Conditional Correlation Multivariate GARCH Model Research Articles

The Volatility Transmission Between Cryptocurrency And Global Stock Market Indices: Case Of Covid-19 Period

The uncertainty originated by the COVID-19 pandemic and the unpredictability of both real and financial market indicators have increased the volatility of global financial markets. As a result of globalization, the determination of risk and information transfer between financial markets has gained importance during the pandemic process. In this context, the spread of volatility between the cryptocurrency market and the global stock markets was analyzed by considering the pandemic process. Bitcoin, which represents 42% of the total market cap, was used to represent the cryptocurrency market in the analysis. S&P500, FTSE100, SSEC and NIKKEI indices, which are among the world's leading indices in terms of market cap, were used to represent the global stock market. Constant Conditional Correlation Multivariate GARCH model was used for the analysis of volatility transmission. Daily closing prices covering the date range from 1st December 2019 to 1st July 202 were used for the analyses. The model results were positive and significant for all predicted conditional correlation parameters. In this context, there is volatility transmission and information transfer between BTC and stock returns. The model findings are expected to be a supporting element for financial market participants to make the right decision in the optimal portfolio allocation process.

Commodity financialization and funding liquidity in China

This study examines commodity financialization in China through commodity futures and stock market price co-movement, captured by a dynamic conditional correlation multivariate GARCH model (DCC-MGARCH). We find a dramatic increase in correlation after 2004; however, after 2010, the correlation decreases. We further investigate how funding liquidity affectes commodity financialization and find that its effect on the industrial sector is stronger than that on the agricultural sector, which reflects commodity financialization layering.

Analysis of financial contagion in influential African stock markets

Drawing from the experience of the global financial crisis that sprang forth from the US stock market, an empirical assessment of the dynamic correlation analysis of financial contagion with evidence from (5) African countries (South African, Nigeria, Egypt, Kenya, Tunisia) is presented. Monthly stock prices indices from 2004 to 2018 was analyzed using the dynamic conditional correlation multivariate GARCH model to ascertain the contagious effect of the US to the selected African markets. By analyzing the correlation coefficient series, three phases of the crisis periods were identified {pre-crisis (2004–2007); crisis (2007–2009) and post-crisis (2009–2018), respectively}. The study revealed that a significant relationship exists between the returns of the US market and the African markets. The inspection of the pre-crisis, crisis, post-crisis mean and variance estimation shows that the crisis period is characterized by substantial increases in volatility, establishing that the shock experienced in the US posed a threat to the African markets being examined. Further, evidence revealed that in the crisis period, an increase in correlation (contagion) existed, while a continued correlation (herding) existed in the post-crisis period.

An application of Regular Vine copula in portfolio risk forecasting: evidence from Istanbul stock exchange

<abstract> In times of financial turbulence, it is a well-documented fact that the co-movement of financial returns tends to increase leading to unexpected portfolio losses. The magnitude of the losses can severely be underestimated when the characteristics of univariate return series and the dependence structure between the returns are not represented well by a risk forecasting model. From a growing literature on the available multivariate modelling tools, this paper aims to investigate daily portfolio Value at Risk and Expected Shortfall forecasting performance of elliptical as well as Regular Vine copulas. For this purpose, return series of twelve stocks that are listed in Istanbul Stock Exchange are obtained for the period of June 2010 to December 2018. The series are modelled with univariate Generalized Auto-Regressive Conditional Heteroskedasticity models with Normal and Student's t innovations. Equally weighted portfolio returns are forecasted depending on the univariate GARCH marginals and their multivariate dependence structure. Estimated daily portfolio Value at Risk and Expected Shortfall values with varying levels are compared with the traditional Variance-Covariance and Dynamic Conditional Correlation Multivariate GARCH model estimates. While the models performed well at the Value at Risk backtests, according to the applied ES backtests R-vine copula GARCH found better at yielding more accurate Expected Shortfall forecasts. </abstract>

The spillover effects of economic policy uncertainty on financial markets: a time-varying analysis

PurposeThis 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/approachThis 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 and vice versa. To capture different dynamic relationships between multiple time-series variables following different regimes, this paper applies the Markov switching model to the stock returns of both the USA and the eight major stock markets.FindingsThe increases in US uncertainty have significant negative impacts on all EU stock returns, whereas only the increases in the uncertainties of Spain, Ireland, Sweden and the UK have significant negative impacts on US stock returns. Notably, the economic policy uncertainty (EPU) in the USA has a dynamic effect on the European stock markets. In a bear market (State 1), the increases in the EPU of the USA and EU have significant negative impacts on EU stock returns in most cases. However, only the increase in US EPU has significant negative impacts on EU stock returns in bull markets (State 2). Reciprocally, the increases in the EU EPUs of Germany, Spain and the UK have significant impacts on US stock returns in bear market.Originality/valueThe observations challenge the conventional wisdom according to which only larger economies can lead the smaller counterparts. The findings also highlight the stronger dependence of the US stock market on international macroeconomic uncertainty.

Asset Price Volatility in EU-6 Economies: How Large is the Role Played by the ECB?

In this paper we provide evidence that the effects of the different waves of asset purchase programmes implemented by the ECB from 2009 onwards have spilled over into asset price volatility developments of a group of six Central and Eastern European economies belonging to the EU but not to the euro area. This has partly shielded their financial markets from the negative shocks that have influenced international investors’ degree of risk aversion in recent years. By means of a dynamic conditional correlation multivariate GARCH model, and by resorting to three different proxies to describe the functioning and measure the impact of the ECB’s asset purchase programmes, we show that such non-standard monetary measures have played a significant role in dampening volatility spikes in the financial markets of the countries at stake. This probably reflects how both a ‘risk taking’ and a ‘liquidity’ channel of transmission actually work. The results are generally robust to an extensive series of tests, and to changes made in the estimation methodology.

Time-varying synchronization and dynamic conditional correlation among the stock market returns of leading South American economies

PurposeThe purpose of this paper is to capture the pattern of return volatility and information spillover and the extent of conditional correlation among the stock markets of leading South American economies. It also examines the connectedness of market returns within the region.Design/methodology/approachThe time series properties of weekly stock market returns of benchmark indices spanning from the second week of 1995 to the fourth week of December 2015 are analyzed. Using univariate auto-regressive conditional heteroscedastic, generalized auto-regressive conditional heteroscedastic, and dynamic conditional correlation multivariate GARCH model approaches, the study finds evidence of returns and volatility linkages along with the degree of connectedness among the markets.FindingsThe findings of this study are consistent with increasing market connectedness among a group of leading South American economies. Stocks exhibit relatively fewer asymmetries in conditional correlations in addition to conditional volatility; yet, the asymmetry is relatively less apparent in integrated markets. The results demonstrate that co-movements are higher toward the end of the sample period than in the early phase. The stock markets of Argentina, Brazil, Chile, and Peru are closely and strongly connected within the region followed by Colombia, whereas Venezuela is least connected with the group.Practical implicationsThe implication is that foreign investors may benefit from the reduction of the risk by adding the stocks to their investment portfolio.Originality/valueThe unique features of the paper include a large sample of national stock returns with updated time series data set that reveals the time series properties and empirical evidence on volatility testing. Unlike other studies, this paper uncovers the relation between the stock markets within the same region facing the same market condition.

A financial network perspective of financial institutions’ systemic risk contributions

This study considers the effects of the financial institutions’ local topology structure in the financial network on their systemic risk contribution using data from the Chinese stock market. We first measure the systemic risk contribution with the Conditional Value-at-Risk (CoVaR) which is estimated by applying dynamic conditional correlation multivariate GARCH model (DCC-MVGARCH). Financial networks are constructed from dynamic conditional correlations (DCC) with graph filtering method of minimum spanning trees (MSTs). Then we investigate dynamics of systemic risk contributions of financial institution. Also we study dynamics of financial institution’s local topology structure in the financial network. Finally, we analyze the quantitative relationships between the local topology structure and systemic risk contribution with panel data regression analysis. We find that financial institutions with greater node strength, larger node betweenness centrality, larger node closeness centrality and larger node clustering coefficient tend to be associated with larger systemic risk contributions.

Stock market comovements in Central Europe: Evidence from the asymmetric DCC model

We examine time-varying stock market comovements in Central Europe employing the asymmetric dynamic conditional correlation multivariate GARCH model. Using daily data from 2001 to 2011, we find that the correlations among stock markets in Central Europe and between Central Europe vis-à-vis the euro area are strong. The correlations increased over time, particularly after their EU entry and largely remained at these levels during the financial crisis. The stock markets exhibit asymmetry in the conditional variances and to a certain extent in the conditional correlations as well, pointing to the importance of applying a sufficiently flexible econometric framework. The conditional variances and correlations are positively related, suggesting that the diversification benefits decrease disproportionally during volatile periods.

Volatility Threshold Dynamic Conditional Correlations: An International Analysis

This paper proposes a modeling framework for the study of changes in cross-market comovement conditional on volatility regimes. Methodologically, we extend the Dynamic Conditional Correlation multivariate GARCH model to allow the dynamics of correlations to depend on asset variances through a threshold structure. The empirical application of our model to a sample of international stock markets in 1994-2011 indicates that the periods of market turbulence are associated with an increase in cross-market comovement. The modeling framework proposed in the paper represents a useful tool for the study of market contagion.

Volatility Threshold Dynamic Conditional Correlations: An International Analysis

This article proposes a modeling framework for the study of changes in cross-market comovement conditional on volatility regimes. Methodologically, we extend the Dynamic Conditional Correlation multivariate GARCH model to allow the dynamics of correlations to depend on asset variances through a threshold structure. The empirical application of our model to a sample of international stock markets in 1994–2011 indicates that the periods of market turbulence are associated with an increase in cross-market comovement. The modeling framework proposed in the article represents a useful tool for the study of market contagion.

Stock Market Comovements in Central Europe: Evidence from Asymmetric DCC Model

We examine time-varying stock market comovements in Central Europe employing the asymmetric dynamic conditional correlation multivariate GARCH model. Using daily data from 2001 to 2011, we find that the correlations among stock markets in Central Europe and between Central Europe vis–a–vis the euro area are strong. They increased over time, especially after the EU entry and remained largely at these levels during financial crisis. The stock markets exhibit asymmetry in the conditional variances and in the conditional correlations, to a certain extent, too, pointing to an importance of applying sufficiently flexible econometric framework. The conditional variances and correlations are positively related suggesting that the diversification benefits decrease disproportionally during volatile periods.

Investigation of Socially Responsible Investment Markets (SRI) Using Dynamic Conditional Correlation (DCC) Method: Implications for Diversification

Problem statement: Over the last ten years there has been a phenomena l growth in the amount of funds placed in SRI globally estimated to be around US$6.5 trillion while around US$55 billion in the Australian market. Accurate knowledg e of correlation of the Australian SRI market with other SRI markets overseas is crucially important f or Australian (SRI) investors for international portfolio diversification since portfolio diversifi cation theory posits that the lower (higher) the correlation between markets, the higher (lower) the gains to be made. The study examines the relationship of the Australian SRI market with four teen other markets-Canada, Denmark, France, Germany, Hong Kong, Ireland, Japan, Netherlands, Norway, South Africa, Sweden, Switzerland, United Kingdom and the United States. Approach: The relationships of the Australian Socially Responsible Investment (SRI) market with other SRI markets worldwide during the period 1994-2009 are examined based on the dynamic conditional correlation multivariate GARCH model (DCC- MVGarch). In the DCC method, the multivariate conditional variance estimation is simplified by estimating univariate GARCH models for each market. Using the transformed residuals resulting from the first stage, the authors can estimate a conditi onal correlation estimator. The standard errors for the first stage parameters remain while the standard er rors for the correlation parameters are modified. Results: Our results showed that the Australian market expe rienced a surge in correlation with all other markets during the global financial crisis. D uring the period of study, the correlation of Austr alia with Canada, Denmark, Norway and the United Kingdom increased over time while its correlation with other countries remained stationary. This impl ies that the Australian SRI market is becoming more integrated with those of Canada, Denmark, Norway and the United Kingdom. Therefore, these overseas markets provide less portfolio diversifica tion benefits to Australian SRI investors while the other markets still offer some opportunities. Conclusion/Recommendations: This study examined the relationship of the Australian Socially Responsible Investment (SRI) market with other SRI markets worldwide during the period 1994-2009 based on dynamic conditional correlation multivariate GARCH model that provides accurate correlations over time that can be incorporated into portfolio models. Australian SRI market was analyzed with fourteen other markets around the world such as Canada, Denmark, France, Germany, Hong Kong, Ireland, Japan, Netherlands, Norway, South Africa, Sweden, Switzerland, United Kingdom and the United States. The results showed that the Australian market experienced a spike in correlation with the other markets during periods of market distress; fo r example, during the recent global financial crisis. In spite of the fluctuations in the correlations o f Australian with the other markets the correlations generally remain below 1. Australia's correlation with Denmark, Norway and Japan increased while with others our market was more or less stationary except from 2007 onwards-global financial crisis. C learly, it still pays for Australian investors to diversify internationally since the correlations ar e still less than 1. However, it pays less to diver sify to such markets as Japan, Norway and Denmark while diversification benefits can be obtained from the other markets. Importantly, our results suggested t hat diversification to other SRI markets is less

Modelling price and volatility inter-relationships in the Australian wholesale spot electricity markets

This paper examines the inter-relationships of wholesale spot electricity prices among the four regional electricity markets in the Australian National Electricity Market (NEM): namely, New South Wales, Queensland, South Australia and Victoria using the constant conditional correlation and Tse and Tsui's (Tse, Y.K., Tsui, A.K.C., 2002. A multivariate generalised autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business and Economic Statistics 20 (3), 351–362.) and Engle's (Engle, R., 2002. Dynamic conditional correlation: a sample class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics 20 (3), 339–350.) dynamic conditional correlation multivariate GARCH models. Tse and Tsui's (Tse, Y.K., Tsui, A.K.C., 2002. A multivariate generalised autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business and Economic Statistics 20 (3), 351–362.) dynamic conditional correlation multivariate GARCH model which takes account of the Student t specification produces the best results. At the univariate GARCH(1,1) level, the mean equations indicate the presence of positive own mean spillovers in all four markets and little evidence of mean spillovers from the other lagged markets. In the dynamic conditional correlation equation, the highest conditional correlations are evident between the well-connected markets indicating the presence of strong interdependence between these markets with weaker interdependence between the not so well-interconnected markets.

A generalized Dynamic Conditional Correlation model for portfolio risk evaluation

We propose a generalization of the Dynamic Conditional Correlation multivariate GARCH model of Engle [R.F. Engle, Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models, Journal of Business and Economic Statistics 20 (2002) 339–350] and of the Asymmetric Dynamic Conditional Correlation model of Cappiello et al.[L. Cappiello, R.F. Engle, K. Sheppard, Asymmetric dynamics in the correlations of global equity and bond returns, Journal of Financial Econometrics 25 (2006) 537–572]. The model we propose introduces a block structure in parameter matrices that allows for interdependence with a reduced number of parameters. Our model nests the Flexible Dynamic Conditional Correlation model of Billio et al. [M. Billio, M. Caporin, M. Gobbo, Flexible dynamic conditional correlation multivariate GARCH for asset allocation, Applied Financial Economics Letters 2 (2006) 123–130] and is named Quadratic Flexible Dynamic Conditional Correlation Multivariate GARCH. In the paper, we provide conditions for positive definiteness of the conditional correlations. We also present an empirical application to the Italian stock market comparing alternative correlation models for portfolio risk evaluation.

Flexible Dynamic Conditional Correlation multivariate GARCH models for asset allocation

This paper introduces the Flexible Dynamic Conditional Correlation (FDCC) multivariate GARCH model which generalizes the Dynamic Conditional Correlation (DCC) multivariate GARCH model proposed by Engle (2002 Engle, RF. 2002. Dynamic conditional correlation – a simple class of multivariate GARCH models. Journal of Business and Economic Statistics, 20: 339–50. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). The FDCC model relax the assumption of common dynamics among all assets used in the DCC model. In fact, we cannot impose that the correlation dynamics of, say, European sectorial stock indexes are identical to the corresponding US ones. We thus extend the DCC model introducing a block-diagonal structure; in the FDCC the dynamics are constrained to be equal among groups of variables. We present an application to a sectorial asset allocation problem.

A Generalized Dynamic Conditional Correlation Model for Portfolio Risk Evaluation

We propose a generalization of the Dynamic Conditional Correlation multivariate GARCH model of Engle (2002) and of the Asymmetric Dynamic Conditional Correlation model of Cappiello et al. (2006). The model we propose introduces a block structure in parameter matrices that allows for interdependence with a reduced number of parameters. Our model nests the Flexible Dynamic Conditional Correlation model of Billio et al. (2006) and is named Quadratic Flexible Dynamic Conditional Correlation Multivariate GARCH. In the paper, we provide conditions for positive definiteness of the conditional correlations. We also present an empirical application to the Italian stock market comparing alternative correlation models for portfolio risk evaluation.

Asymmetry in Regional Real House Prices

Executive Summary.This paper investigates the stochastic behavior of regional real house prices while allowing for asymmetric responses of the current prices to positive and negative changes in the lagged prices. The analysis is carried out using the dynamic conditional correlation multivariate GARCH model. The findings indicate that lagged positive changes have played a far more important role in shaping the current changes in the real house prices. This paper appears to be among the very few empirical studies to document such asym-metry, which suggests that previous studies might en-counter a model misspecification.