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Linear time-varying regression with Copula–DCC–GARCH models for volatility
Economics Letters, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kim, Jong-Min, Jung, Hojin
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Variance Minimization Hedging Analysis Based on a Time-Varying Markovian DCC-GARCH Model
IEEE Transactions on Automation Science and Engineering, 2020Considering time-varying transition probability (TVTP), this article combines Markov regime switching with a dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model to construct a new hedging model and study a state-dependent minimum variance hedging ratio.
Jia Wang +5 more
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Neuroscience Research, 2021
We suggest a time-varying partial correlation as a statistical measure of dynamic functional connectivity (dFC) in the human brain. Traditional statistical models often assume specific distributions on the measured data such as the Gaussian distribution, which prohibits their application to neuroimaging data analysis.
Namgil, Lee, Jong-Min, Kim
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We suggest a time-varying partial correlation as a statistical measure of dynamic functional connectivity (dFC) in the human brain. Traditional statistical models often assume specific distributions on the measured data such as the Gaussian distribution, which prohibits their application to neuroimaging data analysis.
Namgil, Lee, Jong-Min, Kim
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Directional time-varying partial correlation with the Gaussian copula–DCC–GARCH model
Applied Economics, 2018This article suggests a directional time-varying partial correlation based on the dynamic conditional correlation (DCC) method. A recent study proposed the copula DCC based on the vine structure.
Jong-Min Kim, Hojin Jung
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Apple, Microsoft, Amazon and Google - A Correlation Analysis: Evidence from a DCC-GARCH Model
SSRN Electronic Journal, 2020In this paper, we examine time-varying correlations among stock returns of Apple, Microsoft, Amazon and Google. Employing a multivariate DCC-GARCH model, we find that there are strong linkages among these four assets. Starting from lower levels, correlation values for most asset pairs exhibit a stable ascending movement in recent upward trended markets
Christoph Koser, Juergen Klaus
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Volatility Spillover Effects between Indian Stock Market and Global Stock Markets: A DCC-GARCH Model
FIIB Business Review, 2023The present article empirically estimates the volatility spillover transmission in Indian equity market represented by Sensex from world economies composite index (Euro Stoxx 50) using the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model.
Nikhil Yadav +2 more
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Modeling structure of inflation in Türkiye: DCC-GARCH and Markov switching model
Journal of Financial Economic PolicyPurpose Recent surges in inflation have posed significant challenges for Türkiye, with the annualinflation rate culminating at 83.45% by the close of 2022. The purpose of the study is to take a closer look at the details behind the rising inflation trend in Türkiye.
Erginbay Uğurlu +2 more
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Comparison of BEKK GARCH and DCC GARCH Models: An Empirical Study
2010Modeling volatility and co-volatility of a few zero-coupon bonds is a fundamental element in the field of fix-income risk evaluation. Multivariate GARCH model (MGARCH), an extension of the well-known univariate GARCH, is one of the most useful tools in modeling the co-movement of multivariate time series with time-varying covariance matrix. Grounded on
Yiyu Huang, Wenjing Su, Xiang Li
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