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Covid-19 pandemic and stock returns volatility: Evidence from Vietnam’s stock marke
Ho Chi Minh City Open University Journal of Science - Economics and Business Administration, 2022 The Covid-19 global pandemic has caused trouble for labour and financial markets worldwide, and financial and health crises resulted. This makes policy makers get confused. The study is carried out with the aim of investigating the impacts of Covid-19 on
Nguyen Thi My Linh
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Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets [PDF]
Journal of Economics Finance and Administrative Science, 2021 Purpose – This paper tests the accuracies of the models that predict the Value-at-Risk (VaR) for the Market Integrated Latin America (MILA) and Association of Southeast Asian Nations (ASEAN) emerging stock markets during crisis periods.
Ramona Serrano Bautista +1 more
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Generalized Spatial and Spatiotemporal ARCH Models [PDF]
Statistical Papers 2022, 2021 In time-series analyses, particularly for finance, generalized autoregressive conditional heteroscedasticity (GARCH) models are widely applied statistical tools for modelling volatility clusters (i.e., periods of increased or decreased risk). In contrast, it has not been considered to be of critical importance until now to model spatial dependence in ...
arxiv +1 more source
Temporal Aggregation of Garch Processes [PDF]
Econometrica, 1993 Abstract We derive low frequency, say weekly, models implied by high frequency, say daily, ARMA models with symmetric GARCH errors. Both stock and flow variable cases are considered. We show that low frequency models exhibit conditional heteroskedasticity of the GARCH form as well.
Drost, F.C., Nijman, T.E.
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GARCH models without positivity constraints: Exponential or log GARCH? [PDF]
Journal of Econometrics, 2013 This paper provides a probabilistic and statistical comparison of the log-GARCH and EGARCH models, which both rely on multiplicative volatility dynamics without positivity constraints. We compare the main probabilistic properties (strict stationarity, existence of moments, tails) of the EGARCH model, which are already known, with those of an asymmetric
Francq, Christian +2 more
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Sparse Multivariate GARCH [PDF]
SSRN Electronic Journal, 2016 We propose sparse versions of multivariate GARCH models that allow for volatility and correlation spillover effects across assets. The proposed models are generalizations of existing diagonal DCC and BEKK models, yet they remain estimable for high-dimensional systems of asset returns.
Wu, Jianbin, Dhaene, Geert
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Price discovery in the cryptocurrency option market: A univariate GARCH approach
Cogent Economics & Finance, 2020 In this paper, two univariate generalised autoregressive conditional heteroskedasticity (GARCH) option pricing models are applied to Bitcoin and the Cryptocurrency Index (CRIX).
Pierre J. Venter +2 more
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Research Papers in Economics, 2008 The GARCH-t model is widely used to predict volatilty. However, modeling the conditional variance as a linear combination of past squared observations may not be the best approach if the standardized observations are non-Gaussian. A simple modi.cation lets the conditional variance, or its logarithm, depend on past values of the score of a t ...
Harvey, A., Chakravarty, T.
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A Copula-Garch Modelcopula-Garch Model [PDF]
Economic Research-Ekonomska Istraživanja, 2010 AbstractIn the present study we develop a new two-dimensional Copula-GARCH model. This type of two-dimensional process is characterized by a dependency structure modeled using a copula function. For the marginal densities we employ a GARCH(1,1) model with innovations drawn from a t-Student distribution.
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Improving GARCH volatility forecasts with regime-switching GARCH [PDF]
Empirical Economics, 2002 Many researchers use GARCH models to generate volatility forecasts. Using data on three major U.S. dollar exchange rates we show that such forecasts are too high in volatile periods. We argue that this is due to the high persistence of shocks in GARCH forecasts.
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