Journal ArticleDOI
Threshold heteroskedastic models
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In this paper, the conditional standard deviation is a piecewise linear function of past values of the white noise, which allows different reactions of the volatility to different signs of the lagged errors.About:
This article is published in Journal of Economic Dynamics and Control.The article was published on 1994-09-01. It has received 2125 citations till now. The article focuses on the topics: Autoregressive conditional heteroskedasticity & Heteroscedasticity.read more
Citations
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GARCH-Type Models and Performance of Information Criteria
TL;DR: The ability of information criteria toward the correct selection of different especially higher-order generalized autoregressive conditional heteroscedasticity processes, based on their probability of correct selection as a measure of performance, is discussed.
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Herding and Positive Feedback Trading in American Stock Market: A Two Co-directional Behavior of Investors
Malek Belhoula,Kamel Naoui +1 more
TL;DR: In this paper, the authors use aggregate data from DJIA since 1987 to empirically test for herding and positive feedback trading behaviors, and find a significant presence of herding behavior during periods of relatively large market price movements.
Posted Content
Return spillovers around the globe: A network approach
TL;DR: In this paper, a rolling windows analysis of filtered and aligned stock index returns from 40 countries during the period 2006-2014 was performed, and the authors investigated the ensuing structure of the relationships by studying network properties and fitting spatial probit models.
Posted Content
A Conditionally Heteroskedastic Model with Time-varying Coefficients for Daily Gas Spot Prices
TL;DR: In this article, a novel GARCH(1,1) model, with coefficients function of the realizations of an exogenous process, is considered for the volatility of daily gas prices.
Book
Econometric analysis of financial and economic time series
TL;DR: In this article, the basic themes such as - time varying betas of the capital asset pricing model, analysis of predictive densities of nonlinear models of stock returns, modelling multivariate dynamic correlations, flexible seasonal time series models, estimation of long-memory time series, and more.
References
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Journal ArticleDOI
Handbook of Mathematical Functions
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.
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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.
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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.