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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Modeling exchange rate volatilities in croatia
TL;DR: The authors examined the performance of several autoregressive conditional heteroscedastic (ARCH) models for the EUR and USD against the HRK on daily data sets within the time period from 1997 to 2015.
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Is there a Positive Risk-Return Tradeoff? A Forward-Looking Approach to Measuring the Equity Premium
TL;DR: This paper revisited the time-series relation between risk and return on the stock market portfolio and found that conditional market risk is significantly priced in the context of asset pricing theory both in the short and long-run using various specifications for volatility.
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Time-Varying Volatility Asymmetry: A Conditioned HAR-RV(CJ) EGARCH-M Model
TL;DR: This paper developed a model that accounts for volatility feedback and leverage effects, effectively incorporating signed continuous and jump components of the realized variance into the variance specification through a heterogeneous autoregressive forecasting model.
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Empirical performance of garch models with heavy‐tailed innovations
TL;DR: In this article, a new type of heavy-tailed distribution, the normal reciprocal inverse Gaussian distribution (NRIG), was introduced to the GARCH and Glosten-Jagannathan-Runkle (1993) GARCH models, and compared its empirical performance with two other popular types of heavytailed distribution.
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On Asymmetry of Exchange Rate Volatility in New EU Member and Candidate Countries
TL;DR: In this article, the authors examined the exchange rate volatility in selected new EU Member States (Czech Republic, Hungary, Poland, Slovakia) and candidate countries (Croatia, Romania, Turkey) using TARCH model and daily data from the period May 2004 - December 2006.
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.