Dynamic Conditional Correlation GARCH Model Research Articles

Optimal Dynamic Hedging via Copula-Threshold-GARCH Models

The contribution of this paper is twofold. First, we exploit copula methodology, with two threshold GARCH models as marginals, to construct a bivariate copula threshold GARCH model, simultaneously capturing asymmetric nonlinear behaviour in univariate stock returns of spot and futures markets and bivariate dependency, in a flexible manner. Two elliptical copulas (Gaussian and Student-t) and three Archimedean copulas (Clayton, Gumbel and the Mixture of Clayton and Gumbel) are utilized. Secondly, we employ the presenting models to investigate the hedging performance for five East Asian spot and futures stock markets: Hong Kong, Japan, Korea, Singapore and Taiwan. Compared with conventional hedging strategies, including Engle’s dynamic conditional correlation GARCH model, the results show that hedge ratios constructed by a Gaussian or Mixture copula are the best-performed in variance reduction for all markets except Japan and Singapore, and provide close to the best returns on a hedging portfolio over the sample period.

Price Discovery in Real Estate Markets: A Dynamic Analysis

Although the correlation between the public and private market pricing of real estate has generated considerable research effort, the methods utilized in previous studies have failed to capture the dynamic nature of this correlation. This paper proposes a new statistical method to address this issue. This method, known as the dynamic conditional correlation GARCH model, enables us to study the dynamics of the correlation between the two markets over time and enrich our understanding of the public and private market pricing of real assets. We find that the correlation between NAV returns and REIT returns is dynamic for all REIT types and there is a strong degree of persistence in the series of correlation. Our Granger-causality tests show that price discovery generally takes place in the securitized public market. However, we also find significant variations across property types and individual firms within each type. Our results indicate that constructing an optimal portfolio requires firm level analysis of causality and correlation between REIT returns and NAV returns.

Intraday return and volatility spill‐over across international copper futures markets

PurposeThe purpose of this paper is to investigate the short‐run return and volatility spill‐overs across three major international copper futures markets: London Metal Exchange (LME), New York Mercantile Exchange (NYMEX), and Shanghai Futures Exchange (SHFE).Design/methodology/approachThe analysis utilizes a dynamic conditional correlation GARCH model to explore the return and volatility relationships.FindingsThe return and volatility spill‐overs between the two developed markets, LME and NYMEX, are bi‐directional and significantly stronger when the NYMEX operates an electronic trading system. In addition, significant bi‐directional return spill‐over between the LME (developed market) and the SHFE (emerging market) and significant uni‐directional volatility spill‐over from the LME to the SHFE are documented.Research limitations/implicationsThe evidence suggests that degree of market integration and trading mechanism play crucial roles in the return and volatility transmission across the three major copper futures markets. Higher level of market integration and easy access to trading information lead to faster information dissemination and help to establish stronger relationships of returns and volatility across the markets. This is consistent with the findings in the equity markets.Originality/valueThe study provides empirical evidence of short‐run information transmission between developed and emerging copper futures markets.

Optimal dynamic hedging via copula-threshold-GARCH models

The contribution of this paper is twofold. First, we exploit copula methodology, with two threshold GARCH models as marginals, to construct a bivariate copula-threshold-GARCH model, simultaneously capturing asymmetric nonlinear behaviour in univariate stock returns of spot and futures markets and bivariate dependency, in a flexible manner. Two elliptical copulas (Gaussian and Student's- t) and three Archimedean copulas (Clayton, Gumbel and the Mixture of Clayton and Gumbel) are utilized. Second, we employ the presenting models to investigate the hedging performance for five East Asian spot and futures stock markets: Hong Kong, Japan, Korea, Singapore and Taiwan. Compared with conventional hedging strategies, including Engle's dynamic conditional correlation GARCH model, the results show that hedge ratios constructed by a Gaussian or Mixture copula are the best-performed in variance reduction for all markets except Japan and Singapore, and provide close to the best returns on a hedging portfolio over the sample period.

Benefits of diversifying investments into emerging markets with time-varying correlations: An Australian perspective

Australian investors can reduce their overall portfolio risk by diversifying into equities from other markets. Emerging markets have attracted significant interest because of their low correlations with Australian equity market returns; however, a number of studies have indicated that correlations between equity returns are increasing over time, so using unconditional estimates of correlations in a portfolio optimization model can result in the selection of a portfolio that may not be optimal. We use an Asymmetric Dynamic Conditional Correlation GARCH model to estimate time-varying correlations and include these correlation estimates in the portfolio optimization model. The assets used for portfolio construction comprise seven emerging market indices that are available to foreign investors. This study finds that, despite increasing correlations, there are still potential benefits for Australian investors who diversify into international emerging markets.

The comovements in international stock markets: new evidence from Latin American emerging countries

We analyse the time variations in the comovements of Latin American stock markets. Conditional correlations are estimated from the dynamic conditional correlation GARCH model. Then, Bai and Perron's (2003) structural break technique is employed to test for changing nature of market comovements. Main findings are as follows. First, the degree of cross-market comovements changed over time and has significantly increased since 1994. However, room for international diversification still remains largely possible. Second, the comovements are subjected to various regime shifts, essentially due to major economic events. Finally, stock markets move much more together in times of crisis.

Dynamic hedging with futures: A copula‐based GARCH model

AbstractIn a number of earlier studies it has been demonstrated that the traditional regression‐based static approach is inappropriate for hedging with futures, with the result that a variety of alternative dynamic hedging strategies have emerged. In this study the authors propose a class of new copula‐based GARCH models for the estimation of the optimal hedge ratio and compare their effectiveness with that of other hedging models, including the conventional static, the constant conditional correlation (CCC) GARCH, and the dynamic conditional correlation (DCC) GARCH models. With regard to the reduction of variance in the returns of hedged portfolios, the empirical results show that in both the in‐sample and out‐of‐sample tests, with full flexibility in the distribution specifications, the copula‐based GARCH models perform more effectively than other dynamic hedging models. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:1095–1116, 2008

Correlation Dynamics between Asia-Pacific, EU and US Stock Returns

This paper investigates the correlation dynamics in the equity markets of 13 Asia-Pacific countries, Europe and the US using the asymmetric dynamic conditional correlation GARCH model (AG-DCC-GARCH) introduced by Cappiello, Engle and Sheppard (2006). We find significant variation in correlation between markets through time. Stocks exhibit asymmetries in conditional correlations in addition to conditional volatility. Yet asymmetry is less apparent in less integrated markets. The Asian crisis acts as a structural break, with correlations increasing markedly between crisis countries during this period though the bear market in the early 2000s is a more significant event for correlations with developed markets. Our findings also provide further evidence consistent with increasing global market integration. The documented asymmetries and correlation dynamics have important implications for international portfolio diversification and asset allocation.

Time-Varying Comovements in Developed and European Stock Markets: Evidence from Intraday Data

We study comovements between three developed (France, Germany, the United Kingdom) and three emerging (the Czech Republic, Hungary and Poland) European stock markets. The novelty of our paper is that we apply the Dynamic Conditional Correlation GARCH models proposed by Engle (2002) to five-minute tick intraday stock price data for the period from June 2003 to January 2006. We find a strong correlation between the German and French markets and also between these two markets and the UK stock market. By contrast, very little systematic positive correlation can be detected between the Western European stock markets and the three stock markets of Central and Eastern Europe, as well as within the latter group.

The comovement between output and prices: Evidence from a dynamic conditional correlation GARCH model

Estimation results based on a multivariate dynamic conditional correlation GARCH model reveal that the overall price level tended to move in the same direction as output in periods before World War II but in the opposite direction after the war.

Decomposing the Co-Movement of the Business Cycle: A Time-Frequency Analysis of Growth Cycles in the Euro Area

This article analyses the frequency components of European business cycles using real GDP by employing multiresolution decomposition (MRD) with the use of maximal overlap discrete wavelet transforms (MODWT). Static wavelet variance and correlation analysis is performed, and phasing is studied using co-correlation with the euro area by scale. Lastly dynamic conditional correlation GARCH models are used to obtain dynamic correlation estimates by scale against the EU to evaluate synchronicity of cycles through time. The general findings are that euro area members fall into one of three categories: i) high and dynamic correlations at all frequency cycles (e.g. France, Belgium, Germany), ii) low static and dynamic correlations, with little sign of convergence occurring (e.g. Greece), and iii) low static correlation but convergent dynamic correlations (e.g. Finland and Ireland).