Relevant bibliographies by topics / DCC GARCH model

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  1. Journal articles
  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'DCC GARCH model'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 June 2025

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Journal articles on the topic "DCC GARCH model"

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Baumöhl, Eduard, Mária Farkašovská, and Tomáš Výrost. "Stock Market Integration: DCC MV-GARCH Model." Politická ekonomie 58, no. 4 (2010): 488–503. http://dx.doi.org/10.18267/j.polek.743.

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Kim, Jong-Min, Seong-Tae Kim, and Sangjin Kim. "On the Relationship of Cryptocurrency Price with US Stock and Gold Price Using Copula Models." Mathematics 8, no. 11 (2020): 1859. http://dx.doi.org/10.3390/math8111859.

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This paper examines the relationship of the leading financial assets, Bitcoin, Gold, and S&P 500 with GARCH-Dynamic Conditional Correlation (DCC), Nonlinear Asymmetric GARCH DCC (NA-DCC), Gaussian copula-based GARCH-DCC (GC-DCC), and Gaussian copula-based Nonlinear Asymmetric-DCC (GCNA-DCC). Under the high volatility financial situation such as the COVID-19 pandemic occurrence, there exist a computation difficulty to use the traditional DCC method to the selected cryptocurrencies. To solve this limitation, GC-DCC and GCNA-DCC are applied to investigate the time-varying relationship among Bitcoin, Gold, and S&P 500. In terms of log-likelihood, we show that GC-DCC and GCNA-DCC are better models than DCC and NA-DCC to show relationship of Bitcoin with Gold and S&P 500. We also consider the relationships among time-varying conditional correlation with Bitcoin volatility, and S&P 500 volatility by a Gaussian Copula Marginal Regression (GCMR) model. The empirical findings show that S&P 500 and Gold price are statistically significant to Bitcoin in terms of log-return and volatility.
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Chaiyawat, Thitivadee, and Pannarat Guayjarernpanishk. "Effective Forecasting of Insurer Capital Requirements: ARMA-GARCH, ARMA-GARCH-EVT, and DCC-GARCH Approaches." Emerging Science Journal 8, no. 6 (2024): 2173–96. https://doi.org/10.28991/esj-2024-08-06-03.

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This research paper presents a comprehensive analysis of three prominent volatility and dependence models for financial time series: ARMA-GARCH, GARCH-EVT, and DCC-GARCH. These models are employed to assess and forecast capital requirements for life and non-life insurer investments. This study evaluates the models' performance in forecasting Value-at-Risk, using daily data on key Thai financial indicators (representing permissible insurer investment assets) from March 2009 to March 2024. Specifically, 1-day and 10-day VaR forecasts are generated using the ARMA-GARCH and DCC-GARCH models, while the ARMA-GARCH-EVT model is employed for 1-day VaR forecasting. Our findings indicate that the ARMA-GARCH model effectively captures time-varying volatility, while the GARCH-EVT approach enhances tail risk estimation, particularly relevant for stress testing. Additionally, the DCC-GARCH model allows for the examination of dynamic conditional correlations between assets, providing insights into portfolio diversification benefits. Rigorous backtesting procedures, employing Kupiec and Christoffersen tests with a rolling window of 1,000 out-of-sample observations, confirm that the majority of models accurately forecast VaR at their respective horizons, with only a very small subset of 10-day VaR models exhibiting limitations. These results highlight that ARMA-GARCH, ARMA-GARCH-EVT, and DCC-GARCH models offer insurers robust tools for estimating minimum capital requirements, forecasting investment risk, and guiding strategic asset allocation decisions. This research underscores the effectiveness of these models for practical application in the insurance industry while also emphasizing the importance of continued model validation, particularly for extended forecasting horizons. Doi: 10.28991/ESJ-2024-08-06-03 Full Text: PDF
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Trifonov, Ju S., and B. S. Potanin. "Multivariate Asymmetric GARCH Model with Dynamic Correlation Matrix." Finance: Theory and Practice 26, no. 2 (2022): 204–18. http://dx.doi.org/10.26794/2587-5671-2022-26-2-204-218.

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This study examines the problem of modeling the joint dynamics of conditional volatility of several financial assets under an asymmetric relationship between volatility and shocks in returns (leverage effect). We propose a new multivariate asymmetric conditional heteroskedasticity model with a dynamic conditional correlation matrix (DCC-EGARCH). The proposed method allows modelling the joint dynamics of several financial assets taking into account the leverage effect in the financial markets. DCC-EGARCH model has two main advantages over previously proposed multivariate asymmetric specifications. It involves a substantially simpler optimization problem and does away with the assumption of conditional correlation time invariance. These features make the model more suitable for practical applications. To study the properties of the obtained estimators, we conducted a simulated data analysis. As a result, we found statistical evidence in favor of the developed DCC-EGARCH model compared with the symmetric DCC-GARCH process in case of considering data with the presence of the leverage effect. Further, we applied the proposed method to analyze the joint volatility of Rosneft stock returns and Brent oil prices. By estimating the DCC-EGARCH model, we found statistical evidence for both the presence of the leverage effect in the oil price data and the presence of the dynamic correlation structure between the time series, which motivates the practical application of the proposed method.
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Fałdziński, Marcin, and Michał Bernard Pietrzak. "The Multivariate DCC-GARCH Model with Interdependence among Markets in Conditional Variances’ Equations." Przegląd Statystyczny 62, no. 4 (2015): 397–413. http://dx.doi.org/10.5604/01.3001.0014.1763.

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The article seeks to investigate the issue of interdependence that during crisis periods in the capital markets is of particular importance due to the likelihood of causing a crisis in the real economy. The research objective of the article is to identify this interdependence in volatility. Therefore, first we propose our own modification of the DCC-GARCH model which is so designed as to test for interdependence in conditional variance. Then, the DCC-GARCH-In model was used to study interdependence in volatility of selected stock market indices. The results of the research confirmed the presence of interdependence among the selected markets.
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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 volatile stock markets, the bivariate time-varying DCC model provides better performance than traditional VaR models. Thus, the parametric results in the DCC-GRACH model indicate the effectiveness of the model in the dynamic stock markets. This study is helpful to the stockbrokers and investors to understand the actual behavior of stocks in dynamic markets. Subsequently, the results can also provide better insights into forecasting VaR while considering the combined correlational effect of all stocks.
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Moiseev, N. A., and G. V. Aivazian. "Algorithm of Assessing Dynamic Correlation between Time Series Connected by TVP-Regression Model." Vestnik of the Plekhanov Russian University of Economics, no. 3 (May 15, 2025): 34–40. https://doi.org/10.21686/2413-2829-2025-3-34-40.

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The present research proposes algorithm of assessing dynamic correlation of time series connected by TVP-regression model. Topicality of this task is stipulated by the fact that this model often describes asset behavior on finance markets, while modeling of their correlation link over time could help take into account risks, which is an integral part of building strategy of shaping the investment portfolio. This methodology can also be used to study the effect of shock proliferation on finance markets in time of crises. The goal of the research is to assess efficiency of the algorithm described in the work in comparison with the classic algorithm DCC GARCH. Comparison of the present algorithm with DCC GARCH method was carried out on synthetic data with several values of process error dispersion. As a result with all considered values of dispersion of the process error the advanced algorithm showed best figures in terms of mean-square error of assessed and real correlation. However, it was noticed that for higher values of process error the difference in result obtained by advanced algorithm and DCC GARCH method drops. In conclusion certain drawbacks of the algorithm were shown.
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Syrygin, S. P., and E. A. Volokhin. "ASSESSMENT OF INTERTEMPORAL SYSTEMATIC RISK ON THE EXAMPLE OF THE RUSSIAN STOCK MARKET." Social’no-ekonomiceskoe upravlenie: teoria i praktika 20, no. 4 (2024): 52–63. https://doi.org/10.22213/2618-9763-2024-4-52-63.

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The article is dedicated to the study of systematic risk using the Russian stock market as an example. It proposes assessing risk based on intertemporal dynamic beta using modern multivariate models such as DCC-GARCH, GJR-DCC-GARCH, and ADCC-GARCH. The analysis relies on daily returns of the MSCI World index, the main index, and eight sectoral indices of the Moscow Exchange from December 13, 2019, to June 5, 2024. The primary data consisted of daily return time series of the MSCI World Index, the main index, and eight sectoral indices of the Moscow Exchange over the period from December 13, 2019, to June 5, 2024. GARCH model construction for the return series revealed heteroscedasticity, stationarity, and deviations from a normal distribution. Based on a comparison of models with normal and Student's t-distribution in terms of predictive accuracy using cross-validation and the Diebold-Mariano test, the ADCC-GARCH model with a normal distribution was identified as providing the most accurate forecast. Through comparison of the Akaike information criterion and log-likelihood among models, GJR-DCC-GARCH was determined to be the most accurate. Analysis of the ADCC-GARCH model indicated that transport and financial indices are most susceptible to recent shocks and negative news, while the telecommunications sector is the least sensitive. The ADCC-GARCH model found that the electricity sector index has the highest conditional correlation sensitivity to the global market, while the transport index exhibits long-term correlation “memory.” Based on the descriptive statistics of Moscow Exchange beta indices and the Jarque-Bera test, the oil and gas index showed the least extreme fluctuations. Visual analysis of MSCI World and Moscow Exchange index returns revealed a declining trend in correlation between the global and Russian stock markets, indicating a deglobalization of the Russian economy.
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Wei, Ching-Chun. "Empirical Analysis of “Volatilitysurprise” between Dollar Exchange Rate and CRB Commodity Future Markets." International Journal of Economics and Finance 8, no. 9 (2016): 117. http://dx.doi.org/10.5539/ijef.v8n9p117.

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This paper used the five multivariate GARCH models (including BEKK, CCC, DCC, VARMA-CCC and VARMA-DCC) to analyze the mean and volatility interaction of volatility surprise between US dollar exchange and CRB future index (including agricultural, energy, commodity and precious metal equity index). The empirical findings exhibit that significant own short and long-term persistence effects and the cross-markets volatility surprise spillover short and long-term persistence effects between dollar exchange rate and CRB commodity future equity index markets in five multivariate GARCH models. Besides that, the residual diagnostic test indicated that VARMA-DCC models is the best suitable model to modeling the dollar exchange rate with CRB commodity equity index.
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Başkaya, Hatice, and Abdullah Özdemir. "Bazı Sürdürülebilirlik Endekslerinin Volatilite Modelleriyle İncelenmesi." Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi 28, no. 1 (2025): 60–77. https://doi.org/10.29249/selcuksbmyd.1619942.

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Finansal piyasalarda volatilite, sermaye piyasası dinamiklerini şekillendiren en önemli unsurlardan biridir. Ani fiyat dalgalanmaları, makroekonomik göstergelerden politika belirsizliklerine kadar birçok faktörün etkisiyle oluşmaktadır. Bu çalışmada, Türkiye’deki BIST Sürdürülebilirlik Endeksi (XUSRD) ile küresel ölçekteki S&P Dow Jones Sürdürülebilirlik Endeksi (DJSI) arasındaki volatilite yayılımı analiz edilmiştir. Çalışmada, iki endeksin 05.11.2014-29.08.2024 dönemine ait 2463 günlük kapanış fiyatları ARCH, GARCH, EGARCH, TGARCH gibi volatilite modelleriyle değerlendirilmiştir. Ayrıca, endeksler arasındaki dinamik koşullu korelasyonlar DCC-GARCH modeli kullanılarak analiz edilmiştir. Araştırmanın bulguları, her iki endekste de yoğun volatilite kümelenmeleri ve negatif şokların volatilite üzerindeki etkisinin pozitif şoklara kıyasla daha büyük olduğunu ortaya koymaktadır. EGARCH modeli, iki endeksin volatilite dinamiklerini en iyi açıklayan model olarak tespit edilmiştir. DCC-GARCH modeli sonuçları, DJSI’de meydana gelen volatilite şoklarının XUSRD’nin volatilitesini %0.14 oranında azalttığını göstermektedir. Bu bulgu, küresel piyasalardaki belirsizliklerin, yerel piyasalara sermaye akışını artırarak volatiliteyi sınırlayıcı bir etki yaratabileceğini göstermektedir.
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Dissertations / Theses on the topic "DCC GARCH model"

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Noureldin, Diaa. "Essays on multivariate volatility and dependence models for financial time series." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:fdf82d35-a5e7-4295-b7bf-c7009cad7b56.

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This thesis investigates the modelling and forecasting of multivariate volatility and dependence in financial time series. The first paper proposes a new model for forecasting changes in the term structure (TS) of interest rates. Using the level, slope and curvature factors of the dynamic Nelson-Siegel model, we build a time-varying copula model for the factor dynamics allowing for departure from the normality assumption typically adopted in TS models. To induce relative immunity to structural breaks, we model and forecast the factor changes and not the factor levels. Using US Treasury yields for the period 1986:3-2010:12, our in-sample analysis indicates model stability and we show statistically significant gains due to allowing for a time-varying dependence structure which permits joint extreme factor movements. Our out-of-sample analysis indicates the model's superior ability to forecast the conditional mean in terms of root mean square error reductions and directional forecast accuracy. The forecast gains are stronger during the recent financial crisis. We also conduct out-of-sample model evaluation based on conditional density forecasts. The second paper introduces a new class of multivariate volatility models that utilizes high-frequency data. We discuss the models' dynamics and highlight their differences from multivariate GARCH models. We also discuss their covariance targeting specification and provide closed-form formulas for multi-step forecasts. Estimation and inference strategies are outlined. Empirical results suggest that the HEAVY model outperforms the multivariate GARCH model out-of-sample, with the gains being particularly significant at short forecast horizons. Forecast gains are obtained for both forecast variances and correlations. The third paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting. The key idea is to rotate the returns and then fit them using a BEKK model for the conditional covariance with the identity matrix as the covariance target. The extension to DCC type models is given, enriching this class. We focus primarily on diagonal BEKK and DCC models, and a related parameterisation which imposes common persistence on all elements of the conditional covariance matrix. Inference for these models is computationally attractive, and the asymptotics is standard. The techniques are illustrated using recent data on the S&P 500 ETF and some DJIA stocks, including comparisons to the related orthogonal GARCH models.
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Tabiš, Peter. "Dynamické modely oceňovania aktiv." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199290.

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Field of examination is theoretical and empirical review of dynamic CAPM models that assume non constant volatility and correlation. In other words time evolution is considered in estimation process. As theoretical basement is recommended to be R. Engle's (Dynamic Conditional Beta) research and other sources.
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Jurdi, Doureige. "Essays on volatility and liquidity in financial markets." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/61103/1/Doureige_Jurdi_Thesis.pdf.

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The price formation of financial assets is a complex process. It extends beyond the standard economic paradigm of supply and demand to the understanding of the dynamic behavior of price variability, the price impact of information, and the implications of trading behavior of market participants on prices. In this thesis, I study aggregate market and individual assets volatility, liquidity dimensions, and causes of mispricing for US equities over a recent sample period. How volatility forecasts are modeled, what determines intradaily jumps and causes changes in intradaily volatility and what drives the premium of traded equity indexes? Are they induced, for example, by the information content of lagged volatility and return parameters or by macroeconomic news, changes in liquidity and volatility? Besides satisfying our intellectual curiosity, answers to these questions are of direct importance to investors developing trading strategies, policy makers evaluating macroeconomic policies and to arbitrageurs exploiting mispricing in exchange-traded funds. Results show that the leverage effect and lagged absolute returns improve forecasts of continuous components of daily realized volatility as well as jumps. Implied volatility does not subsume the information content of lagged returns in forecasting realized volatility and its components. The reported results are linked to the heterogeneous market hypothesis and demonstrate the validity of extending the hypothesis to returns. Depth shocks, signed order flow, the number of trades, and resiliency are the most important determinants of intradaily volatility. In contrast, spread shock and resiliency are predictive of signed intradaily jumps. There are fewer macroeconomic news announcement surprises that cause extreme price movements or jumps than those that elevate intradaily volatility. Finally, the premium of exchange-traded funds is significantly associated with momentum in net asset value and a number of liquidity parameters including the spread, traded volume, and illiquidity. The mispricing of industry exchange traded funds suggest that limits to arbitrage are driven by potential illiquidity.
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Lönnquist, Anders. "The economic relevance of multivariate GARCH models : CCC, DCC, VCC MGARCH(1,1) covariance predictions for the use in global minimum variance portfolios." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-67989.

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Huang, Wei-Chih, and 黃薇之. "Reevaluate the DCC-GARCH and DCC-CARR model hedging performance." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/08907709827319368644.

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碩士<br>淡江大學<br>財務金融學系碩士在職專班<br>98<br>This article takes stock index and index future in United States, Germany and Japan as the research object . The sample period of S&P 500、DAX and Nikkei 225 index covers from 1/1/1991 to 31/12/2009, and the sample period of Dow Jones index covers from 1/1/1998 to 12/31/2009. The purpose of this study is to compare the out of sample performances among OLS、CCC-GARCH、DCC-GARCH、DCC-CARR models by using Variance、Utility function、Semi-variance、LPM and CVaR measurements. The empirical result shows: 1. OLS hedging model has the best out of sample performance. 2. In the dynamic model, if it only compares DCC-GARCH and DCC-CARR as the volatility forecasting estimator, DCC-CARR model has more accurate result. 3. If it takes transaction costs into the consideration, Utility function shows OLS and DCC-CARR models both have better hedging performances. Consideration the transaction cost, DCC-CARR model is also better than DCC-GARCH model.
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Wu, Chih-Pei, and 伍智培. "Evaluate the DCC-GARCH and Realized-GARCH model hedging performance." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/86112822973360507755.

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碩士<br>淡江大學<br>財務金融學系碩士班<br>101<br>In this paper , we used the data from Chicago Mercantile Exchange which trades S&P 500 futures prices and spot prices as the main object of study . The researching period was from 1 January 2002 to 31 December 2008 ended, in which the in-the-sample period was set in 1 January 2002 to 31 December 2006 , and the out-of-sample heding period was set in 1 January 2007 to 31 December 2008 , using the rolling windows method to estimate it .The paper used the various methods to evaluate the out-of-sample hedging performance under the hedging models : Realized variance、Bi-power realized variance and Tri-power realized variance , these methods were the Variance、Semi-variance、Utility function、VaR、CVaR and Economic value. The empirical results showed that : 1.Under the out-of-sample hedging performance period , the DCC-Realized-GARCH-RV30 hedging model worked best both in statistical analysis and economic analysis. 2.And then the paper considered the transaction cost , in order to be close to the reality , we used the Utility function to evaluate the hedging performance and Economic value . In the end , only the DCC-Realized-GARCH-RBV30 hedging model was superior to DCC-GARCH hedging model , and had the positive Economic value under the long hedge , hence , the conclusion was inconsistent with the circumstance which did not consider the transaction cost . Transaction cost were therefore considered not feasible in practice , because in practice it could not adjust hedge ratio daily.
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LaBarr, Aric David. "Multivariate robust estimation of DCC-GARCH volatility model." 2010. http://www.lib.ncsu.edu/resolver/1840.16/6015.

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Chen, Hsiang-ning, and 陳湘寧. "The Application of DCC-GARCH model in Portfolio Selection." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/27500883893096776587.

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碩士<br>國立高雄第一科技大學<br>風險管理與保險研究所<br>100<br>This paper aims to analyze the decision making on model selection under alternative constraints, utilizing the DCC-GARCH (Dynamic Conditional Correlation) model based on weekly, monthly and quarterly time frequencies to investigate the performances of mean-variance efficient portfolios. The empirical analysis is conducted using the S&P500 stock index, FTSE NAREIT U.S. All REITs and the bond index obtained from Barclays Capital U.S. Aggregate, all of which are sampled spanning from Jan. 3, 2000 to Apr. 29, 2011. The empirical results indicate that the strategy 4 which aims to maximize the rate of return with allowance of short selling outperforms to other strategies based on the Sharp ratio. According to the Hedge effectiveness, the strategy 1 which aims to minimize the variance of rate of return not allowing short selling, tend to be the best strategies, respectively.
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HSU, Ming-Chin, and 徐明墐. "SSI, Order Flow and Exchange Rate Volatility─DCC-GARCH Model." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/03950058937706380439.

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碩士<br>輔仁大學<br>經濟學研究所<br>97<br>Abstract It is important to find the high correlated exchange rate with other economy variables in forecasting. The Meese and Rogoff (1983a,b) use traditional exchange rate determination models to forecast exchange rate and they showed that the random walk model were better than others. Evans and Lyons (2002a) wanted to “beating a random walk” in forecasting is too strong a criterion for accepting an exchange rate model. They provided a model which described order flow in determining exchange rates. Order flow is taken to be a variant of the more familiar concept of ‘net demand’ and measures the net of buyer-initiated orders and seller-initiated orders. Evans and Lyons provided evidence to show that order flow was a significant determinant of two major bilateral exchange rates at the daily frequency, obtaining coefficients of determination substantially were larger than the ones which usually obtained using standard macroeconomic models of nominal exchange rates. Compared with the above literature, we use SSI for our empirical study. The Speculative Sentiment Index (SSI) is based on proprietary customer flow information and is designed to recognize price trend breaks and reversals in the four most popularly traded currency pairs. The absolute number of the ratio itself represents the amount by which longs exceed shorts or vice versa. Both of foreign exchange rates on order flow and on Speculative Sentiment Index (SSI) are investigated for five major exchange rate pairs, EUR/USD, GBP/USD, USD/CAD, USD/CHF and USD/JPY, across sampling frequencies 1 hour during 2003 and 2006.In the paper we use the Dynamic Conditional Correlation (DCC) Model to check the relationship with order flow, SSI and exchange rate. Our results indicate order flow and SSI both has high correlation coefficient with exchange rate especially in “winter” period. The results are similar with five major exchange rate pairs.
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Chen, Szu-Yin, and 陳思尹. "Synchronization of monthly real GDP :analysis by VAR-DCC-GARCH model." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/8atjxw.

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碩士<br>輔仁大學<br>經濟學系碩士班<br>102<br>The purpose of this paper is to disaggregate quarterly GDP into monthly GDP and then estimate the pair-correlation and VAR-DCC-GARCH model to measure the synchronization between individual countries and euro area. Then we attempt to compare these two results. The data which we use is from January, 1999 to December, 2010 in 13 countries. The empirical results reveal that the figures for the pair-correlation are higher than that for VAR-DCC-GARCH model. That is, we may overestimate when adopting pair-correlation. In addition, although Czech Republic and Hungary are the members of both euro union and euro area, the correlation which is estimated by VAR-DCC-GARCH model between euro area fluctuates. UK is not in euro area, however, the correlation between euro area is always positive.
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Book chapters on the topic "DCC GARCH model"

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Büyükkara, Göknur, Onur Enginar, and Hüseyin Temiz. "Volatility Spillovers Between Oil and Stock Market Returns in G7 Countries: A VAR-DCC-GARCH Model." In Regulations in the Energy Industry. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-32296-0_10.

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Bentouir, Naima, Ali Bendob, Mohammed El Amine Abdelli, Samir B. Maliki, Mourad Kertous, and Afef Khalil. "How the Cryptocurrencies React to Covid-19 Pandemic? An Empirical Study Using DCC GARCH Model (2019–2021)." In Digital Economy, Business Analytics, and Big Data Analytics Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05258-3_34.

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Cai, Junling, and Ning Zhang. "The Dynamic Correlation Between Civil Aviation Passenger Traffic Volume and Its Influential Factors Based on DCC-GARCH Model." In Recent Trends in Intelligent Computing, Communication and Devices. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9406-5_76.

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Huang, Yiyu, Wenjing Su, and Xiang Li. "Comparison of BEKK GARCH and DCC GARCH Models: An Empirical Study." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17313-4_10.

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Karaömer, Yunus. "Is Gold Safe Haven for Turkish Stocks During the Russia-Ukraine War?" In Ekonomi ve Finans Çalışmaları. Özgür Yayınları, 2023. http://dx.doi.org/10.58830/ozgur.pub138.c511.

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This study aims to investigate the safe haven property of gold against Turkish stocks, namely, the BIST tourism, BIST construction, BIST food-beverage, and BIST chem petrol plastic during the Russia-Ukraine war. In the study, the dynamic conditional correlation-GARCH (DCC-GARCH) model is preferred to investigate the dynamic correlation between gold and Turkish stocks. To check the robustness of the DCC-GARCH empirical findings, the corrected dynamic conditional correlation-GARCH (cDCC-GARCH) model is applied to investigate the dynamic correlation between gold and Turkish stocks. Empirical results show that the correlations between the gold and BIST tourism, BIST construction, and BIST food-beverage returns are negative from February 24, 2022, to February 28, 2022. Gold could act as a safe haven during war periods for BIST tourism, BIST construction, and BIST food-beverage. The gold and BIST chem petrol plastic are positive from February 24, 2022, to February 28, 2022. For BIST chem petrol plastic, gold could not act as a safe haven during war periods. Besides, the empirical findings of the cDCC-GARCH support the empirical results of the DCC-GARCH. This study finds that gold exhibits safe haven properties during the Russia-Ukraine war.
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Chen, Peimin, Chunchi Wu, and Ying Zhang. "DCC-GARCH Model for Market and Firm-Level Dynamic Correlation in S&P 500." In Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning. WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811202391_0129.

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Chen, Peimin, Chunchi Wu, and Ying Zhang. "DCC-GARCH Model for Market and Firm-Level Dynamic Correlation in S&P 500." In Handbook of Financial Econometrics, Statistics, Technology, and Risk Management. WORLD SCIENTIFIC, 2025. https://doi.org/10.1142/9789819809950_0129.

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Derbali, Abdelkader Mohamed Sghaier. "COVID-19 or Russia-Ukraine Conflict, Which Is Informative in Defining the Dynamic Relationship Between Bitcoin and Major Energy Commodities?" In Blockchain Applications for Smart Contract Technologies. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1511-8.ch001.

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This chapter examines an essential methodology to evaluate the influence of the COVID-19 and Russia-Ukraine conflict surprises and conception statements employed for the dynamic conditional correlation between returns and volatilities of energy commodity indices and Bitcoin. To assess analytically the unexpected component of COVID-19 and Russia-Ukraine conflict surprises, the authors use GARCH-DCC(1,1) model by incorporating a dummy variable which measures the surprise factor during the period of study from January 4, 2016, to April 4, 2022. The results suggest significant and considerable dynamic conditional correlation between energy commodities indices and Bitcoin if COVID-19 pandemic and Russia-Ukraine conflict shocks are incorporated in variance assessments. Additionally, these outcomes demonstrate the financialization phenomena of energy commodities indices and Bitcoin. The authors find that the dynamic conditional correlation between energy commodities indices and Bitcoin start to respond considerably more in the situation of Russia-Ukraine conflict shocks than COVID-19 surprises.
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Dimitriou, Dimitrios, and Theodore Simos. "Are Exotic Assets Contagious?" In Recent Advances and Applications in Alternative Investments. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2436-7.ch005.

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In this study, authors investigate the possibility of contagion/safe haven effects during the Global Financial Crisis (GFC) of 2007-2009 for two exotic assets: rare coins and wine lvx50. The data sample is monthly comprising a rare coins and wine lvx50 indices, as well as MSCI (Morgan Stanley Capital Index) World financial index as a benchmark for world financial sector, spanning from 2000 until 2016. According to Baur and Lucey (2010) an asset may be characterized as safe haven, by the following definition: “A safe haven is defined as an asset that is uncorrelated or negatively correlated with another asset or portfolio in times of market stress or turmoil”. Employing a bivariate GARCH (1,1)-DCC model, authors uncover significant evidence of contagion effects among the MSCI World Financial and wine lvx50, while the pair MSCI World Financial and rare coins show a safe haven behaviour. These findings confirm a specific pattern of contagious and safe haven behaviors that provide important implications for international investors.
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Yildirim, Ecenur Ugurlu. "Globalization of Stock Market, Economic Growth, and Geopolitical Risk." In Handbook of Research on Institutional, Economic, and Social Impacts of Globalization and Liberalization. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4459-4.ch009.

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Although the significance of the foreign investors constructing the significant magnitude of GDP increases for the emerging markets, their equity markets' attractiveness is affected by their vulnerability to geopolitical risk. The purpose of this study is to empirically investigate the effect of the stock market globalization on the correlation between economic growth and geopolitical risk in Brazil. After the dynamic correlation between economic growth and the geopolitical risk in Brazil is obtained by DCC-GARCH(1,1) methodology, the nonlinear autoregressive distributed lag (NARDL) model is employed to examine the asymmetric relationship among variables. The findings demonstrate while the changes in the globalization of the stock market decrease the connection between economic growth and geopolitical risk in the long-run, the positive changes in the participation of foreign investors make economic growth and geopolitical risk more connected the in short-run. Moreover, this impact is asymmetric. This chapter provides valuable implications for international investors and policymakers.
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Conference papers on the topic "DCC GARCH model"

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Ge, Yan, and Haixia Wu. "Dynamic Correlation Research on Grain Markets Based on DCC-GARCH Model." In 2017 3rd International Conference on Economics, Social Science, Arts, Education and Management Engineering (ESSAEME 2017). Atlantis Press, 2017. http://dx.doi.org/10.2991/essaeme-17.2017.157.

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Wu, Tianyu. "Research on Multimodal Futures Price Prediction Method based on DCC-GARCH Model." In 2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC). IEEE, 2023. http://dx.doi.org/10.1109/icaisc58445.2023.10199418.

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Singh, Vikrant Vikram, Harendra Singh, Aditya Kumar Gupta, and Prashant Dev Yadav. "Cryptocurrency as a Hedging Alternative- DCC GARCH Model Analysis using R Programming." In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2022. http://dx.doi.org/10.1109/ic3i56241.2022.10073144.

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Nafisi-Moghadam, Maryam, and Shahram Fattahi. "A Hybrid Model of VAR-DCC-GARCH and Wavelet Analysis for Forecasting Volatility." In ITISE 2022. MDPI, 2022. http://dx.doi.org/10.3390/engproc2022018006.

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Cai, Junling. "The Dynamic Relationship Between Defense Expenditure and Economic Growth Based on DCC-GARCH Model." In Proceedings of the 3rd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2024, May 24–26, 2024, Jinan, China. EAI, 2024. http://dx.doi.org/10.4108/eai.24-5-2024.2350174.

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"Co-movements between Chinese and British metal futures markets：Some New Evidence base on DCC-GARCH model." In 2017 4th International Conference on Business, Economics and Management. Francis Academic Press, 2017. http://dx.doi.org/10.25236/busem.2017.26.

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Ou, Meng, and Jie Li. "Co-movements Between Chinese and CBOT Grain Futures Markets: Some New Evidence Based on DCC-GARCH Model." In Proceedings of the Third International Conference on Economic and Business Management (FEBM 2018). Atlantis Press, 2018. http://dx.doi.org/10.2991/febm-18.2018.44.

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Lu Xiuhong and Zhu Zhengxuan. "Research on the correlation between the SHIBOR and stock market returns based on the DCC-GARCH model." In 2016 13th International Conference on Service Systems and Service Management (ICSSSM). IEEE, 2016. http://dx.doi.org/10.1109/icsssm.2016.7538487.

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Charaeva, Marina V., Marina A. Kuznetsova, and Song Yansong. "The impact of commodity market volatility on China's stock market." In Sustainable and Innovative Development in the Global Digital Age. Dela Press Publishing House, 2022. http://dx.doi.org/10.56199/dpcsebm.zmib9194.

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The article examines individual industry data series on the Chinese stock market and international commodity markets based on the application of the method of decomposition of generalized variance of forecast errors to build a secondary volatility index and overflow network. The DCC-GARCH model proposed by the author is used to study the effect of hedging wholesale goods on the Chinese stock market. The results show that in every industry in China, industry and consumer industry are the main risk-taking market, and the energy industry and financial industry are the main export risk market.
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XIE, RUOTING. "THE IMPACT OF INVESTOR SENTIMENT ON THE RETURN OF STOCKS—EMPIRICAL ANALYSIS BASED ON THE DCC-GARCH MODEL." In 2021 INTERNATIONAL CONFERENCE ON ADVANCED EDUCATION AND INFORMATION MANAGEMENT (AEIM 2021). Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/dtssehs/aeim2021/35991.

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Abstract. With the gradual inapplicability of the “rational man” and the efficient market hypothesis in the contemporary financial field, modern finance represented by behavioral finance has emerged. Behavioral finance is guided by the study of human psychology and behavior, exploring the internal connections and fluctuations in the financial market. Investor sentiment is often regarded as the most effective data reflected from a human perspective. Therefore, this article selects the monthly data of CICSI Investor Sentiment Index, Shenzhen Component Index, and Shanghai Composite Index logarithmic rate of return from February 2003 to December 2017, and establishes a DCCGARCH model for dynamic correlation analysis as an empirical study Basis, and draw conclusions. After research, it is found that there is a very obvious relationship between the investor sentiment index and the logarithmic return rate of the Chinese main board market. Particularly during periods of high investor sentiment, the negative correlation presented is more significant. Finally, based on the results of the research, this article makes recommendations for behavioral finance research, policy and regulation formulation, financial supervision and investors.
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