• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.995-1005
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• 2009

Conditional correlation between financial time series plays an important role in risk management, asset allocation and portfolio selection and therefore diverse efforts for modeling conditional correlations in multivariate-GARCH processes have been made in last two decades. In particular, CCC (cf. Bollerslev, 1990) and DCC(dynamic conditional correlation, cf. Engle, 2002) models have been commonly used since they are relatively parsimonious in the number of parameters involved. This article is concerned with DCC modeling for multivariate GARCH processes in comparison with CCC specification. Various multivariate financial time series are analysed to illustrate possible advantages of DCC over CCC modeling.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.1013-1024
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• 2015

We compare the performance of two representative estimation methods for the dynamic conditional correlation (DCC) GARCH model. The first method is the pairwise estimation which exploits partial information from the paired series, irrespective to the time series dimension. The second is the multi-dimensional estimation that uses full information of the time series. As a simulation for the comparison, we generate a multivariate time series similar to those observed in real markets and construct a DCC GARCH model. As an empirical example, we constitute various portfolios using real KOSPI 200 sector indices and estimate volatility and VaR of the portfolios. Through the estimated dynamic correlations from the simulation and the estimated volatility and value at risk (VaR) of the portfolios, we evaluate the performance of the estimations. We observe that the multi-dimensional estimation tends to be superior to pairwise estimation; in addition, relatively-uncorrelated series can improve the performance of the multi-dimensional estimation.

• Korea Real Estate Review
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• v.28 no.2
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• pp.21-34
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• 2018

The purpose of this study is to investigate the effect of macro-finance variables on the correlation between the housing and stock markets because understanding the nature of time-varying correlations between different assets has important implications on portfolio allocation and risk management. Thus, we adopted the AG-DCC GARCH model to obtain time-varying, conditional correlations. Our sample ranged from January 2004 to November 2017. Our empirical result showed that the coefficients on asymmetric correlation were significantly positive, implying that correlations between the housing and stock markets were significantly higher when changes in the housing price and stock returns were negative. This finding suggested that the housing market has less hedging potential during a stock market downturn, when such a hedging strategy might be necessary. Based on the regression analysis, we found that the term spread had a significantly negative effect on correlations, while the credit spread had a significantly positive effect. This result could be interpreted by the risk premium effect.

• Korea Trade Review
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• v.48 no.4
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• pp.189-208
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• 2023

This paper analyzed the dynamic conditional correlation between the Korean ETS market, energy market and stock market. This paper conducted an empirical analysis using daily data of Korea's carbon credit trading price, WTI crude oil futures price, and KOSPI index from February 2, 2015 to December 30, 2021. First, the volatility of the three markets was analyzed using the GARCH model, and then the dynamic conditional correlations between the three markets were studied using the bivariate DCC-GARCH model. The research results are as follows. First, it was found that the Korean ETS market has a higher rate of return and higher investment risk than the stock market. Second, the yield volatility of the Korean ETS market was found to be most affected by external shocks and least affected by the volatility information of the market itself. Third, the correlation between the Korean ETS market and the stock market was stronger than that of the WTI crude oil futures market. This paper analyzed the correlation between the Korean ETS market, energy market, and stock market and confirmed that the level of financialization in the Korean ETS market is quite low.

• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.199-226
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• 2009

We examine the intertemporal relation between risk and return in the Korean stock market and foreign exchange market based on the two factor ICAPM framework. The standard GARCH model and the GJR(1993) model are employed to estimate conditional variances of the stock returns and foreign exchange rates. The covariance between the rates of stock returns and changes in the exchange rates are estimated by the constant conditional correlation model of Bollerslev(1990) and the dynamic conditional correlation model of Engle(2002). The multivariate GARCH in mean model and quasi-maximum likelihood estimation method, consequently, are applied to investigate riskreturn relation jointly. We find that the estimated coefficient of relative risk aversion is negative and statistically significant in the post-financial crisis sample period in the Korean stock market. We also show that the expected stock returns are negatively related to the dynamic covariance with foreign exchange rates. Both estimated parameters of conditional variance and covariance in the foreign exchange market, however, are not statistically significant. The GJR model is better than the standard GARCH model to estimate the conditional variances. In addition, the dynamic conditional correlation model has higher explanatory power than the constant correlation model. The empirical results of this study suggest following two points to investors and risk managers in hedging and diversifying strategies for their portfolios in the Korean stock market: first, the variability of foreign exchange rates should be considered, and second, time-varying correlation between stock returns and changes in foreign exchange rates supposed to be considered.

• International Area Studies Review
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• v.21 no.4
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• pp.223-245
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• 2017

This study is examined interaction between carbon market with stock market using a multivariate GARCH(DCC) model. Carbon market is EU ETS EUA price, stock market is the iron and cement stock price which has relatively energy intensive and massive carbon emissions sector in the industrial sector. It also analyzed changes in the correlation between the markets through an analysis of correlation coefficients. Moreover, it checked whether there was marketability expansion(or expansion of carbon emissions reduction) through the analysis above. As a result of empirical tests, it showed that the price spillover effect was insignificant. In addition, it represented that there was a weak correlation between the two markets since the volatility spillover effect disappeared in the second phase by an external shock(a financial crisis). Moreover, it was revealed that there were no significant changes although there was a weak upward trend in terms of the correlation between the carbon market and the stock market. This implies that emission rights could not expand marketability to financial market as a commodity(or did not play its natural role of the reduction of carbon emission).

• Journal of Korea Port Economic Association
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• v.34 no.4
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• pp.125-140
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• 2018

This study uses daily price data on three major types of crude oil (Brent, Dubai, and WTI) and BDI from January 2, 2009 to June 29, 2018, to compare the relationship between crude oil prices and BDI for rate of change and volatility. Unlike previous studies, the correlation between BDI and crude oil prices was analyzed both the rate of change and variability, VARs, Granger Causality Test, and the GARCH and DCC models were employed. The correlation analysis, indicated that the crude oil price change rate and volatility affect the BDI change rate and that BDI volatility affects the crude oil price change rate and volatility. The relationship between oil prices and BDI is identified, but their correlation is low, which is likely a result of lower dependence on crude oil as demand for natural gas increases worldwide and demand for renewable energy decreases. These trends could result in lower correlations over time. Therefore, focusing on the changing demand for raw materials in future investments in international shipping(real economy) and oil markets and macroeconomic analysis is necessary.