Journal ArticleDOI
Threshold heteroskedastic models
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TLDR
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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Journal ArticleDOI
Identifying common dynamic features in stock returns
Jorge Caiado,Nuno Crato +1 more
TL;DR: Volatility and spectral based methods for the cluster analysis of stock returns are proposed to investigate the similarities and dissimilarities between the ‘blue-chip’ stocks used to compute the Dow Jones Industrial Average (DJIA) index.
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Modelling Stock Returns in the G-7 and in Selected CEE Economies: A Non-linear GARCH Approach
Balázs Égert,Yosra Koubaa +1 more
TL;DR: In this article, conditional variance patterns in daily return series of stock market indices in the G-7 and 6 selected economies of Central and Eastern Europe were investigated over the period 1987 to 2002.
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Risk factors and value at risk in publicly traded companies of the nonrenewable energy sector
TL;DR: In this paper, a sample of 64 oil and gas companies of the non-renewable energy sector from 24 countries using daily observations on return on stock from July 15, 2003 to August 14, 2012 was analyzed.
A prospective study of the k-factor Gegenbauer processes with heteroscedastic errors and an application to inflation rates
TL;DR: In this paper, the authors investigate some statistical properties of the new k-factor Gegenbauer process with heteroscedastic noises and give tools which permit to use this model to explain the behaviour of certain data sets in finance and in macroeconomics.
Posted Content
Identifying common dynamic features in stock returns
Jorge Caiado,Nuno Crato +1 more
TL;DR: In this paper, the authors proposed volatility and spectral based methods for cluster analysis of stock returns using the information about both the estimated parameters in the threshold GARCH (or TGARCH) equation and the periodogram of the squared returns, they compute a distance matrix for the stock returns.
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.