Dynamic Conditional Correlation Multivariate GARCH Research Articles

Currency hedging behavior for stock returns uncertainty in Ghana

ABSTRACT This article investigates the dynamic interaction between stock market returns and exchange rate movement and their implication for portfolio risk management in Ghana. To do so, and considering the Covid-19 crisis, the Dynamic Conditional Correlation Multivariate GARCH estimation technique was employed on daily data for Ghana Stock Exchange (GSE) Composite Index and the Ghana Cedi to USD, Euro, and GBP exchange rates from 3 January 2012 to 26 November 2021. The results revealed that: (i) the GSE market has no significant exposure to the USD, Euro, and GBP exchange rates in the full sample and periods before the crisis but was significantly exposed to the Euro rates during the crisis, (ii) the returns on holding foreign currencies were relatively higher but riskier compared to returns on stocks for the full sample and periods before the crisis, and (iii) while the Euro rates act as the most efficient hedge currency for stock returns uncertainty the correlations between stock returns and exchange rate movement were generally low and therefore, forming a portfolio of stocks and currency pairs improved an investor’s daily returns and risk. Based on the findings, relevant policy suggestions are offered to guide investors and policymakers.

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.

Relationships among the Fossil Fuel and Financial Markets during the COVID-19 Pandemic: Evidence from Bayesian DCC-MGARCH Models

This study examined how the relationships among the fossil fuel, clean energy stock, gold, and Bitcoin markets have changed since the COVID-19 pandemic took place for hedging the price change risks in the fossil fuel markets. We applied the Bayesian Dynamic Conditional Correlation-Multivariate GARCH (DCC-MGARCH) models using US daily data from 2 January 2019 to 26 February 2021. Our results suggest that the fossil fuel (WTI crude oil and natural gas) and financial markets (clean energy stock, gold, and Bitcoin) generally had negative relationships in 2019 before the pandemic prevailed, but they became positive for a while in mid-2020, alternating between positive (0.8) and negative values (−0.8). As it is known that negative relationships are required among assets to hedge the risk of price changes, this implies that stakeholders need to be cautious in hedging the risk across the fossil fuel and financial markets when a crisis like COVID-19 occurs. However, our study also revealed that such negative relationships only lasted for three to six months, suggesting that the effects of the pandemic were short term and that stakeholders in the fossil fuel markets could cross hedge with the financial markets in the long term.

Assessing the impact of COVID-19 on major industries in Japan: A dynamic conditional correlation approach

This study assesses the impact of the novel coronavirus disease (COVID-19) cases on the Japanese stock market. As of October 30, 2020, the cumulative number of cases in Japan has reached over one hundred thousand. COVID-19 has significantly affected both the lifestyle and the economy in Japan. First, this study develops composite stock indices by industry sector and prefecture, taking into consideration the effects of the increase in infections on industries and firms in the core prefectures. Second, this study investigates the dynamic conditional correlations between the composite stock index returns and the increment in COVID-19 cases using dynamic conditional correlation multivariate GARCH models. Finally, it can contribute to financial research in terms of coexistence of regional business economies with COVID-19.

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.

Assessing the Impact of COVID-19 on Major Industries in Japan: A Dynamic Conditional Correlation Approach

This study assesses the impact of the novel coronavirus disease (COVID-19) cases on the Japanese stock market. As of October 30, 2020, the cumulative number of cases in Japan has reached over one hundred thousand. COVID-19 has significantly affected both the lifestyle and the economy in Japan. First, this study develops composite stock indices by industry sector and prefecture, taking into consideration the effects of the increase in infections on industries and firms in the core prefectures. Second, this study investigates the dynamic conditional correlations between the composite stock index returns and the increment in COVID-19 cases using dynamic conditional correlation multivariate GARCH models. Finally, it can contribute to financial research in terms of coexistence of regional business economies with COVID-19.

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 Time-Varying Correlation between Crude oil Future and USA Bond Markets During 2005-2020: Evidence from a DCC-GARCH Model

In this paper, we examine potential time-varying correlations between crude oil future and USA bond markets. We employ a dynamic conditional correlation (DCC) multivariate GARCH model in order to quantify potential contagion effects between the markets for the period 2005-2020. We divide the period in two sub-period to make the empirical analysis easier. Empirical results reveal increased conditional correlation in the first sub-period (2005-2012) and no contagion in the second sub-period (2012-2020). Results are of interest to investors, who invest long-term into the under investigation financial markets, as well as, to policymakers, who provide regulations for the under investigation derivate market.

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.

Banks’ equity performance and the term structure of interest rates

AbstractUsing an extensive global sample, this paper investigates the impact of the term structure of interest rates on bank equity returns. Decomposing the yield curve to its three constituents (level, slope and curvature), the paper evaluates the time‐varying sensitivity of the bank's equity returns to these constituents by using a diagonal dynamic conditional correlation multivariate GARCH framework. Evidence reveals that the empirical proxies for the three factors explain the variations in equity returns above and beyond the market‐wide effect. More specifically, shocks to the long‐term (level) and short‐term (slope) factors have a statistically significant impact on equity returns, while those on the medium‐term (curvature) factor are less clear‐cut. Bank size plays an important role in the sense that exposures are higher for SIFIs and large banks compared to medium and small banks. Moreover, banks exhibit greater sensitivities to all risk factors during the crisis and post‐crisis periods compared to the pre‐crisis period; though these sensitivities do not differ for market‐oriented and bank‐oriented financial systems.

Hedging and effectiveness of Indian currency futures market

PurposeThe purpose of this paper is to measure the effectiveness of the hedging with futures currency contracts. Measuring the effectiveness of hedging has become mandatory for Indian companies as the new Indian accounting standards, Ind-AS, specify that the effectiveness of hedges taken by the companies should be evaluated using quantitative methods but leaves it to the company to choose a method of evaluation.Design/methodology/approachThe paper compares three models for evaluating the effectiveness of hedge – ordinary least square (OLS), vector error correction model (VECM) and dynamic conditional correlation multivariate GARCH (DCC-MGARCH) model. The OLS and VECM are the static models, whereas DCC-MGARCH is a dynamic model.FindingsThe overall results of the study show that dynamic model (DCC-MGARCH) is a better model for calculating the hedge effectiveness as it outperforms OLS and VECM models.Practical implicationsThe new Indian accounting standards (Ind-AS) mandates the calculation of hedge effectiveness. The results of this study are useful for the treasurers in identifying appropriate method for evaluation of hedge effectiveness. Similarly, policymakers and auditors are benefitted as the study provides clarity on different methods of evaluation of hedging effectiveness.Originality/valueMany previous studies have evaluated the efficiency of the Indian currency futures market, but with rising importance of hedging in the Indian companies, Reserve Bank of India’s initiatives and encouragement for the use of futures for hedging the currency risk and now the mandatory accounting requirement for measuring hedging effectiveness, it has become more relevant to evaluate the effectiveness of hedge. To the authors’ best knowledge, this is one of the first few papers which evaluate the effectiveness of the currency future hedging.

The Time-Varying Correlation between Crude oil Future and USA Bond Markets During 2005-2020: Evidence from a DCC-GARCH Model

In this paper, we examine potential time-varying correlations between crude oil future and USA bond markets. We employ a dynamic conditional correlation (DCC) multivariate GARCH model in order to quantify potential contagion effects between the markets for the period 2005-2020. We divide the period in two sub-period to make the empirical analysis easier. Empirical results reveal increased conditional correlation in the first sub-period (2005-2012) and no contagion in the second sub-period (2012-2020). Results are of interest to investors, who invest long-term into the under investigation financial markets, as well as, to policymakers, who provide regulations for the under investigation derivate market.

Modeling exchange rate volatility in the gambia using dynamic conditonal correlaton model

The relationship between different international stock markets is of importance for both financial practitioners and academicians in order to manage risks. Especially after the financial crisis, the pronounced financial contagion draws the public attention to look into such associations. However, measuring and modelling dependence structure becomes complicated when asset returns present non-linear, non-Gaussian and dynamic features. This paper examines the time-varying conditional correlations to the weekly exchange rate returns for the USD, EURO and GBP against the Gambian Dalasi (GMD) during the period 2000 to 2017. We use a dynamic conditional correlation (DCC) multivariate GARCH model. This model can be simplified by estimating univariate GARCH models for each return series, and then, using transformed residuals resulting from the first stage, estimating a conditional correlation estimator. DCC-GARCH model was implemented for two different assumptions of the error distribution; assuming Gaussian and Student t-distribution. Empirical results show substantial evidence of significant increase in conditional correlation. It is also clean that, the Student t-distributed errors better forecast the conditional correlation.

The Dynamics of Exchange Rate and Stock Return Before and After the Fed Policy Normalization: Evidence from Fragile Five Countries

Abstract: This research aims to examine the correlation between exchange rate and stock price return in each fragile five countries; Indonesia, Brazil, India, Turkey and South Africa. Using daily data, we investigate and then divide it into two periods; before Fed funds rate normalization (2013-2015) and after normalization (2016-2018), to find out whether the Fed funds rate hike caused a difference in the correlation between the two variables in each fragile five country. The methods used for this analysis are granger causality test and Vector Autoregression (VAR) using Eviews 9 program. Further investigation by analyzing the Dynamic Conditional Correlation-Multivariate GARCH (DCC MGARCH) method using Stata 15 program, which aims to find out the dynamic correlation between stock markets and also between currencies in fragile five countries. Granger test results found a difference in the relationship between variable exchange rates and stock price returns in Indonesia, India, and Turkey after the Fed normalization. Additionally, we learn that exchange rate lead stock price return in these three countries. Furthermore, the results of the DCC MGARCH test show that there is a significant positive dynamic correlation on the stock price index returns between markets. Moreover, we found similar results in testing positive and significant dynamic correlations between the exchange rates of each country.&#x0D; Key words: fragile five, exchange rate, stock return, VAR, DCC MGARCH

PORTFOLIO DIVERSIFICATION AND OIL PRICE SHOCKS: A SECTOR WIDE ANALYSIS

This paper investigates the time-varying relationship between the oil price and disaggregated stock market of India using Dynamic conditional correlation multivariate GARCH and Continuous Wavelet Transformation modelling approaches. Our findings reveal the evolving relationship between the oil price and disaggregated stock market. The correlations are generally volatile before the 2007-08 crisis but since then the correlations are positive implying no diversification benefits for the investors during rising oil prices. As emerging markets in general, and India in particular, is expected to increase its share of oil consumption in the world's energy market, therefore for the stock market to grow, especially the oil-intensive industries, we recommend the government should increase its reliance on alternative energy resources. Furthermore, as rising oil prices can also have its adverse effect through exchange rate channel, we suggest the monetary policies should be time varying to manage the oil inflationary pressures arising out of extreme volatility in the oil prices.Keywords: DCC-GARCH, CWT, Disaggregated stock market, India, Oil price shocks, Diversification.JEL Classifications: C50; G10; O53; Q43DOI: https://doi.org/10.32479/ijeep.7747

Futures market efficiency and effectiveness of hedge in Indian currency market

PurposeCorporate treasurers manage the currency risk of their organization by hedging through futures contracts. The purpose of this paper is to evaluate the effectiveness of hedging by US currency futures contracts by taking into account the efficiency of the currency market.Design/methodology/approachThe static models for calculating hedge ratio are as popular as dynamic models. But the main disadvantage with the static models is that they do not consider important properties of time series like autocorrelation and heteroskedasticity of the residuals and also ignore the cointegration of the market variables which indicate short-run market disequilibrium. The present study, therefore, measures the hedging effectiveness in the US currency futures market using two dynamic models – constant conditional correlation multivariate generalized ARCH (CCC-MGARCH) and dynamic conditional correlation multivariate GARCH (DCC-MGARCH).FindingsThe study finds that both the dynamic models used in the study provide similar results. The relative comparison of CCC-MGARCH and DCC-MGARCH models shows that CCC-MGARCH provides better hedging effectiveness result, and thus, should be preferred over the other model.Practical implicationsThe findings of the study are important for the company treasurers since the new updated Indian accounting standards (Ind-AS), applicable from the financial year 2016–2017, make it mandatory for the companies to evaluate the effectiveness of hedges. These standards do not specify a quantitative method of evaluation but provide the flexibility to the companies in choosing an appropriate method which justifies their risk management objective. These results are also useful for the policy makers as they can specify and list the appropriate methods for evaluating the hedge effectiveness in the currency market.Originality/valueMajorly, the studies on Indian financial market limit themselves to either examining the efficiency of that market or to evaluate the effectiveness of the hedges undertaken. Moreover, most of such works focus on the stock market or the commodity market in India. This is one of the first studies which bring together the concepts of efficiency of the market and effectiveness of the hedges in the Indian currency futures market.

Stock Net Entropy: Evidence from the Chinese Growth Enterprise Market.

By introducing net entropy into a stock network, this paper focuses on investigating the impact of network entropy on market returns and trading in the Chinese Growth Enterprise Market (GEM). In this paper, indices of Wu structure entropy (WSE) and SD structure entropy (SDSE) are considered as indicators of network heterogeneity to present market diversification. A series of dynamic financial networks consisting of 1066 daily nets is constructed by applying the dynamic conditional correlation multivariate GARCH (DCC-MV-GARCH) model with a threshold adjustment. Then, we evaluate the quantitative relationships between network entropy indices and market trading-variables and their bilateral information spillover effects by applying the bivariate EGARCH model. There are two main findings in the paper. Firstly, the evidence significantly ensures that both market returns and trading volumes associate negatively with the network entropy indices, which indicates that stock heterogeneity, which is negative with the value of network entropy indices by definition, can help to improve market returns and increase market trading volumes. Secondly, results show significant information transmission between the indicators of network entropy and stock market trading variables.

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.

Chemical industry disasters and the sectoral transmission of financial market contagion

Industrial incidents causing injury and fatality generate substantial costs to publicly traded firms. Risks associated with these potential incidents are not limited to only those companies that might be directly involved. Theoretically, stock markets are designed to self-regulate safety standards by decreasing company valuations should an incident occur, due to anticipated increased costs. This paper examines the sectoral consequences of such incidents in the United States, that is, the spillover effects of an industrial incident on firms operating in similar industrial operations. This is identified through the transmission of contagion within stock market sectors at the time of such incidents using a dynamic conditional correlation (DCC) multivariate GARCH methodology. The results indicate larger incidents, as measured by the number of injuries and fatalities, generate the largest contagion effects. Also, smaller competitor companies experience the largest contagion as measured by volatility effects as a result of sectoral chemical incidents, indicating that investors perceive increased risk factors and/or additional regulatory costs when valuing these companies in the period thereafter. We hypothesise that the observed reaction originates from expected increases in future sectoral operating costs through anticipated legislative and/or regulatory changes as well as uncertainty in the period immediately after an incident.

Estimating value-at-risk using a multivariate copula-based volatility model: Evidence from European banks

This paper proposes a multivariate copula-based volatility model for estimating Value-at-Risk (VaR) in the banking sector of selected European countries by combining dynamic conditional correlation (DCC) multivariate GARCH (M-GARCH) volatility model and copula functions. Non-normality in multivariate models is associated with the joint probability of the univariate models' marginal probabilities –the joint probability of large market movements, referred to as tail dependence. In this paper, we use copula functions to model the tail dependence of large market movements and test the validity of our results by performing back-testing techniques. The results show that the copula-based approach provides better estimates than the common methods currently used and captures VaR reasonably well based on the differences in the numbers of exceptions produced during different observation periods at the same confidence level.