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Journal articles 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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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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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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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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11

Danila, Nevi. "Spillover of volatility among financial instruments: ASEAN-5 and GCC market study." PLOS ONE 18, no. 10 (2023): e0292958. http://dx.doi.org/10.1371/journal.pone.0292958.

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The research examines a comovement and spillover of volatility among foreign exchange, conventional and shariah stock markets in Association of South East Asian Nation-5 (ASEAN) countries and Gulf Cooperation Council (GCC) countries. Generalized Autoregressive Conditional Heteroskedasticity—Baba, Engle, Kraft and Kroner (GARCH-BEKK) and Dynamic Conditional Correlation (GARCH-DCC) models are used to capture the correlation and transmission volatility of the markets. The overall results show that both the Shariah and the conventional stock indices respond similarly to each country’s currency. A

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12

Valizdeh Chari, Nima, Hoda Hemmati, and Fereydon Ohadi. "Examining Risk Perception and Cost of Capital in Emerging Market Projects Using the DCC-GARCH Model." Dynamic Management in Business Analysis 3, no. 1 (2024): 258–88. http://dx.doi.org/10.61838/dmbaj.3.1.15.

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Objective: The aim of this study is to accurately estimate the cost of capital and manage risk in energy projects based on public-private partnerships (PPP) in emerging markets. Methodology: To explore the mechanism of determining capital costs and evaluating capital budgeting, Dynamic Conditional Correlation (DCC) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models were employed. Additionally, the pure-play method was used as a tool for measuring systematic risk in energy-related projects in emerging markets. Findings: The results showed that the DCC-GARCH model is ca

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13

Inayah, Asty Khairi, Lesia Fatma Ginoga, Dahri Tanjungan, Resti Jayeng Ramadhanti, and Novi Rosyanti. "Analysis of return and volatility spillover between oil-gold and oil-bitcoin during the covid-19 pandemic." E3S Web of Conferences 577 (2024): 02020. http://dx.doi.org/10.1051/e3sconf/202457702020.

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This study analyzes the return and volatility spillover between oil-gold and oil-Bitcoin pairs before and after the COVID-19 pandemic using the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model. The data used in this research consists of daily returns of oil, gold, and Bitcoin from January 2018 to December 2021 to understand volatility dynamics. The data period is divided into two phases: before and after theCOVID-19 pandemic. The analysis results show no significant volatility spillover between oil andgold. The relationship between oil

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14

Mohamed, Beraich, Amine Fadali Mohamed, and Bakir Yousra. "IMPACT OF THE COVID-19 CRISIS ON THE MOROCCAN STOCK MARKET: MODELING THE VOLATILITY OF THE M.A.S.I STOCK MARKET INDEX." International Journal of Accounting, Finance, Auditing, Management and Economics 2, no. 1 (2021): 100–108. https://doi.org/10.5281/zenodo.4474606.

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The containment measures taken to combat the Covid-19 outbreak caused an economic and financial crisis at the international scale as well as at the national scale. The purpose of this article is to study and analyze the impact of this pandemic crisis on the Moroccan stock market and to show to what extent the containment decisions have negatively impacted the performance of the stock market. We proposed an approach that introduces the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to estimate the volatility of the Moroccan All-Share

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15

Hema, Saini. "Volatility Spillover Among Sectoral Indices of the Indian and US Stock Markets." International Journal of Management and Humanities (IJMH) 11, no. 9 (2025): 11–17. https://doi.org/10.35940/ijmh.G1801.11090525.

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<strong>Abstract:</strong> The main aim of this study is to empirically analyze the volatility spillover among the sectoral equity returns for Indian and US markets. Utilizing the Dynamic Conditional Correlation model, the paper extracts the time‐varying conditional correlations between the sector indices. The analysis of the DCC-GARCH model indicates a conditional correlation between the Indian and US stock markets. Furthermore, despite market volatility and a significant disruption caused by the COVID-19 crisis in 2019, the consistent presence of a positive correlation highlights the strong

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16

Hema, Saini. "Volatility Spillover Among Sectoral Indices of the Indian and US Stock Markets." International Journal of Management and Humanities (IJMH) 11, no. 9 (2025): 11–17. https://doi.org/10.35940/ijmh.G1801.11090525/.

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<strong>Abstract: </strong>The main aim of this study is to empirically analyze the volatility spillover among the sectoral equity returns for Indian and US markets. The paper extracts the time‐varying conditional correlations between the sector indices using the Dynamic Conditional Correlation model. The analysis of the DCC-GARCH model indicates a conditional correlation between the Indian and US stock markets. Furthermore, despite market volatility and a significant disruption caused by the COVID-19 crisis in 2019, the consistent presence of a positive correlation highlights the strong and l

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17

Li, Shuping, Xinghua Liu, and Chongren Wang. "The Influence of Internet Finance on the Sustainable Development of the Financial Ecosystem in China." Sustainability 12, no. 6 (2020): 2365. http://dx.doi.org/10.3390/su12062365.

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As a new species in the financial ecosystem, internet finance has significantly impacted traditional finance and has improved the diversity and ended the long-term stability of the financial ecosystem. From the perspective of the interaction between the ecological subjects of the Internet and traditional finance, this study examines the influence of internet finance on the sustainability of the financial ecosystem in China. We tested the dynamic correlation and risk transmission at the volatility level between the ecological subjects of internet finance and the banking, securities, and insuran

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18

Hou, Yang, and Steven Li. "Volatility behaviour of stock index futures in China: a bivariate GARCH approach." Studies in Economics and Finance 32, no. 1 (2015): 128–54. http://dx.doi.org/10.1108/sef-10-2013-0158.

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Purpose – This paper aims to investigate the volatility transmission and dynamics in China Securities Index (CSI) 300 index futures market. Design/methodology/approach – This paper applies the bivariate Constant Conditional Correlation (CCC) and Dynamic Conditional Correlation (DCC) Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models using high frequency data. Estimates for the bivariate GARCH models are obtained by maximising the log-likelihood of the probability density function of a conditional Student’s t distribution. Findings – This empirical analysis yields a few in

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19

Nittayakamolphun, Pitipat, Wiwatwong Bunnun, Nathaporn Phong-a-ran, Raweepan Uttarin, and Panjamapon Pholkerd. "Thailand Sustainability Investment Performance on Thailand’s Stock Market and Financial Assets." International Journal of Financial Studies 13, no. 2 (2025): 71. https://doi.org/10.3390/ijfs13020071.

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Extreme weather events are the primary driver of environmental, social, and governance (ESG) responsible investment or sustainable stocks, which are gaining popularity worldwide, including in Thailand. Nevertheless, the function of sustainable stocks remains an academic dispute and without satisfactory conclusion for decision-making of Thai investors. Thus, we adopt a dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model to examine the influence of Thailand sustainability investment on Thailand’s stock market and financial assets. The resul

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20

Katusiime, Lorna. "Investigating Spillover Effects between Foreign Exchange Rate Volatility and Commodity Price Volatility in Uganda." Economies 7, no. 1 (2018): 1. http://dx.doi.org/10.3390/economies7010001.

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This study investigates the impact of commodity price volatility spillovers on financial sector stability. Specifically, the study investigates the spillover effects between oil and food price volatility and the volatility of a key macroeconomic indicator of importance to financial stability: the nominal Uganda shilling per United States dollar (UGX/USD) exchange rate. Volatility spillover is examined using the Generalized Vector Autoregressive (GVAR) approach and Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) techniques, namely the dynamic conditional correlat

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21

Sianturi, Danny Jubel Abrian. "FLUCTUATING COMMODITY PRICES' EFFECT ON INDONESIAN COAL AND PALM OIL." EKUITAS (Jurnal Ekonomi dan Keuangan) 8, no. 1 (2024): 67–84. http://dx.doi.org/10.24034/j25485024.y2024.v8.i1.5916.

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Studi ini mengkaji dampak fluktuasi harga komoditas terhadap volatilitas saham dan kinerja keuangan perusahaan batubara dan kelapa sawit Indonesia antara tahun 2011 dan 2022. Selama pandemi COVID-19, sektor-sektor ini sangat dipengaruhi oleh fluktuasi harga komoditas. Dengan menggunakan pendekatan Vector Error Correction Model (VECM) dan Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC GARCH), serta regresi panel, penelitian ini menganalisis volatilitas harga dan pengaruhnya terhadap kinerja keuangan. Temuan mengungkapkan pola volatilitas yang berb

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22

Umar, Saminu, and Gafar M. Oyeyemi. "A Hybrid LSTM-DCC Model for Multivariate Cryptocurrency Volatility Prediction." Asian Journal of Probability and Statistics 27, no. 7 (2025): 179–91. https://doi.org/10.9734/ajpas/2025/v27i7784.

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Accurate volatility forecasting remains a central challenge in the analysis of cryptocurrency markets, where extreme price fluctuations, nonlinear dependencies and evolving cross-asset correlations complicate traditional modeling approaches. This study proposes a hybrid framework that integrates the Dynamic Conditional Correlation (DCC) Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) model with Long Short-Term Memory (LSTM) networks to enhance forecasting accuracy. The LSTM–DCC model improves the representation of volatility clustering, structural breaks and int

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23

Ba, Xuezhen, Xizhao Wang, and Yu Zhong. "The Impact of Federal Reserve Monetary Policy on Commodity Prices: Evidence from the U.S. Dollar Index and International Grain Futures and Spot Markets." Agriculture 15, no. 9 (2025): 923. https://doi.org/10.3390/agriculture15090923.

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There is a strong connection between the Federal Reserve’s monetary policy and the trend of international food prices. Employing the average information share model, EGARCH(Exponential Generalized Autoregressive Conditional Heteroskedasticity), and DCC-MGARCH(Dynamic Conditional Correlation-Multivariate Generalized Autoregressive Conditional Heteroskedasticity) models, this study investigates the relationship between the U.S. dollar index, international grain futures prices, and spot prices in the context of Federal Reserve monetary policy adjustments from 2000 to 2023. The findings reveal tha

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Nuzula Agustin, Isnaini, Hesniati, and Estin Rose Eviyani. "Uncovering dynamic relationships across sustainable-ethical financial assets: A new outlook from Indonesia." Investment Management and Financial Innovations 22, no. 2 (2025): 385–96. https://doi.org/10.21511/imfi.22(2).2025.30.

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Against the rapid developments in cross-border investment that shed light on portfolio diversification opportunities, this study investigates the relationship between Indonesia’s sustainable ethical stocks and global sustainable financial assets. Amid market uncertainty, the need for safe havens and diversification of investment portfolios is imperative. Given the remarkable performance of Indonesia’s Islamic stocks, which are considered ethical stock, and the importance of sustainable stocks, this study examines how global financial assets such as Green Bonds, Artificial Intelligence (AI) sto

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25

Qin, Yuxin. "A Study of the Impact of Investor Sentiment on Stock Investment Returns." Advances in Economic Development and Management Research 1, no. 3 (2024): 81. http://dx.doi.org/10.61935/aedmr.3.1.2024.p81.

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This study examines the influence of investor sentiment on stock investment returns in China's rapidly developing yet volatile securities market. Utilizing principal component analysis, a comprehensive investor sentiment index is constructed from six key indicators. The dynamic relationship between investor sentiment and stock market return volatility is explored through a Vector Autoregression (VAR) model and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The findings reveal a significant bidirectional correlation, with positive optimism exerting a greater impact on

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26

Su, Jung-Bin, and Jui-Cheng Hung. "The Value-At-Risk Estimate of Stock and Currency-Stock Portfolios’ Returns." Risks 6, no. 4 (2018): 133. http://dx.doi.org/10.3390/risks6040133.

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This study utilizes the seven bivariate generalized autoregressive conditional heteroskedasticity (GARCH) models to forecast the out-of-sample value-at-risk (VaR) of 21 stock portfolios and seven currency-stock portfolios with three weight combinations, and then employs three accuracy tests and one efficiency test to evaluate the VaR forecast performance for the above models. The seven models are constructed by four types of bivariate variance-covariance specifications and two approaches of parameters estimates. The four types of bivariate variance-covariance specifications are the constant co

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Ji, Xiuping, Sujuan Wang, Honggen Xiao, Naipeng Bu, and Xiaonan Lin. "Contagion Effect of Financial Markets in Crisis: An Analysis Based on the DCC–MGARCH Model." Mathematics 10, no. 11 (2022): 1819. http://dx.doi.org/10.3390/math10111819.

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Global crises have created unprecedented challenges for communities and economies across the world, triggering turmoil in global finance and economy. This study adopts the dynamic conditional correlation multiple generalized autoregressive conditional heteroskedasticity (DCC–MGARCH) model to explore contagion effects across financial markets in crisis. The main findings are as follows: (1) the financial crisis and COVID-19 pandemic intensified the connection between the Chinese and US stock markets in the short term; (2) the dynamic conditional correlations (DCCs) during the COVID-19 pandemic

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Cebrián-Hernández, Ángeles, and Enrique Jiménez-Rodríguez. "Modeling of the Bitcoin Volatility through Key Financial Environment Variables: An Application of Conditional Correlation MGARCH Models." Mathematics 9, no. 3 (2021): 267. http://dx.doi.org/10.3390/math9030267.

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Since the launch of Bitcoin, there has been a lot of controversy surrounding what asset class it is. Several authors recognize the potential of cryptocurrencies but also certain deviations with respect to the functions of a conventional currency. Instead, Bitcoin’s diversifying factor and its high return potential have generated the attention of portfolio managers. In this context, understanding how its volatility is explained is a critical element of investor decision-making. By modeling the volatility of classic assets, nonlinear models such as Generalized Autoregressive Conditional Heterosk

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Adailah, Radi Mohammad, Saba Bassam Al-Damour, and Ahmad Al-Majali. "The Impact of Global Energy Price Volatility on Oil Derivative and Local Price in Jordan: Using DCC-GARCH Model." International Journal of Energy Economics and Policy 14, no. 1 (2024): 336–48. http://dx.doi.org/10.32479/ijeep.15158.

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The central aim of this study is to evaluate the repercussions of global energy price fluctuations on the pricing of local oil derivatives in Jordan, and their subsequent impact on domestic price levels. This research seeks to propose risk mitigation strategies to address the challenges posed by energy price volatility, offering valuable insights for Jordanian policymakers. Given the dearth of understanding among policymakers and industry stakeholders regarding the economic ramifications of global energy prices on local markets, this study posits a statistically significant relationship, at a

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Raji, Jimoh Olajide, Rihanat Idowu Abdulkadir, and Bazeet Olayemi Badru. "Dynamic relationship between Nigeria-US exchange rate and crude oil price." African Journal of Economic and Management Studies 9, no. 2 (2018): 213–30. http://dx.doi.org/10.1108/ajems-06-2017-0124.

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Purpose The purpose of this paper is to investigate the dynamic relationship between Nigeria-US exchange rate (XR) and crude oil price (OILP) using daily data from 1 January 2001 to 31 December 2015. Design/methodology/approach The study uses alternative methods, including vector autoregressive-generalised autoregressive conditional heteroskedasticity (VAR-GARCH) within the framework of Baba-Engle-Kraft-Kroner model, constant conditional correlation (CCC)-GARCH and dynamic conditional correlation (DCC)-GARCH models. Findings The results from the VAR-GARCH model indicate unidirectional cross-ma

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Kostika, Eleftheria, and Nikiforos T. Laopodis. "Dynamic linkages among cryptocurrencies, exchange rates and global equity markets." Studies in Economics and Finance 37, no. 2 (2019): 243–65. http://dx.doi.org/10.1108/sef-01-2019-0032.

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Purpose The purpose of this paper is to investigate the short- and long-run dynamic linkages between selected cryptocurrencies, several major world currencies and major equity indices. The results show that despite sharing some common characteristics, the cryptocurrencies do not reveal any short- and long-term stochastic trends with exchange rates and/or equity returns. The dynamics of each cryptocurrency with the Chinese Yuan appears to be more turbulent than that with the other exchange rates. Each cryptocurrency appears to follow its own trend in the global financial market and is independe

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Cui, Jinyang. "The Relationship between the Gold Price, Crude Oil Price, Exchange Rate and Chinese Stock Market Indexes." Highlights in Business, Economics and Management 10 (May 9, 2023): 180–88. http://dx.doi.org/10.54097/hbem.v10i.8037.

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One of the trickiest problems for investors is how the financial and commodity markets interact with each other. The volatility in one market might affect the price of the other market. This essay aims to clarify the relationship between gold, crude oil, exchange rates, and Chinese stock market indices. In order to do this, the Shanghai Stock Exchange Index and the China Industrial Index, two indices that reflect the Chinese financial market, were subjected to the DCC-GARCH model (Generalized Autoregressive Conditional Heteroskedasticity Model). By capturing the dynamic correlations of the tim

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Just, Małgorzata, and Aleksandra Łuczak. "Assessment of Conditional Dependence Structures in Commodity Futures Markets Using Copula-GARCH Models and Fuzzy Clustering Methods." Sustainability 12, no. 6 (2020): 2571. http://dx.doi.org/10.3390/su12062571.

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The dynamic development of commodity derivatives markets has been observed since the mid-2000s. It is related to the development of e-commerce, the inflow of financial investors’ capital, and the emergence of exchange-traded funds and passively managed index funds focused on commodities. These advances are accompanied by changes in dependence structure in the markets. The main purpose of this study is to assess the conditional dependence structure in various commodity futures markets (energy, metals, grains and oilseeds, soft commodities, agricultural commodities) in the period from the beginn

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Kampman, Onno P., Joe Ziminski, Soroosh Afyouni, Mark van der Wilk, and Zoe Kourtzi. "Time-varying functional connectivity as Wishart processes." Imaging Neuroscience 2 (June 2024): 1–28. http://dx.doi.org/10.1162/imag_a_00184.

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Abstract We investigate the utility of Wishart processes (WPs) for estimating time-varying functional connectivity (TVFC), which is a measure of changes in functional coupling as the correlation between brain region activity in functional magnetic resonance imaging (fMRI). The WP is a stochastic process on covariance matrices that can model dynamic covariances between time series, which makes it a natural fit to this task. Recent advances in scalable approximate inference techniques and the availability of robust open-source libraries have rendered the WP practically viable for fMRI applicatio

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Abdulai, M. G., A. Salakpi, and I. Mahama. "Time-Varying Connectedness Between Global Uncertainties and Economic Activity in a Developing Economy Using a Dynamic Conditional Correlation — GARCH Model." Review of Business and Economics Studies 12, no. 4 (2025): 106–20. https://doi.org/10.26794/2308-944x-2024-12-4-106-120.

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As economies become increasingly interconnected, individual economies are at risk of shocks from external uncertainties ranging from fluctuations in climate regulations to geopolitical conflicts and international economic policies.The purpose of the study is to investigate the time-varying correlation between global uncertainties (e. g., global economic policy uncertainty, climate policy uncertainty and geopolitical risk) and economic activity in a developing economy using a dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) model.The relevance of

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Buthelezi, Eugene Msizi. "Assessing the impact of fiscal consolidation uncertainty on South Africa’s foreign debt." Journal of Economic Studies 52, no. 9 (2025): 111–45. https://doi.org/10.1108/jes-05-2024-0340.

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PurposeThis study aims to investigate the impact of fiscal consolidation uncertainty on South Africa’s total foreign debt from the first quarter of 1960 to the third quarter of 2022. It seeks to fill a gap in the literature by analyzing how uncertainty in government budget indicators influences foreign debt dynamics.Design/methodology/approachTo achieve this objective, the study employs time-varying conditional autoregressive (ARCH), generalized autoregressive conditional heteroskedasticity (GARCH) models and Markov-switching dynamic regression. These econometric techniques are utilized to exa

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YIN, Kedong, Zhe LIU, and Peide LIU. "TREND ANALYSIS OF GLOBAL STOCK MARKET LINKAGE BASED ON A DYNAMIC CONDITIONAL CORRELATION NETWORK." Journal of Business Economics and Management 18, no. 4 (2017): 779–800. http://dx.doi.org/10.3846/16111699.2017.1341849.

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The paper analyses the trend of global stock market linkages via daily data of 51 stock indices spanning the period 22 July 2005 to 30 June 2016 which covers four regions: America, Europe, Asia Pacific and Africa. A dynamic conditional multivariate generalized autoregressive conditional heteroskedasticity (DCC-MVGARCH) approach was used to calculate dynamic correlation coefficient in order to construct the volatility networks. The methods of minimum spanning tree (MST) and low pass filter were for the first time applied to analyze the variable periodicity of the comovement. The original contri

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Maharana, Narayana, Ashok Kumar Panigrahi, and Suman Kalyan Chaudhury. "Commodity Spillovers and Risk Hedging: The Evolving Role of Gold and Oil in the Indian Stock Market." Commodities 4, no. 2 (2025): 5. https://doi.org/10.3390/commodities4020005.

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This study examines the volatility and hedging effectiveness of commodities, specifically gold and oil, on the Indian stock market, focusing on both aggregate and sectoral indices. Data have been collected from 1 January 2021 to 31 December 2024 to cover the post-COVID-19 period. Utilizing the Asymmetric Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (ADCC-GARCH) model, we analyze the volatility spillovers and time-varying correlations between commodity and stock market returns. The analysis of spillover connectedness reveals that both commodities exh

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Nguyen, Canh Phuc, Thanh Dinh Su, Udomsak Wongchoti, and Christophe Schinckus. "The spillover effects of economic policy uncertainty on financial markets: a time-varying analysis." Studies in Economics and Finance 37, no. 3 (2020): 513–43. http://dx.doi.org/10.1108/sef-07-2019-0262.

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Purpose This study aims to examine the spillover effects of trans-Atlantic macroeconomic uncertainties on the local stock market returns in the USA and eight selected European countries, namely, Germany, France, Spain, Italy, Greece, Ireland, Sweden and the UK, during the 2000-2019 period. Design/methodology/approach This paper applies the dynamic conditional correlation multivariate GARCH model (i.e. multivariate generalized autoregressive conditional heteroskedasticity model or DCC MGARCH) to examine the potential existence of the spillover from the uncertainty of the USA to EU stock markets

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Lawal, Adedoyin Isola, Ezeikel Oseni, Adel Ahmed, Hosam Alden Riyadh, Mosab I. Tabash, and Dominic T. Abaver. "Analysing Rational Bubbles in African Stock Markets: Evidence from Econophysics Frequency Domain Estimates and DCC MGARCH Model." Economies 12, no. 8 (2024): 217. http://dx.doi.org/10.3390/economies12080217.

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The stock market operates on informed decisions based on information gathered from heterogeneous sources, encompassing diverse beliefs, strategies, and knowledge. This study examines the validity of rational bubbles in stock market prices, focusing on eight African stock markets: South Africa, Nigeria, Kenya, Egypt, Morocco, Mauritius, Ghana, and Botswana. Utilizing newly developed econophysics-based unit root tests and the Dynamic Conditional Correlation Multivariate Generalized Autoregressive Conditional Heteroskedasticity (DCC MGARCH) models, the authors analyzed daily data from 1996 to 202

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Tian, Haocheng. "Research on macroeconomic indicators and stock market correlation analysis based on machine learning." Applied and Computational Engineering 87, no. 1 (2024): 179–84. http://dx.doi.org/10.54254/2755-2721/87/20241611.

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The stock market is known as the barometer of the national economy, and macroeconomic factors have an important impact on the volatility of the stock market. Therefore, considering the impact of macroeconomic factors on the sustainability of stock market volatility will help to capture the time-varying characteristics of volatility persistence, so as to significantly improve the estimation and forecasting effect of volatility. In this work, the generalized autoregressive conditional heteroskedasticity-mixing data sampling (GARCH-MIDAS) model is adopted, which combines the advantages of the GAR

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Gunay, Samet, Walid Bakry, and Somar Al-Mohamad. "The Australian Stock Market’s Reaction to the First Wave of the COVID-19 Pandemic and Black Summer Bushfires: A Sectoral Analysis." Journal of Risk and Financial Management 14, no. 4 (2021): 175. http://dx.doi.org/10.3390/jrfm14040175.

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In this study, we investigated the impact of the first wave of the COVID-19 pandemic on various sectors of the Australian stock market. Market capitalization and equally weighted indices were formed for eleven Australian sectors to examine the influence of the pandemic on them. First, we examined the financial contagion between the Chinese stock market and Australian sector indices through the dynamic conditional correlation fractionally integrated generalized autoregressive conditional heteroskedasticity (DCC-FIGARCH) model. We found high time-varying correlations between the Chinese stock ma

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Singh, Amanjot, and Manjit Singh. "A revisit to how linkages fuel dependent economic policy initiatives." International Journal of Law and Management 59, no. 6 (2017): 1068–108. http://dx.doi.org/10.1108/ijlma-08-2016-0074.

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Purpose The authors aim to report empirical linkages between the US and Brazil, Russia, India and China (BRIC) financial stress indices catalyzing catalyzing dependent economic policy initiatives (an extended version of Singh and Singh, 2017a). Design/methodology/approach Initially, the study develops financial stress indices for the respective BRIC financial markets. Later, it captures linkages among the said US-BRIC indices by using Johansen cointegration, vector autoregression/vector error correction models (VECM), generalized impulse response functions, Toda–Yamamoto Granger causality, var

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Narayan, Seema. "The Influence of Domestic and Foreign Shocks on Portfolio Diversification Gains and the Associated Risks." Journal of Risk and Financial Management 12, no. 4 (2019): 160. http://dx.doi.org/10.3390/jrfm12040160.

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This paper evaluates the influence of foreign or domestic stock market return and return of volatility shocks on dynamic conditional correlations (DCCs) between international stock markets and correlation volatility, respectively. The correlations between markets have implications for the gains from portfolio diversification, while correlation volatilities can be seen as risks to portfolio diversification. Meanwhile, domestic shocks are sourced from the return and return volatility from 24 developed, emerging, and frontier stock markets. The US stock market is the source of foreign shocks. The

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Volokhin, E. A., and S. P. Syrygin. "THE IMPACT OF MACROECONOMIC FACTORS ON THE SYSTEMATIC RISK OF THE RUSSIAN STOCK MARKET." Social’no-ekonomiceskoe upravlenie: teoria i praktika 21, no. 1 (2025): 18–28. https://doi.org/10.22213/2618-9763-2025-1-18-28.

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The article is dedicated to the characteristics of systematic risk in the Russian stock market. It provides an analysis of macroeconomic factors influencing the Russian stock market, highlighting the causal relationships between economic factors and market dynamics. These factors include exchange rates, oil prices, inflation, the monetary policy of the Central Bank of Russia, the M2 money supply, and geopolitical factors. The sample of daily data covers the period from December 13, 2019, to May 6, 2024. Using the Asymmetric Generalized Autoregressive Conditional Heteroskedasticity model with D

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Andriychuk, Sergiy. "CRYPTOCURRENCY VOLATILITY AND RISK MODELING: MONTE CARLO SIMULATIONS, GARCH ANALYSIS, AND FINANCIAL MARKET INTEGRATION." Economics, Finance and Management Review, no. 1(21) (March 31, 2025): 98–115. https://doi.org/10.36690/2674-5208-2025-1-98-115.

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Cryptocurrencies have rapidly emerged as a significant financial asset class, influencing global monetary systems and financial markets. However, their extreme volatility, speculative nature, and evolving regulatory landscape pose challenges to investors, policymakers, and financial analysts. This study presents an in-depth quantitative analysis of cryptocurrency volatility and risk assessment, focusing on Bitcoin (BTC-USD) and its correlation with traditional financial assets, including the EUR/USD exchange rate and S&P 500 index. Our research employs Generalized Autoregressive Conditiona

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Lyu, Jingye, and Zimeng Li. "Time-Varying Spillover Effects of Carbon Prices on China’s Financial Risks." Systems 12, no. 12 (2024): 534. http://dx.doi.org/10.3390/systems12120534.

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As China’s financial markets become increasingly integrated and the carbon market undergoes financialization, the impact of carbon emission price fluctuations on financial markets has emerged as a key area of systemic risk research. This study employs the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model and the optimal Copula function to investigate the dynamic correlation between carbon prices and China’s financial markets. Building on this, the Monte Carlo simulation and Copula CoVaR models are used to explore the spillover effects of carbon price volatility on China’s

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Afzal, Fahim, Pan Haiying, Farman Afzal, Asif Mahmood, and Amir Ikram. "Value-at-Risk Analysis for Measuring Stochastic Volatility of Stock Returns: Using GARCH-Based Dynamic Conditional Correlation Model." SAGE Open 11, no. 1 (2021): 215824402110057. http://dx.doi.org/10.1177/21582440211005758.

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To assess the time-varying dynamics in value-at-risk (VaR) estimation, this study has employed an integrated approach of dynamic conditional correlation (DCC) and generalized autoregressive conditional heteroscedasticity (GARCH) models on daily stock return of the emerging markets. A daily log-returns of three leading indices such as KSE100, KSE30, and KSE-ALL from Pakistan Stock Exchange and SSE180, SSE50 and SSE-Composite from Shanghai Stock Exchange during the period of 2009–2019 are used in DCC-GARCH modeling. Joint DCC parametric results of stock indices show that even in the highly volat

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Tiara Kania Ladzuardini. "Volatilitas Imbal Hasil Saham dan Kaitannya dengan Harga Minyak Dunia (Pendekatan Model ARCH/GARCH dan VAR)." JURNAL RISET MANAJEMEN DAN EKONOMI (JRIME) 1, no. 4 (2023): 97–116. http://dx.doi.org/10.54066/jrime-itb.v1i4.723.

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Volatility generally refers to the amount of uncertainty or risk associated with changes in a security's value. The value of a security can potentially spread over a wider range of values if it has higher volatility. This study aims to analyze the volatility of stock returns and the growth of world oil prices. The stock that the author analyzes in this scientific article is PT Pakuwon Jati Tbk. by using daily time series data from January 2 2019 to December 14 2020. The model used in this study is Autoregressive Conditional Heteroskedasticity (ARCH)/Generalized Autoregressive Conditional Heter

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Li, Xuedi, Jie Ma, Zhu Chen, and Haitao Zheng. "Linkage Analysis among China’s Seven Emissions Trading Scheme Pilots." Sustainability 10, no. 10 (2018): 3389. http://dx.doi.org/10.3390/su10103389.

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This paper focuses on the time-varying correlation among China’s seven emissions trading scheme markets. Correlation analysis shows a weak connection among these markets for the whole sample period, which spans from 9 June 2014 to 30 June 2017. The return rate series of the seven markets show the characteristics of a fat-tailed and skewed distribution, and the Vector Autoregression (VAR) residuals present a significant Autoregressive Conditional Heteroscedasticity (ARCH) effect. Therefore, we adopt Vector Autoregression Generalized ARCH model with Dynamic Conditional Correlation (VAR-DCC-GARCH

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