Dynamic Conditional Correlation Analysis Research Articles

Return volatility of Asian stock exchanges; a GARCH DCC analysis with reference of Bitcoin and global crude oil price movement

PurposeThis study aims to investigate the conditional volatility of the Asian stock market concerning Bitcoin and global crude oil price movement.Design/methodology/approachThis study uses the newest Dynamic Conditional Correlation (DCC)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to examine the conditional volatility of the stock market for Bitcoin and crude oil prices in the Asian perspective. The sample stock market includes Chinese, Indian, Japanese, Malaysian, Pakistani, Singaporean, South Korean and Turkish stock exchanges, with daily time series data ranging from 4 April 2015−31 July 2023.FindingsThe outcome reveals the presence of volatility clustering on the return series of crude oil, Bitcoin and all selected stock exchanges of the current study. Secondly, the outcome of DCC, manifests that there is no short-run volatility spillover from crude oil to the Malaysian, Pakistani and South Korean and Turkish stock markets, whereas Chinese, Indian, Japanese, Singapore stock exchanges show the short-run volatility spillover from crude oil in the short run. On the other hand, in the long run, there is a volatility spillover effect from crude oil to all the stock exchanges. Thirdly, the findings suggest that there is no immediate spillover of volatility from Bitcoin to the stock markets return volatility of China, India, Malaysia, Pakistan, South Korea and Singapore. In contrast, both the Japanese and Turkish stock exchanges exhibit a short-term volatility spillover from Bitcoin. In the long term, a volatility spillover effect from Bitcoin is observed in all stock exchanges except for Malaysia. Lastly, based on the outcome of conditional variance, it can be concluded that there was increase in the return volatility of stock exchanges during the period of the COVID-19 pandemic.Research limitations/implicationsThe analysis below does not account for the bias induced due to certain small sample properties of DCC-GARCH model. There exists a huge literature that suggests other methodologies for small sample corrections such as the DCC connectedness approach. On the other hand, decisive corollaries of the conclusions drawn above have been made purely based on a comprehensive investigation of eight Asian stock exchange economies. However, there is scope for inclusive examination by considering other Nordic and Western financial markets with panel data approach to get more robust inferences about the reality.Originality/valueMost of the empirical analysis in this perspective skewed towards the Nordic and Western countries. In addition to that many empirical investigations examine either the impact of crude oil price movement or Bitcoin performance on the stock market return volatility. However, none of the examinations quests the crude oil and Bitcoin together to unearth their implication on the stock market return volatility in a single study, especially in the Asian context. Hence, current investigation endeavours to examine the ramifications of Bitcoin and crude oil price movement on the stock market return volatility from an Asian perspective, which has significant implications for the investors of the Asian financial market.

Non-Financial Markets and Interconnectedness between US and Emerging Financial Economies: Evidence from Covid-19 Financial Crisis

During times of financial turmoil, when traditional assets experience significant volatility, commodity markets provide diversification benefits to investors. The objective is to investigate the factors influencing financial contagion between the United States and emerging Asian equity markets (China and India). The study analyzes the influential impact of the volatility index, gold, oil, and USD index on financial contagion among the markets. The dynamic conditional correlation analysis is utilized to explore the correlations during the US subprime and Covid-19 crises, and quantile regression analysis is conducted at different levels of time-varying correlations. The study's results suggest that financial contagion becomes more pronounced during periods of financial turmoil, and global financial crises contribute to alterations in the dependence structure between financial contagion among equity markets and global macroeconomic risk factors. The effect of financial contagion can be abridged through altering portfolio reallocation strategies according to investors’ risk appetite during high market volatility.

Dynamic conditional correlation analysis of green and grey investments: Evidence from energy ETFs

Dynamic conditional correlation analysis of green and grey investments: Evidence from energy ETFs

Forecasting Value at Risk and Expected Shortfall of Foreign Exchange Rate Volatility of Major African Currencies via GARCH and Dynamic Conditional Correlation Analysis.

Research on the exchange rate volatility and dynamic conditional correlation of African currencies/financial markets interdependence appears to be limited. In this paper, we employ GARCH models to characterize the exchange rate volatility of eight major African currencies. The variation of interdependence with respect to time is described using the DCC-GARCH model. From the results of the DCC, remarkable variations in correlations through time across these countries are observed with the correlations varying from low to moderate, suggesting that African economies are generally governed by certain economic factors and are vastly regulated. These regulations, including exchange rate misalignment led to sluggish and negative growth in most of the African countries. For instance, persistent misalignment can cause high levels of inflation, for example, undervaluation. Overvaluation can lead to trade imbalances and they can in turn create macroeconomic instability and balance of payment problems. Given these results, we suggest that policy makers should revamp and adopt state resilience so as to reduce the negative effect of exchange rate misalignment on economic growth.

Can cryptocurrencies hedge oil price fluctuations? A pandemic perspective

The article aims to verify whether cryptocurrencies can hedge extreme price movements in Brent crude oil. The COVID-19 pandemic revealed that oil prices are heavily influenced by economic uncertainty and the mobility factor. We analyse Brent crude oil prices from February 10, 2020, to February 10, 2022. We consider Bitcoin, BNB, Ether, Tether, and USD Coin, the top five cryptocurrencies by market capitalization, as possible hedges. We explore their potential to protect oil investments using two approaches. The first focuses on price movement, and the second one on minimizing portfolio volatility. We use three modelling techniques: asymmetric causality in prices, a threshold vector-autoregressive model for returns, and dynamic conditional correlation analysis. We show that while stablecoins provide the best protection against downward movements in oil prices, they do not reduce investment volatility.

The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis

This study aims to evaluate the monitoring and predictive value of web-based symptoms (fever, cough, dyspnea) searches for COVID-19 spread. Daily search interests from Turkey, Italy, Spain, France, and the United Kingdom were obtained from Google Trends (GT) between January 1, 2020, and August 31, 2020. In addition to conventional correlational models, we studied the time-varying correlation between GT search and new case reports; we used dynamic conditional correlation (DCC) and sliding windows correlation models. We found time-varying correlations between pulmonary symptoms on GT and new cases to be significant. The DCC model proved more powerful than the sliding windows correlation model. This model also provided better at time-varying correlations (r ≥ 0.90) during the first wave of the pandemic. We used a root means square error (RMSE) approach to attain symptom-specific shift days and showed that pulmonary symptom searches on GT should be shifted separately. Web-based search interest for pulmonary symptoms of COVID-19 is a reliable predictor of later reported cases for the first wave of the COVID-19 pandemic. Illness-specific symptom search interest on GT can be used to alert the healthcare system to prepare and allocate resources needed ahead of time.

Oil price shocks and emerging stock markets revisited

PurposeThe study aims to understand the role of different streams of oil shocks (demand, supply and risk shocks) on the oil-importing and exporting countries' stock returns. The study also examines the impact of crude oil shocks across the economic regimes and market states. Besides, the role of the Global Financial Crisis (GFC) of 2008 in shaping the oil–stock relationship is also investigated.Design/methodology/approachThe authors revisit the impact of oil shocks on emerging equity markets by using the novel shock decomposition algorithm proposed by Ready (2018). The authors consider 24 emerging equity markets for the period spanning over July 15, 2002, to June 18, 2018, and bifurcate them based on oil dependence. The authors use rolling and dynamic conditional correlation analysis to understand the time-varying co-movements between oil prices and stock returns. The regime and state-specific dependence of stock returns on the structural oil shocks are captured by the Markov regime switching and quantile regression models.FindingsThe authors find that the demand shocks are positively associated with stock markets, whereas the supply shocks are negatively related, except in some of the oil-exporting countries. The risk-based shocks also appear to have a negative association with stocks. The authors do not find evidence of strong regime dependence and the direction of relationship across the high and low regimes is somewhat stable. Further, the authors observe an intense oil–stock relationship in the bearish market conditions. Besides, the authors also report evidences of changes in oil–stock relationship onset the GFC.Originality/valueThis is among the first studies to use the oil shock decomposition algorithm of Ready (2018) in the context of emerging equity markets. Additionally, oil shocks' role on the stock market movements across the regimes and market states is studied comprehensively. Thus, the nature of oil shock and the extent to which the emerging markets are exposed is observed in this study.

Contagion between liquid and illiquid assets during the financial crisis: evidence from the US credit derivative market

This study aims to investigate the existence of contagion between liquid and illiquid assets in the credit default swap (CDS) market around the recent financial crisis. The authors perform analyses based on vector autoregression model and the dynamic conditional correlation model. The estimation of vector autoregression models reveals that changes in liquid CDS (LCDS) spreads lead to changes in illiquid CDS spreads at least one week ahead during the financial crisis period, whereas the leading direction is reversed during the post-crisis period. Moreover, the results are robust after controlling for structural variables which are proven as determinants of CDS spreads and are empirically supported. This study interprets that information was incorporated first into the LCDSs because of the flight-to-liquidity during the recent crisis period but there is a default contagion effect by reflecting illiquidity-induced credit risk after the crisis. Finally, the dynamic conditional correlation analysis also confirms the main results.

Hedging effectiveness of precious metals across frequencies: Evidence from Wavelet based Dynamic Conditional Correlation analysis

This study examines the dynamic relationship between precious metals and stock markets of major developed (G7) and emerging (BRICS) nations. We use a hybrid wavelet-based Dynamic Conditional Correlation (DCC) approach, which allows us to investigate the dynamic relationship between precious metals and stock markets in time and frequency domain. Our results suggest that DCC between the return series of precious metals and stock market varies with timescales in terms of dynamicity, persistence and strength of relationship. G7 and BRICS nations exhibit different dynamicity with precious metals over the study period (2000 to 2017). The dynamics between precious metals and G7 stock markets show similar patterns, which depicts a clustering behaviour, however, same is not true for BRICS nations. In contrast to existing literature, this study found that silver offers better hedging capability than other precious metals for both short and long-run. To construct a two-asset optimal portfolio of precious metal and stock index, palladium emerges as the most favourable option for both short and long-run.

ECB’s unconventional monetary policy and cross-financial-market correlation dynamics

This paper examines the effects of the unconventional monetary policy (UMP) launched by the European Central Bank on the cross-market correlations between bond, stock and currency forward markets. Using a dynamic conditional correlation analysis and several robustness tests, we investigate possible differences on the correlation dynamics across four UMP periods and across a range of developed countries and emerging market economies. The empirical results indicate a spillover effect on both developed and emerging markets, although this impact is not identical across assets and countries. We also find that the new UMP phase started in 2014 has a more prominent impact, highlighting differences on the impact between the earlier and the new wave of UMPs and across cross-market correlations.

Dynamic Functional Connectivity States Between the Dorsal and Ventral Sensorimotor Networks Revealed by Dynamic Conditional Correlation Analysis of Resting-State Functional Magnetic Resonance Imaging

Functional connectivity in resting-state functional magnetic resonance imaging (rs-fMRI) has received substantial attention since the initial findings of Biswal et al. Traditional network correlation metrics assume that the functional connectivity in the brain remains stationary over time. However, recent studies have shown that robust temporal fluctuations of functional connectivity among as well as within functional networks exist, challenging this assumption. In this study, these dynamic correlation differences were investigated between the dorsal and ventral sensorimotor networks by applying the dynamic conditional correlation model to rs-fMRI data of 20 healthy subjects. k-Means clustering was used to determine an optimal number of discrete connectivity states (k = 10) of the sensorimotor system across all subjects. Our analysis confirms the existence of differences in dynamic correlation between the dorsal and ventral networks, with highest connectivity found within the ventral motor network.

Portfolio Effects of VIX Futures Index

This paper tests short-term and mid-term VIX indexes as a hedge and safe haven asset against U.S. stock risk from January 2006 through July 2016. GARCH dynamic conditional correlation analysis indicates that VIX indexes are an effective hedge due to the consistent inverse relationship between the VIX indexes and stocks. VIX indexes are either a strong or weak safe haven in times of extreme stock market volatility. Additionally, VIX indexes provide a strong safe haven during recent periods of turmoil including the 2008 global financial crisis, the 2011 downgrade of the U.S. government triple-A credit rating, and the 2016 U.K. vote to leave the E.U. (Brexit).

International contagion through financial versus non-financial firms

The role of financial firms in the transmission of financial shocks across countries is well recognized in the literature. However, contagion through non-financial firms has not received much attention. This study examines the role of financial vis-à-vis non-financial firms in transmitting shocks across countries using a dynamic conditional correlation analysis. We provide empirical evidence from a sample of 49 countries. A novel finding of our study is that non-financial firms play a more pronounced role in the cross-market transmission of shocks than financial firms. Financial contagion is positively related to the level of equity market development and bilateral trade intensity. It is higher during periods of US economic downturns and financial crises. Given that the extent of international contagion varies across economic states and is more prevalent in the non-financial than in the financial sector, this study has implications for global sector rotation strategies.

Hedging Emerging Market Stock Risk with Sovereign Credit Default Swaps

This paper tests sovereign credit default swaps (CDS) as a hedge and safe haven asset against stock index returns in a sample of 18 emerging markets from 2005-2014. GARCH dynamic conditional correlation analysis indicates that CDS are an effective hedge due to the consistent inverse relationship between CDS and the stock indexes of each country. CDS are largely a weak safe haven in times of extreme stock market volatility. However, during the 2008 U.S. financial crisis and the 2010 European debt crisis, CDS provide either a strong or weak safe haven in most countries.

Reducing Stock Risk with Hedge Funds

This paper tests the risk reduction properties of hedge fund investing against a sample of stocks ranging from 1990 through 2014. GARCH dynamic conditional correlation analysis indicates that hedge funds are a significant diversifier due to the consistent imperfect relationship between the hedge fund returns and the stock return indexes. Hedge funds serve as a weak safe haven in times of extreme stock market volatility. During periods of financial crisis, hedge funds also largely function as a weak safe haven. In contrast to their name, hedge funds do not provide a traditional “hedge” against stock risk.

The impact of crude oil price on Islamic stock indices of South East Asian countries: Evidence from MGARCH-DCC and wavelet approaches

Abstract: This paper is the first attempt at testing the ‘time-varying’ and ‘time-scale dependent’ volatilities of and correlations between the selected Islamic stock indices of South East Asian countries and selected commodities for enhancing portfolio diversification benefits. Consistent with the results of our VECM, our analysis based on the application of the recent wavelet technique MODWT, indicates that the Singapore Islamic index is leading the other Islamic indices and the commodities. From the point of view of portfolio diversification benefits, based on the extent of dynamic correlations between variables, our results suggest that an investor should be aware that the Philippine Islamic stock index is less correlated with the crude oil in the short run (as evidenced in the continuous wavelet transform analysis) and that an investor holding the crude oil can gain by including the Malaysian Islamic stock index in the portfolio (as evidenced in the Dynamic conditional correlation analysis).

Gold and Systemic Risk

This study tests gold as a hedge and safe haven asset against systemic risk in 21 emerging and developed countries from 1979 to 2012. Generalized autoregressive conditional heteroskedasticity (GARCH) dynamic conditional correlation analysis indicates that gold serves as an effective hedge against systemic risk. Gold also provides a safe haven in times of extreme market volatility and during periods of financial crises in most countries.

Dynamic Conditional Correlation Analysis Asia Pacific and Latin America Equity Market: Interdependence and Contagion

This research analyzed dynamic correlation in daily equity price data on thirteen Asia Pacific countries and five Latin America countries for period of 2003 to 2012. We identified two crisis periods in our research. The first periode is the global financial crisis trigerred by United States´s internal crisis, and the second is the financial crisis happened within the European countries. This empirical research using Dynamic Conditional Correlation as a multivariate GARCH method suggests that there is a high average dynamic correlation especially in the internal region of Asia Pacific and Latin America. In addition, there are also an interdependence and minor contagion effect in these several countries. Result of our research also finds that there are comfounding effects between these two crisis periods.

An asymmetric dynamic conditional correlation analysis of linkages of European financial institutions during the Greek sovereign debt crisis

This article employs the asymmetric dynamic conditional correlation (DCC) model to assess impacts of the recent sovereign debt crisis on the time-varying correlations of five European financial institutions holding large amounts of Greek sovereign bonds (National Bank of Greece, BNP Paribas, Dexia, Generali, and Commerzbank). Contrary to the results of preceding studies, we find significant increases in the correlations between several combinations of the financial institutions’ stock returns after the inception of the sovereign debt crisis, indicating contagion effects. Moreover, our findings show that the parameter of the standardized negative residuals is statistically significant in the case of DCC estimates between two specific institutions. This suggests that the conditional correlation of stock returns between the two institutions is more significantly influenced by negative shocks than by positive innovations to return.

An asymmetric DCC analysis of correlations among bank CDS indices

This study explores the time-varying correlations among the bank industry Credit Default Swap (CDS) indices for the EU, the UK and the US, using the asymmetric Dynamic Conditional Correlation (DCC) model developed by Cappiello et al. (2006). The main findings of the study include: (i) The correlations between each pair of bank CDS indices vary substantially over time. (ii) There is evidence of asymmetric dynamic correlations between the EU and the UK bank CDS indices. The correlations between them tend to be higher when responding to joint downward shocks. (iii) The conditional correlations between the US bank CDS and the UK and the EU bank CDS, respectively, exhibited significant drops immediately after the collapse of Lehman Brothers during the global financial crisis. (iv) The sovereign debt crisis dummy in Autoregressive (AR) models, applied to the estimated DCCs, is significantly positive for the UK and US bank CDSs, as shown by the increased correlations after the onset of the debt crisis.