-frequency intraday data into the measurement, modeling and forecasting of daily and lower frequency volatility and return distributions.
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Modeling and forecasting realized volatility
TL;DR: In this article, the authors provide a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions.
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Fractionally integrated generalized autoregressive conditional heteroskedasticity
TL;DR: In this article, the FIGARCH (Fractionally Integrated Generalized AutoRegressive Conditionally Heteroskedastic) process is introduced and the conditional variance of the process implies a slow hyperbolic rate of decay for the influence of lagged squared innovations.
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.
Journal ArticleDOI
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.
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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.
Journal ArticleDOI
On a measure of lack of fit in time series models
Greta M. Ljung,George E. P. Box +1 more
TL;DR: In this paper, the overall test for lack of fit in autoregressive-moving average models proposed by Box & Pierce (1970) is considered, and it is shown that a substantially improved approximation results from a simple modification of this test.
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ARCH modeling in finance: A review of the theory and empirical evidence
TL;DR: An overview of some of the developments in the formulation of ARCH models and a survey of the numerous empirical applications using financial data can be found in this paper, where several suggestions for future research, including the implementation and tests of competing asset pricing theories, market microstructure models, information transmission mechanisms, dynamic hedging strategies, and pricing of derivative assets, are also discussed.