Conditional Correlation Approach Research Articles

Financial cycles synchronisation in South Africa. A dynamic conditional correlation (DCC) Approach

Financial cycles synchronisation in South Africa. A dynamic conditional correlation (DCC) Approach

Short-term effect of COVID-19 pandemic on cryptocurrency markets: A DCC-GARCH model analysis

This research examines the impact of the coronavirus index on the returns and volatility of ten major cryptocurrencies during the COVID-19 pandemic. For this purpose, we applied a multivariate volatility GARCH model with an integrated dynamic conditional correlation (DCC) approach to daily cryptocurrency values observed data during the January-December, 2020 period. Moreover, we used the Granger causality test to study return-volume correlations. The findings indicate that cryptocurrency volatility declined after the World Health Organization declared on March 11, 2020, that the coronavirus was a pandemic. Unlike most of the relevant previous studies, we found that the COVID-19 crisis did not have a long-term effect on cryptocurrency returns and volatility but only presented a short-term effect. Our results have implications for investors who need to determine an optimal portfolio for a scenario other than the base.

The domain-separation language network dynamics in resting state support its flexible functional segregation and integration during language and speech processing

Modern linguistic theories and network science propose that language and speech processing are organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct framewise time-varying language networks and k-means clustering was employed to investigate the temporal-reoccurring patterns. We found that the framewise language network dynamics in resting state were robustly clustered into four states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of speech perception and lexical-phonological processing, speech production, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and was regarded as a baseline state. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), state 4 was dominant. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in resting state, which forms a dynamic “meta-network” framework to support flexible functional segregation and integration during language and speech processing.

The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility: A Two-Stage DCC-EGARCH Model Analysis

This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility exponential GARCH (EGARCH) model with an integrated dynamic conditional correlation (DCC) approach to measure the impact on the financial portfolio returns from 2019 to 2020. Moreover, we used value-at-risk (VaR) and value-at-risk measurements based on the Cornish–Fisher expansion (CFVaR). The empirical results show significant long- and short-term spillover effects. The two-stage multivariate EGARCH model’s results show that the conditional volatilities of both asset portfolios surge more after positive news and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk when there are no external interruptions. Despite the financial assets’ sensitivity to shocks, they exhibit some resistance to fluctuations in market confidence. The VaR performance comparison results with the assets portfolios differ. During the COVID-19 outbreak, the Dow (DJI) index reports VaR’s highest loss, followed by the S&P500. Conversely, the CFVaR reports negative risk results for the entire cryptocurrency portfolio during the pandemic, except for the Ethereum (ETH).

Going green: Do green bonds act as a hedge and safe haven for stock sector risk?

The paper documents the hedging and safe haven benefits of green bonds in equities using the dynamic conditional correlation approach. Green bonds act as a strong hedge for eleven stock sectors in the USA. Additionally, they demonstrate a strong safe haven property with high-emission sectors for the entire study period and with all sectors except financials during the COVID-19 period. Interestingly, this hedging and safe haven benefit of green bonds is agnostic of the environmental disclosure score of a firm. Hence, investors can add green bonds to hedge their equity portfolios regardless of the environmental consciousness of their portfolio firms.

Volatility spillover effects between oil and GCC stock markets: a wavelet-based asymmetric dynamic conditional correlation approach

Purpose This study aims to examine the spillover effects of the mean and volatility between oil prices and stock indices of six Gulf Cooperation Council (GCC) countries (UAE, Kuwait, Saudi Arabia, Qatar, Oman and Bahrain). Design/methodology/approach Over the period 2008–2019, a bivariate VARMA-GARCH-ADCC model was combined with the maximal overlap discrete wavelet transform technique filter to shed light on a wide range of possible spillover effects in the mean and variances of level prices at various time horizons. Findings The authors find that the spillover effects between oil prices and the GCC stock markets are time-varying and spread across various time horizons. Besides, oil prices and stock market indices are directly impacted by their own shocks and variations and indirectly influenced by other price volatilities and wavelet scales. The linkages in volatility spillovers between oil prices and the GCC stock markets occur in the short-term, midterm and long-term horizons. More specifically, the results also show that the asymmetric estimates are statistically significant for the associations between oil prices and each stock market in the GCC countries. This implies that negative shocks play a more vital role than positive shocks in driving the dynamic condition correlations between oil and stock markets under study. Practical implications The significant interrelatedness between oil prices and each stock market in the GCC countries has important implications for investors, portfolio managers, and other market participants. They can use the findings of this research to create the best oil-GCC stock portfolios and predict more precisely the volatility spillover patterns in constructing their hedging strategies. Originality/value In several ways, this study differs from previous research. First, while previous empirical studies of the dynamic link between oil prices and stock markets have focused primarily on developed or emerging markets, the focus of this is on six GCC countries. Second, the linkage between oil prices and stock markets is typically studied at the original data level in the time domain in relevant literature, while frequency information is overlooked. Therefore, the current study examines this relationship from a multiscale perspective. Third, in this paper, to capture a wide range of possible spillover effects in the mean and variance of level prices at multiple wavelet scales, the authors use a VARMA-GARCH-ADCC model in conjunction with wavelet multiresolution analysis. Additionally, this article also applies wavelet hedge ratio and wavelet hedge portfolio analysis at various time horizons.

Hedging uncertainty with cryptocurrencies: Is bitcoin your best bet?

AbstractAre cryptocurrencies useful minimum‐variance hedging instruments? This paper develops a two‐step analytical framework to explore this question across time. First, it estimates dynamic optimal weights, calibrated when investing between the aggregate market and a respective sampled cryptocurrency. This is performed separately for 11 major cryptocurrencies using the dynamic conditional correlation approach of Engle. Second, using a fractional regression approach, it uncovers linkages between optimal weights in cryptocurrencies and sources of economic uncertainty. Overall, this paper makes the following important findings. First, optimal weights in cryptocurrencies all rose rapidly during the COVID‐19 pandemic. In all, bitcoin showed to be the leading cryptocurrency in terms of hedging effectiveness during this recent time period. Second, most cryptocurrencies exhibit zero or negative betas consistently across time, thus making them natural hedging instruments for investors seeking to reduce their portfolio's comovement with the market. Finally, cryptocurrencies serve as better hedges for economic uncertainties arising from equity and commodity markets. They are relatively less effective for uncertainties arising from risks in the banking industry and firm default risk. This paper contributes broadly to the asset pricing literature since our two‐step approach herein can tractably be extended to other asset classes or other econometric measures of systematic risk.

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.

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.

Do the Determinants of Non-Performing Loans Have a Different Effect over Time? A Conditional Correlation Approach

Several studies have explored the linkage between non-performing loans and major macroeconomic indicators, using a wide variety of methodologies, sometimes with different results. This occurs, we argue, because these relationships are generally derived in terms of correlation coefficients evaluated in certain time spans, which represent a sort of average level of correlations. However, such correlations are necessarily time-varying, because the relationships between bank loan indicators and macroeconomic variables could be stronger during particular periods or in correspondence with important economic events. We propose an empirical exercise using dynamic conditional correlation models, with constant and time-varying parameters. Applying these models to quarterly delinquency rates and an array of macroeconomic variables for the US, for the period 1985–2019, we find that the correlation is often negligible in this period except during periods of economic crises, in particular the early 1990 crisis and the subprime mortgage crisis.

Oil price effect on sectoral stock returns: A conditional covariance and correlation approach for Mexico

This paper analyzes the relationship between the volatility of oil price and selected sectoral stock returns in Mexico (industrials, materials, financials and consumer discretionary) by implementing a Diagonal VECH-type bivariate GARCH model in order to estimate conditional covariances and correlations. The econometric results suggest that there exists a statistically significant relationship between sector indices, as well as between Mexico’s aggregate stock exchange returns, and variations in oil prices. Conditional correlations suggest that during most of the analyzed period, the relationship between oil price fluctuations and sectoral stock returns is positive. The recommendation, supported by these results, is that investors should take into consideration the interaction between the analyzed variables in order to generate more robust risk-hedge strategies. An important limitation for this work is information availability at sector level in the country. The original contribution of this paper lies mainly in the analysis of the influence of oil prices over sectoral indices of the Mexican Stock Exchange. These results provide more support to the current that suggests that a price increase in oil has a direct spillover effect on stock market performance.

Dependence among Energy and Asian Emerging Stocks: A Dynamic Conditional Correlation Approach

Dependence among Energy and Asian Emerging Stocks: A Dynamic Conditional Correlation Approach

Illiquidity contagion and pricing of commonality risk: Evidence from a dynamic conditional correlation model

This paper investigates the pricing of the commonality in liquidity risk in the U.S. stock market by using a more comprehensive measure of market illiquidity cost that can reflect the liquidity condition of broader asset markets, and by forming portfolios in a way that is consistent with the definition of the commonality risk. Estimating a conditional version of the Liquidity-Adjusted Capital Asset Pricing Model by the Dynamic Conditional Correlation approach, we find a commonality risk premium that is higher than those derived from alternative measures. The premium is time varying, with values being higher during periods of market turmoil.

Correlation regimes in international equity and bond returns

Measuring comovements across international financial markets is important for policy purposes and portfolio management. We develop a new approach to analyse such comovements in relation to key state variables, such as equity market volatility and short-term interest rates. These state variables can identify regimes in comovements through a fast, tractable threshold model. The advantage over existing methods is that our model can be easily estimated and does not have a serious bias, even when applied to large asset portfolios. Out-of-sample portfolio evaluation shows that our method outperforms the standard dynamic conditional correlation approach, especially during the recent global financial crisis when financial market comovements experienced substantial regime shifts. Overall, we contribute to the empirical literature by shedding new light on the fundamental drivers of comovements across international financial markets which can help guide analysis of systemic risk, asset allocation and hedging.

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.

Economic engagement and within emerging markets integration

We explore the co-movements between emerging markets by employing dynamic conditional correlation approach. We additionally explore the factors that might drive the conditional correlations between emerging markets. We show that trade with the high income countries is a more important driver of the co-movements between emerging markets relative to trade with other emerging markets either within or outside the geographic region of the given country. We further document that the overall health of an economy, investment and market depth explain the correlation between emerging markets. Evidence is also provided that although, the recent emerging markets and global financial crises raised the correlation between emerging markets, not all country pair correlations increased around the period of the crisis. The findings show that economic engagement as opposed to geographic proximity is more relevant in describing within emerging markets integration. The findings suggest that diversification gains could be achieved by strategically investing across some emerging markets even in crisis periods.

Hedging stocks with oil

We study the feasibility of hedging stocks with oil. The Dynamic Conditional Correlation (DCC) approach allows for the calculation of optimal hedge ratios and corresponding hedge portfolio returns. Our results show that there are distinct economic benefits from hedging stocks with oil, although the effectiveness of hedging is both time-varying and market-state-dependent. The event of the Global Financial Crisis (GFC) is shown to affect the effectiveness of hedging. During the GFC, a positive jump in the hedge ratios occurs and hedge effectiveness increases. Among a set of common financial and macroeconomic drivers, we identify the implied volatility index VIX as being the most important. During times of global financial uncertainty, investors reduce stock positions more than commodity positions, thus VIX shocks negatively affect the portfolio returns of stock-oil hedges. The results also show that an appreciation of the U.S. dollar against the euro is associated with reduced hedge portfolio returns. From the GFC onwards, we document an increased significance of the gold price and the term spread in explaining hedge portfolio returns.

Short-term fiscal imbalance comparison in V4 countries using a dynamic conditional correlation approach

Research background: The crisis periods have highlighted the interdependence between individual European economies in the area of economic and fiscal development. The common development tendencies raise the question whether the fiscal indebtedness and preferred fiscal policy of V4 countries can be considered as interdepending. Considering this assumption, a possibility for implementing a similar type of consolidation in selected clusters of countries could be proposed.Purpose of the article: The research is, from the empirical point of view, focused on the analysis and comparison of primary balance indicator, analysis and assessment of fiscal measures (in identified fiscal episodes) and analysis of fiscal development intercorrelations using the dynamic conditional correlation approach.Methods: The empirical assessment of the research objective is, from the methodological point of view, divided into five phases: (1) the calculation of primary deficit indicator, (2) the analysis, comparison and assessment of the fiscal development in the V4 countries based on the annual primary deficit during the selected period 1999?2016, (3) the seasonal adjustment of performing time series and non-stationarity testing, (4) the dynamic conditional correlation approach (DCC) application. Within the analysis, the traditional and specific methods were used (time series analysis, content analysis, descriptive statistics, correlation methods, DCC approach).Findings & Value added: The results of the research suggest that between V4 countries do exist significant differences based on which cannot be clearly confirmed the assumption about the fiscal development interdependence in V4 countries. From the long-term perspective, it can be stated that the fiscal development in V4 countries is a very slightly positively related aspect, but in the context of the implementation of a common consolidation strategy it is not sufficient.

Asymmetric Dynamic Conditional Correlation Approach to Financial Contagion: A Study of Asian Markets

The study seeks to examine the contagion effect of the global financial crisis on the major Asian markets, namely, China, Hong Kong, Indonesia, South Korea, Malaysia, Japan, India and Taiwan. The study incorporates the impact of leverage effect in the dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (DCC-GARCH) framework to achieve the goal. The US is taken as the crisis-originating country. The period of study has been divided into three categories, namely, pre-crisis period, crisis period and post-crisis period, to examine the sudden change in average conditional correlation from one period to other period so as to identify the contagion effect. The findings support the contagion effect from US market to the major Asian markets with the exception of Japan. Policy makers seek to study the existence of contagion among markets so that they can strategically manage risk and it further helps in asset allocation. If the markets are contagious, then the investors will be unable to reap benefits through international diversification of portfolio. In such a case, the policy makers will further frame policies so that they can insulate themselves from inflicting heavy damage from various crises.

Time-Varying Bond Market Integration in EMU

We examine the level and progress of bond market integration amongst the eleven Economic and Monetary Union countries with active bond markets, over normal and crisis periods. The study covers data from January 2002 up to March 2014. We employ seven indicators for assessing integration, namely beta convergence, sigma convergence, variance ratio, Asymmetric Dynamic Conditional Correlation, dynamic co-integration, market synchronisation, and common factors approach. The results suggest that there is no heterogeneity in the integration process of large-sized economies and medium-sized economies, thereby restricting portfolio diversification potential. Further, bond market integration in the Economic and Monetary Union deteriorated during the crisis period, especially during the European debt crisis, with the economies of Greece, Ireland, Italy, Portugal, and Spain being the worst affected. We observe that bond markets take a lead in information linkages vis-a-vis stock markets, and hence should get precedence in policy intervention relating to market integration, development, and crisis management.