Multivariate Dynamic Conditional Correlation Research Articles

Long-Run Volatility Memory Dynamics and Inter-Market Linkages in GCC Equity Markets: Application of DCC-FIGARCH Models

Abstract The study investigates volatility persistence, long-term memory and time-varying conditional correlations among the stock markets of the Gulf Cooperation Council (GCC) countries. Daily equity index data between 2012 and 2024 were analyzed using univariate fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) models to examine long-memory behavior and multivariate dynamic conditional correlation (DCC) models to assess conditional correlations between these markets. For each of the GCC equity markets, the analysis highlighted large degrees of long-memory and volatility persistence. Finally, the DCC model shows that strong and dynamic Intermarket links among the GCC, especially between KSA and UAE, exist and reflect significant volatility spillover from good economic ties. This study fills the gap in the literature by providing a comprehensive understanding of long-run volatility memory and inter-market associations in the GCC stock markets.

Using Multivariate Dynamic Conditional Correlation GARCH model to analysis financial market data

Using Multivariate Dynamic Conditional Correlation GARCH model to analysis financial market data

Brazilian REITs: Are They an Opportunity for Diversification and Performance?

We study the dynamic diversification benefits of Brazilian real estate investment trusts (BR-REITs) by analyzing their complex dynamic relationship with stock and government bond indices. We estimate the assets’ conditional variance–covariance matrix using a vector autoregressive multivariate dynamic conditional correlation GARCH model with Student’s t distribution (VAR-DCCt MGARCH) and a global minimum variance portfolio. We find that BR-REITs provide strong risk-adjusted performance and persistent diversification benefits for Brazilian investors. Furthermore, these diversification benefits are not impacted by the market performance of stocks and bonds. Nevertheless, our results show that the BR-REITs’ conditional correlations with stocks and bonds are dynamic; thus, BR-REIT investors should employ dynamic investment strategies that account for time variation in REITs’ diversification benefits. Our results suggest that Brazilian investors, particularly long-term investors, would secure significant performance and diversification benefits, lowering their portfolios’ short-term volatility, by investing in BR-REITs.

Exchange rate spillover effect on Stock Returns: Evidence from Pakistan

Volatility spillover is measured as the transferal of variability among different financial markets. The objective of this study is to see if there is any effect volatility spillover amongexchange rates and stock returns in Pakistan or not. For this purpose the secondary dataranging from April 2010 – April 2020 is collected and analyzed using multivariate dynamic conditional correlation GARCH (DCC-MGARCH) model. It is observed that there is a notable volatility spillover impact between the two. This study can be used by different investing firms and individual investors incorporating the spillover impact in order to hedge and diversify their portfolios. It can also be used by educational institutions for teaching and training purposes. It can also help the policy makers providing recommendations regarding spillover effect between the concerned markets.

The impact of world oil and food price shocks on the interdependence of Brazil and Russia: SVAR-DCC-GARCH model

This study seeks to identify the extent to which global oil and food price volatilities affected the interdependence of the Brazilian and Russian economies in the period from 1996 to 2021. The ARCH/GARCH framework was used to model the volatility of oil and food prices. The Structural Vector Autoregressive (SVAR) approach was used to ascertain the sensitivity of key economic indicators to oil and food shocks. The Impulse Response Function (IRF) was used to trace short-term effects over a period of 12 months. Subsequently, the multivariate dynamic conditional correlation DCC-GARCH model, created by Engle & Sheppard (2001), was used to model time-varying correlations of paired macroeconomic variables. This study contributes to the empirical literature in two fundamental ways. Firstly, it pairs the two largest oil and food producers in the BRICS bloc. Secondly, unlike some earlier studies, the applied methodology ensures the effectiveness of the results by using stationary time series data. The results show that Brazil and Russia have long-run spillover effects for all macroeconomic variables in response to both oil and food price shocks. Furthermore, money supply and exchange rate variables exhibited declining positive correlation coefficients during the global financial crisis of 2008–2009, but peaked in early 2020 due to the Covid-19 pandemic. As a corollary of the main findings, the researchers recommend that investors should diversify their portfolios beyond these two economies in order to minimize the risk of contagion during severe global crises.

Price Volatility Transmission in China’s Hardwood Lumber Imports

Hardwood lumber is the principal part of the global hardwood timber trade. China has become the largest importer of hardwood lumber in the world. However, China’s hardwood lumber imports are affected by price volatility. Thus, we investigated the price volatility transmission of China’s hardwood lumber imports. We aimed to detect the source, path, and intensity of the volatility transmission in China’s hardwood lumber imports, and reveal the intrinsic interactions between price volatilities. To date, there is little research on the price fluctuations of forest products. This paper provides an empirical analysis on the volatility transmission in China’s forest product imports. We selected four types of major hardwood lumber imports to China; that is, teak (Tectona grandis L.F.), merbau (Merbau), sapele (Entandrophragma), and casla (Terminalia spp.) (The Latin names of tree species are given in parentheses), and used their daily prices from 4 August 2010 to 15 April 2020. The Baba–Engle–Kraft–Kroner (BEKK) multivariate models and dynamic conditional correlation (DCC) models were employed. The empirical results indicate that there is an intrinsic relationship between the price fluctuations in China’s hardwood lumber imports. The volatility transmission chain originates from casla; it is transmitted along the casla→sapele→merbau→teak pathway. The direction of transmission is from lower prices to higher prices. The dynamic conditional correlation of each link in the chain does not exhibit any particular time trend. This suggests that volatility transmission is a crucial price mechanism in China’s hardwood lumber imports. Our findings have important policy implications for hedging timber price risks and designing timber trade policies.

Analysing volatility spillovers between grain and freight markets

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 Generalized Autoregressive Conditional Heteroskedasticity model (DCC GARCH) is used to find the spillovers between the two markets and thereby explore the effect of GOFI on GOI markets from the year 2013. The research article consists of four sections after introducing the subject namely a literature review, research methodology and models, analysis and conclusions of the study.

Blockchain ETFs: dynamic correlations and hedging capabilities

PurposeIn this paper, the authors examine the interconnectedness of four blockchain exchange-traded funds (ETFs) with other financial markets, such as stocks and cryptocurrencies.Design/methodology/approachA multivariate dynamic conditional correlation model is used to model the relationship of blockchain ETFs with equity and cryptocurrency markets. Risk-minimizing hedge ratios are calculated following the methods used in studies by Kroner and Sultan (1993) and Sadorsky (2012).FindingsThe empirical results show a high degree of correlation of blockchain ETF returns with returns of the NASDAQ Composite Index, while the level of comovement with Bitcoin is relatively low.Research limitations/implicationsThe results imply that blockchain ETFs may be suitable for hedging purposes in a portfolio holding Bitcoin. Furthermore, investing in blockchain ETFs appears similar to investing in NASDAQ.Originality/valueTo the best of the authors’ knowledge, no studies have investigated the dynamic relationship of blockchain ETFs and other financial assets.

Comparing high-dimensional conditional covariance matrices: Implications for portfolio selection

Abstract Portfolio selection based on high-dimensional covariance matrices is a key challenge in data-rich environments with the curse of dimensionality severely affecting most of the available covariance models. We challenge several multivariate Dynamic Conditional Correlation (DCC)-type and Stochastic Volatility (SV)-type models to obtain minimum-variance and mean-variance portfolios with up to 1000 assets. We conclude that, in a realistic context in which transaction costs are taken into account, although DCC-type models lead to portfolios with lower variance, modeling the covariance matrices as latent Wishart processes with a shrinkage towards the diagonal covariance matrix delivers more stable optimal portfolios with lower turnover and higher information ratios. Our results reconcile previous findings in the portfolio selection literature as those claiming for equicorrelations, a smooth dynamic evolution of correlations or correlations close to zero.

Liability driven investment with alternative assets: Evidence from Brazil

We study the portfolio choice problem for an asset-liability investor who invests in stocks, equity mutual funds, government bonds, short term interest, hedge funds, listed real estate, and commodities futures available in Brazil. Inflation and real interest play as important risk sources. We estimate the asset classes and liabilities time-varying conditional covariance structure using an asymmetric multivariate dynamic conditional correlation GARCH model and compare the asset-liability portfolio's global minimum variance allocation with Brazilian pension funds' market portfolio. The conditional covariance structure provides insights about the complex dynamic relationships between the asset classes and liabilities. We find that some (though not all) Brazilian alternative assets render strong diversification and liabilities hedging benefits for asset-liability investors. There are significant strategic asset allocation differences between the market portfolio and the liability driven portfolio as given by our model. We, therefore, question the Brazilian pension funds' allocation.

On the Comovements Among Gold and Oil: A Multivariate Time-Varying Asymmetric Approach

Previous studies have reported that there is a relationship among gold and oil prices. This research analyses how gold and oil prices variables interact focusing on different Global financial crisis (GFC) phases, we adopt a multivariate asymmetric dynamic conditional correlation GARCH framework, during the period spanning from January 1 st , 2000 until December 31 th , 2017. Our empirical results suggest correlations’ asymmetric responses among them. Moreover, the results indicate a correlations increase of gold and oil, during the crisis periods, suggesting different prices vulnerability. JEL classification numbers: E3 D4 Keywords: DCC-FIAPARCH model, Global financial crisis, Volatility DOI : 10.7176/JETP/9-7-01 Publication date :October 31 st 2019

On the co-movements among gold and other financial markets: a multivariate time-varying asymmetric approach

The analysis of time varying correlation between gold price and financial market in the context of international investments has been well researched in the literature in last few years. In this paper we study the interdependence of three financial markets and the gold price. We adopt a multivariate asymmetric dynamic conditional correlation EGARCH framework, during the period spanning from January 5, 1995 until May 30, 2017. The empirical results suggest asymmetric responses in correlations among gold market and financial market. We find that asymmetric dynamic correlations exhibit substantial time variation even in similar market tensions for same pairs of assets. Our findings imply that investors and fund managers should take into account the asymmetric dependence structure, which depends on the upside or downside of the market.

What They Did Not Tell You about Algebraic (Non-) Existence, Mathematical (IR-)Regularity, and (Non-) Asymptotic Properties of the Dynamic Conditional Correlation (DCC) Model

In order to hedge efficiently, persistently high negative covariances or, equivalently, correlations, between risky assets and the hedging instruments are intended to mitigate against financial risk and subsequent losses. If there is more than one hedging instrument, multivariate covariances and correlations have to be calculated. As optimal hedge ratios are unlikely to remain constant using high frequency data, it is essential to specify dynamic time-varying models of covariances and correlations. These values can either be determined analytically or numerically on the basis of highly advanced computer simulations. Analytical developments are occasionally promulgated for multivariate conditional volatility models. The primary purpose of this paper is to analyze purported analytical developments for the only multivariate dynamic conditional correlation model to have been developed to date, namely the widely used Dynamic Conditional Correlation (DCC) model. Dynamic models are not straightforward (or even possible) to translate in terms of the algebraic existence, underlying stochastic processes, specification, mathematical regularity conditions, and asymptotic properties of consistency and asymptotic normality, or the lack thereof. This paper presents a critical analysis, discussion, evaluation, and presentation of caveats relating to the DCC model, with an emphasis on the numerous dos and don’ts in implementing the DCC model, as well as a related model, in practice.

Forecasting Large Stochastic Covariance Matrices

Portfolio selection based on high dimensional covariance matrices is a key challenge in data-rich environments with the curse of dimensionality severely affecting most of the available covariance models. We challenge several multivariate Dynamic Conditional Correlation (DCC)-type and Stochastic Volatility (SV)-type models to obtain minimum variance and mean-variance portfolios with up to 1000 assets. We conclude that, in a realistic context in which transaction costs are taken into account, although DCC-type models lead to portfolios with lower variance, modeling the covariance matrices as latent Wishart processes with a shrinkage towards the diagonal covariance matrix delivers more stable optimal portfolios with lower turnover and higher information ratios. Our results reconcile previous findings in the portfolio selection literature as those claiming for equicorrelations, a smooth dynamic evolution of correlations or correlations close to zero.

Volatility transmission among Latin American stock markets under structural breaks

The paper investigates the volatility spillovers among five major Latin American (LA) stock markets under the presence of the structural breaks in variance. We employ a multivariate dynamic conditional correlation (DCC GARCH) model allowing for structural breaks in variance. The dynamic correlations show that volatility spillover effects among the markets are not strong. Causality in mean tests indicate one way causality from BOVESPA to all markets, whereas causality in variance tests indicate one way causality only from BOVESPA to IPSA. These findings suggest that while the markets in the sample are interdependent, there is not enough statistical evidence to infer the contagion effects among the markets.

Econometric analysis of financial cointegration of least developed countries (LDCs) of Asia and the Pacific

Purpose – The purpose of this paper is to econometrically investigate the level of financial co-integration of the least developed countries (LDCs) of Asia and Pacific region. In addition, the paper also tested the co-integration of LDCs with the world’s second largest economy “China.” For this, the paper employed the foreign exchange data sets of respective LDCs. It also aimed to assess the dynamic conditional correlation (DCC) between the foreign exchange rates of LDCs and China, and further, examined the past and current level of their co-relational dependence. Design/methodology/approach – The authors created data sets namely LDCs of Asia and Pacific, LDCs of SAARC, LDCs of ASEAN, LDCs of Pacific, LDCs of SAARC and ASEAN, LDCs of ASEAN and Pacific, and LDCs of SAARC and Pacific. In addition, the authors tested the co-integration of these seven groups with China, and thus, making a total of 14 data sets. The analysis was carried out using the Johansen and Gregory-Hansen multivariate co-integration econometric techniques. To assess the DCC, multivariate DCC GARCH model was employed. Findings – It was found that at the intra-regional level, exchange rates of LDCs of SAARC, ASEAN and Pacific were co-integrated and showed the existence of 1-3 co-integrating equations. At inter-regional level SAARC-ASEAN, ASEAN-Pacific and SAARC-Pacific were also co-integrated and showed 1-3 co-integrated equations. However, on the inclusion of China in the study, the degree of co-integration of exchange rate of China with LDCs of SAARC and ASEAN increased, while with Pacific, the result was mixed. Conditional correlation estimated of multivariate DCC GARCH model suggested that except for Afghanistan, there was an upward shift in the correlation dynamics of exchange rates of LDCs with China, post global financial crisis. Practical implications – Asia and Pacific region constituted of 53 countries, of which 13 were LDCs. Enhanced financial integration among LDCs of Asia-Pacific region and also between LDCs and major economies of the region like China will strengthen economic and financial integration efforts in the region. Originality/value – The present paper attempted a comparative assessment of the co-movements of the foreign exchange markets of LDCs, the countries which have remained largely neglected in academic discourses on financial integration.

Co-movement of major energy, agricultural, and food commodity price returns: A time-series assessment

Using monthly data between 1970 and 2013, we provide a comprehensive analysis of the extent of co-movement (measured by correlation coefficients) among the nominal price returns of 11 major energy, agricultural, and food commodities. We study the degree and the time evolution of unconditional and conditional correlations using a uniform-spacings estimation and testing approach, multivariate dynamic conditional correlation models, and a rolling regression procedure. We find that (1) the price returns of energy and agricultural commodities are highly correlated; (2) the overall level of co-movement among commodities increased in recent years, especially between energy and agricultural commodities, and in particular in the cases of maize and soybean oil, which are important inputs in the production of biofuels; and (3) the stock market volatility is positively associated with the co-movement of price returns across markets, especially after 2007.

Detecting nonlinear dependencies in eurozone peripheral equity markets: A multistep filtering approach

This paper assesses the presence of linear and nonlinear dynamic causal relations among the stock markets of the Eurozone peripheral countries, namely Portugal, Ireland, Italy, Greece and Spain (PIIGS countries). In addition to the pairwise analysis, this paper tests causality in a multivariate setting to take into account the effects of all variables by using a three-step multivariate filtering procedure. The paper also investigates the existence of nonlinear causal linkages of VAR/VECM filtered residuals to verify whether the observed causalities are strictly nonlinear and controls for conditional heteroskedasticity using a multivariate Dynamic Conditional Correlation (DCC) GARCH model to test the hypothesis of nonlinear noncausality. Most of the nonlinear causal linkages were purged after multivariate GARCH filtering, a fact which indicates that volatility effects primarily induce nonlinear causality. Yet in some cases that nonlinear links pertained, possibly peripheral stock returns may exhibit statistically significant higher-order moments. Our results may be useful in explaining a significant part of the (non)predictability of peripheral stock markets but more importantly in quantifying the process of financial markets and banking integration in the Eurozone.

Reducing Basel III Capital Requirements with Dynamic Conditional Correlation and Monte Carlo Simulation

Value-at-Risk (VaR) and Conditional-Value-at-Risk (CVaR) are popular risk measure in portfolio optimisation and market regulations. However, so far little research has been done on how these risk measures reduce the Basel III market risk capital requirements. This paper analyses the efficiency of empirical, parametric and simulation based VaR and CVaR optimised portfolios on the regulatory capital requirements. Furthermore, we show how the Population-Based Incremental Learning algorithm can be used to solve the constraint optimisation problems. We find that the parametric and empirical distribution assumption generate similar results and neither of them clearly outperforms the other. Our results indicate that portfolios optimised with a multivariate Dynamic Conditional Correlation simulation approach reduce the capital requirements by about 11%.

POLICY UNCERTAINTY AND FOREIGN EXCHANGE RATES: THE DCC-GARCH MODEL OF THE US / JAPANESE FOREIGN EXCHANGE RATE

Since the breakdown of the Bretton Woods system in the 1970s, the US / Japan foreign exchange rate has been largely influenced by policy changes in the United States and Japan. This study applies the multivariate dynamic conditional correlation (DCC) ? generalized autoregressive conditional heteroscedasticity (GARCH) models to analyze the time-varying effects of policy uncertainty, measured by the economic policy uncertainty (EPU) index of Baker et al. (2013, 2016), on the US / Japan foreign exchange rate. Using the EPU index as a proxy variable, it shows that the dynamic conditional correlations between policy uncertainty and the exchange rate are not time-invariant, but even sign-changing in the sample period. The analysis also empirically examined what drives the evolution of the time-varying correlations. The driving force of the correlations is, however, mostly attributed to unknown random factors.