Dynamic Equicorrelation Model Research Articles

Dynamic Equicorrelation Analysis of Financial Contagion: Evidence from Latin America Markets

This research employs the multivariate autoregressive moving average-generalized autoregressive conditionally heteroscedastic-dynamic equicorrelation (ARMA-GARCH-DECO) model to identify contagion among Latin American financial markets during financial turmoil period. We analyze the dynamic conditional correlations among 18 American Depositary Receipts (ADR), 8 Exchange Traded Funds (ETF) and 6 Foreign Exchange Rates (Forex). Our sample includes daily closing prices from April 1, 2014, to January 29, 2021, for Argentina, Brazil, Chile, Colombia, Mexico, and Peru. Results find long-run properties in the volatility of most instruments including those belonging to defensive super sector implying that defensive super sector and basic materials are the most impacted sectors during the last financial crises. We present evidence that in times of economic disruption like in the midst of the COVID-19 pandemic, those financial assets do not act as safe harbor investments since they are relatively more correlated during period of financial crises than in normal periods. Our findings have policy implications and are of interest to practitioners who look a better understanding of the dynamics of spillovers among the behavior of emerging financial assets. JEL classification numbers: C58, D53, G15. Keywords: Dynamic equicorrelation model, Latin America, American Depositary Receipts, Exchange Traded Funds, Foreign Exchange Rates, ARMA-GARCH.

Correlated idiosyncratic volatility shocks

The commonality in idiosyncratic volatility cannot be fully explained by time-varying volatility; correlated idiosyncratic volatility shocks are an important contributing factor. We empirically document this fact for various characteristic-sorted portfolios and industry portfolios. To capture the commonality in idiosyncratic volatility, we propose a novel multivariate generalized autoregressive conditional heteroscedasticity (GARCH) model called dynamic factor correlation (DFC). The DFC model has a closed-form likelihood function that allows computationally cheap estimation, even for a large number of assets. The DFC model improves statistical fit compared with existing multivariate GARCH models such as the dynamic conditional correlation model and the dynamic equicorrelation (DECO) model, achieving the lowest root mean square error and mean absolute error in simulations. Empirical tests also corroborate simulations in demonstrating the improved statistical fit of the DFC model. Mean--variance portfolio optimization using the DFC model outperforms alternative volatility models such as the historical covariance matrix and the DECO model. Out-of-sample mean--variance-efficient portfolios using the DFC model have the lowest volatility and the highest Sharpe ratios, thereby improving the investor's opportunity set. Under parametric restrictions, the DFC model reduces to the constant conditional correlation model or the DECO model.

Directional spillover effects between ASEAN and world stock markets

We investigate dynamic spillovers between ASEAN-5 and world stock markets using a dynamic equicorrelation (DECO) model and the spillover index of Diebold and Yilmaz (2012), which identifies net directional spillovers for each one of the markets. The DECO model uses more information to calculate dynamic correlations between each pair of returns than standard dynamic conditional correlation models, decreasing the estimation noise of the correlations. Directional spillovers from world stock markets to ASEAN-5 stock markets are higher than in the opposite direction. Besides, our results indicate heterogeneity among the ASEAN-5 stock markets in the degree of spillover to world markets over time. We verify an increase in both return and volatility spillovers during financial crises, confirming the intensity of information transmission during periods of turmoil. These findings help understand the economic channels through which the ASEAN-5 equity markets are connected, and have important implications for emerging and frontier markets.

Time-varying diversification benefits of commodity futures

This paper analyzes the conditional diversification benefits (CDBs) of commodity futures. We utilize three distinct classes of empirical models in order to explore the additional value of commodities in stock portfolios. Firstly, the dynamic equicorrelation model is conducted which allows us to compute the average conditional correlations for a large number of assets. Secondly, we employ the dynamic conditional correlation (DCC) technique to examine pairwise correlations between commodity futures and equity markets. Thirdly, using the time-varying correlations derived from the DCC model, we quantify the diversification benefits through time within the context of CDB measure. By constructing six hypothetical portfolios, our results point out that the portfolio consisting of the commodity futures and the emerging stock markets exhibits the lowest equicorrelation level. The cross-sectional differences in the bivariate correlations show that the energy and metal futures have the highest level of co-movements with the equities. Our findings also reveal that the inclusion of commodity futures into the emerging and developed market portfolios increases the diversification benefits although these benefits deteriorate negligibly in the episodes of financial turmoil. The futures that offer the highest diversification benefits are lean hogs, feeder cattle, natural gas, orange juice, and gold. Our empirical results provide significant insights for portfolio managers and global investors to assess the gains from investments in commodity futures.

Was Guinea the Source of the Ebola Virus contagion? Evidence via a Dynamic Equicorrelation Model

Forecasting the death number and co-movement case evolution is critically important in investigating the Ebola virus spreading phenomenon. To this end, it is crucial to examine investigate patterns and trends in correlations over time using daily figures data relevant to three major West African countries (Guinea, Liberia and Sierra lione) during the period ranging from March 3, 2014 to February 02, 2015. The aim is to show that several countries co-movements are possible to be simultaneously modeled by means of DCC, ADCC, GDCC, AGDCC and DECO models. Empirically, it has been discovered that correlations have significantly registered an upward trend for both countries Guinea and Sierra-Lione. Based on a time-varying measure of dynamic conditional correlation, it turns out to be impossible for a long-only virus transmission by itself to circumvent the increasing correlations, by means of adjusting the Ebola outbreak over time. However, some evidence has been attained highlighting that the DECO estimation model is actually possible, and proved that the number of Ebola-related deaths does indeed increase after three month lapse. JEL Classification: G12

Dynamic Conditional Correlation Models in a Multiple Financial Asset Portfolio

A key concept in risk and portfolio management is diversification, which has recently come under intense scrutiny following dramatic market movements where historical correlation patterns have collapsed. To counter the devastating effects from these coordinated sell-off events, academics have developed several models to forecast correlation. This article examines two recent dynamic conditional correlation models: an asymmetric dynamic conditional correlation (ADCC) model and a dynamic equicorrelation (DECO) model. Previous research has demonstrated the merits of these advanced models in an artificial setting, but a study in a high-dimensional, though still artificial, setting was inconclusive on the merits. This article casts doubt on the merits of these models in a real-world setting. The results show that neither the ADCC model nor the DECO model provides any additional information over a sample-based approach when evaluated using a simple parametric covariance-variance value at risk (VaR) measurement. Used in risk monitoring, the models provide sound risk estimates but no additional insights into forecasting correlation spikes.