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Relevant bibliographies by topics / Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH)

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Academic literature on the topic 'Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH)'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH)"

1

Naifar, Nader. "Exploring the Dynamic Links between GCC Sukuk and Commodity Market Volatility." International Journal of Financial Studies 6, no. 3 (2018): 72. http://dx.doi.org/10.3390/ijfs6030072.

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This study investigates the impact of commodity price volatility (including soft commodities, precious metals, industrial metals, and energy) on the dynamics of corporate sukuk returns. Using a sample of sukuk indices from Gulf Cooperation Council (GCC) countries, we study the dynamic conditional correlation using a multivariate generalized autoregressive conditional heteroskedasticity dynamic conditional correlation (GARCH-DCC) process. Empirical results show a time-varying negative correlation between GCC sukuk returns and commodity prices. In fact, a negative conditional correlation among a

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2

Bangar Raju, Totakura, Ayush Bavise, Pradeep Chauhan, and Bhavana Venkata Ramalingeswar Rao. "Analysing volatility spillovers between grain and freight markets." Pomorstvo 34, no. 2 (2020): 428–37. http://dx.doi.org/10.31217/p.34.2.23.

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The International Grain Council (IGC) circulates two price indices which are the Grain and Oilseeds Index (GOI) and the Grain and Oilseeds Freight Market Index (GOFI). These two indices indicate the respective market prices. The GOI markets are affected by various factors like supply and demand, weather, freight markets, etc. This research article attempts to explore and analyse volatility in GOI and GOFI markets using various GARCH family models, that is Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) analysis. The multivariate Dynamic Conditional Correlation Ge

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3

Chung, Victor, Jenny Espinoza, and Alan Mansilla. "Analysis of Financial Contagion and Prediction of Dynamic Correlations During the COVID-19 Pandemic: A Combined DCC-GARCH and Deep Learning Approach." Journal of Risk and Financial Management 17, no. 12 (2024): 567. https://doi.org/10.3390/jrfm17120567.

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This study aims to combine the use of dynamic conditional correlation multiple generalized autoregressive conditional heteroskedasticity (DCC-GARCH) models and deep learning techniques in analyzing the dynamic correlation between stock markets. First, we examine the contagion effect of the high-risk financial crisis during COVID-19 in the United States on the Latin American stock market using a dynamic conditional correlation approach. The study covers the period from 2014 to 2020, divided into the pre-COVID-19 period (January 2014–February 2020) and the COVID-19 period (March 2020–November 20

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4

Seth, Neha, and Laxmidhar Panda. "Time-varying Correlation Between Indian Equity Market and Selected Asian and US Stock Markets." Global Business Review 21, no. 6 (2019): 1354–75. http://dx.doi.org/10.1177/0972150919856962.

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The purpose of this article is to examine the dynamic relationship between the Indian stock market and the selected Asian and US stock markets during the post-crisis period. This article uses univariate GARCH (Generalized Autoregressive Conditional Heteroskedasticity) family models on daily observations from March 2009 to December 2015 to evaluate the volatility persistence and leverage effect on Asian developed (Japan, Singapore and Hong Kong) and emerging markets (India, China, Indonesia, Korea, Malaysia and Taiwan) along with the US stock market. AR (Autoregressive) ( 1 )-GARCH (1, 1)-ADCC

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5

Ozdurak, Caner, and Cengiz Karatas. "Inflation inertia in Turkish economy: dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) and wavelet analysis." Pressacademia 7, no. 4 (2020): 324–37. http://dx.doi.org/10.17261/pressacademia.2020.1306.

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6

Sun, Xiaochun, Jiaqi Liu, Jihong Zhang, and Chengjun Wang. "The Dynamic Correlation of Stock Markets in the World’s Five Largest Economies—Based on DCC-GARCH Model." International Journal of Economics and Finance 15, no. 3 (2023): 27. http://dx.doi.org/10.5539/ijef.v15n3p27.

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The dynamic correlation of stock markets in various countries has attracted the attention of scholars and financial investors. In this paper, the dynamic conditional correlation model and the generalized autoregressive conditional heteroskedasticity model are combined to analyze the dynamic conditional correlation coefficient matrix of the stock data of China, the United States, Britain, Germany and Japan, aiming at the five indexes of the Shanghai Securities Composite Index, the Dow Jones Index, the Financial Times Stock Exchange 100 Index, the Frankfurt DAX Index and the Nikkei Index. The re

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7

Mohd. Khan, Caroline. "Currency Crises and Commodity Markets: Dynamic Relationships and Implications for Sustainable Investing and International Trade." European Journal of Sustainable Development 14, no. 2 (2025): 861. https://doi.org/10.14207/ejsd.2025.v14n2p861.

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This study examines the inter-relation between WTI crude oil futures prices and two energy-related exchange-traded funds (ETFs): the ETF of iShares Global Clean Energy (Clean Energy) and the ETF of Energy Sector SPDR Fund (Traditional Energy). Using Granger causality tests and the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model, we analyze causal relationships and volatility transmission between these assets. The Granger causality results show that traditional energy markets dominate, while clean energy markets are becoming more influ

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8

Salisu, Abubakar. "Volatility Spill-Over and Financial Contagion Effect of Conventional Stock on Islamic Equity in Nigeria: Evidence from Covid-19." International Journal of Finance and Business Management 2, no. 1 (2024): 29–44. http://dx.doi.org/10.59890/ijfbm.v2i1.1126.

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This study empirically investigated volatility spill-over and financial contagion effect between conventional stocks and shariah compliant equities before and during covid-19 pandemic in Nigeria. Banking and insurance sectoral stocks indices were selected to represent conventional equities while Nigerian stock exchange lotus islamic index represent islamic stocks. Daily closing stock price data from 02-01-2018 to 26-02-2020 for pre-covid and 27-02-2020 to 31-12-2021 for pre-covid and during covid were considered. DCC-GARCH model (Dynamic conditional correlation- Generalized Autoregressive Cond

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9

Salisu, Abubakar. "Volatility Spill-Over and Financial Contagion Effect of Conventional Stock on Islamic Equity in Nigeria: Evidence from Covid-19." Volatility Spill-Over and Financial Contagion Effect of Conventional Stock on Islamic Equity in Nigeria: Evidence from Covid-19 2, Vol. 2 No. 1 (2024): January 2024 (2024): 16. https://doi.org/10.59890/ijfbm.v2i1.1126.

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This study empirically investigated volatility spill-over and financial contagion effect between conventional stocks and shariah compliant equities before and during covid-19 pandemic in Nigeria. Banking and insurance sectoral stocks indices were selected to represent conventional equities while Nigerian stock exchange lotus islamic index represent islamic stocks. Daily closing stock price data from 02-01-2018 to 26-02-2020 for pre-covid and 27-02-2020 to 31-12-2021 for   pre-covid and during covid were considered. DCC-GARCH model (Dynamic conditional correlation- Generalized Autoregressi

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10

Liu, Shuyi. "Dynamic Correlations Between Carbon Futures and Energy Futures Markets." Advances in Economics, Management and Political Sciences 91, no. 1 (2024): 120–29. http://dx.doi.org/10.54254/2754-1169/91/20241086.

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An essential instrument for reducing carbon emissions worldwide is the carbon market. Comprehending the dynamic relationships between carbon and energy futures prices is crucial as carbon trading picks up promote globally. This research examines how two energy futurescrude oil and natural gasinteract with carbon futures in the American and European markets between November 21, 2013, and March 28, 2024. We examine correlations between futures returns across different regions using the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model. Ou

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