Journal ArticleDOI
A conditionally heteroskedastic time series model for speculative prices and rates of return
TLDR
In this article, a simple time series model designed to capture the dependence of speculative price changes and rates of return data was presented, which is an extension of the autoregressive conditional heteroskedastic (ARCH) and generalized ARCH (GARCH) models obtained by allowing for conditionally t-distributed errors.Abstract:
The distribution of speculative price changes and rates of return data tend to be uncorrelated over time but characterized by volatile and tranquil periods. A simple time series model designed to capture this dependence is presented. The model is an extension of the Autoregressive Conditional Heteroskedastic (ARCH) and Generalized ARCH (GARCH) models obtained by allowing for conditionally t-distributed errors. The model can be derived as a simple subordinate stochastic process by including an additive unobservable rror term in the conditional variance equation. The descriptive validity of the model is illustrated for a set of foreign exchange rates and stock price indices.read more
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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.
Journal ArticleDOI
Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model.
TL;DR: In this article, a multivariate time series model with time varying conditional variances and covariances but with constant conditional correlations is proposed, which is readily interpreted as an extension of the seemingly unrelated regression (SUR) model allowing for heteroskedasticity.
Journal ArticleDOI
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.
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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.
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.
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
Efficient capital markets: a review of theory and empirical work*
TL;DR: Efficient Capital Markets: A Review of Theory and Empirical Work Author(s): Eugene Fama Source: The Journal of Finance, Vol. 25, No. 2, Papers and Proceedings of the Twenty-Eighth Annual Meeting of the American Finance Association New York, N.Y. December, 28-30, 1969 (May, 1970), pp. 383-417 as mentioned in this paper
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.
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.