• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.825-835
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• 2008

Multivariate GARCH(MGARCH) has been useful in financial studies and econometrics for modeling volatilities and correlations between components of multivariate time series. An obvious drawback lies in that the number of parameters increases rapidly with the number of variables involved. This thesis tries to resolve the problem by using dimension reduction technique. We briefly review both factor models for dimension reduction and the MGARCH models including EWMA (Exponentially weighted moving-average model), DVEC(Diagonal VEC model), BEKK and CCC(Constant conditional correlation model). We create meaningful portfolios obtained after reducing dimension through statistical factor models and fundamental factor models and in turn these portfolios are applied to MGARCH. In addition, we compare portfolios by assessing MSE, MAD(Mean absolute deviation) and VaR(Value at Risk). Various financial time series are analyzed for illustration.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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• pp.45-52
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• 2019

The current study aims to investigate the relationship between tax avoidance and firm risk in an emerging market called Iran. The study population consists of 400 observations and 80 companies listed on the Tehran Stock Exchange (TSE) over a five-year period during 2012 and 2016. The statistical model used in this study is a multivariate regression model; besides, the statistical technique used to test the hypotheses proposed in this research is panel data. The results showed that low effective tax rate (tax avoidance) is more consistent than the higher effective tax rate. Moreover, there is no significant relationship between tax avoidance and future tax rate volatility. The findings also proved that lower effective tax rates are positively associated with future stock price volatility. This implies that since Iranian firms have many financial problems because of economic sanctions, they have a tendency to delay the disclosure of bad news about their firms. Needless to say, when a huge number of negative news reaches its peak, they immediately will enter the market and lead to a remarkable fluctuation in stock prices.

• Journal of Korea Port Economic Association
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• v.26 no.4
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• pp.88-100
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• 2010

This paper investigates the influence of industrial activity volatility and exchange rate volatility on import container volume of the Korea during the 1999:1- 2010:9. Conditional variance from the GARCH(1, 1) model is applied as the volatility. The Johansen multivariate cointegration method and the error correction (general-to-specific) method are applied to study the relationship between import volume and its determinants. The empirical results show that volatility has statistically significant negative effect on import volume.

• Bulletin of the Korean Mathematical Society
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• v.53 no.3
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• pp.733-749
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• 2016

We aim to compute discretely monitored geometric Asian option prices under the Heston model. This method involves explicit formula for multivariate generalized Fourier transform of volatility process and their integrals over different time intervals using a recursive method. As numerical results, we illustrate efficiency and accuracy of our method. In addition, we simulate scenarios which show evidently practical importance of our work.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1333-1343
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• 2014

Hansen and Lund (2005) documented that a univariate GARCH(1,1) model is no worse than other sophisticated GARCH models in terms of prediction errors such as MSPE and MAE. Here, we extend Hansen and Lund (2005) by considering multivariate GARCH models and incorporating risk management measures such as VaR and fail percentage. Our Monte Carlo simulations study shows that multivariate GARCH(1,1) model also performs well compared to asymmetric GARCH models. However, we suggest that actual model selection should be done with care in light of risk management. It is applied to the realized volatilities of KOSPI, NASDAQ and HANG SENG index for recent 10 years.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.667-675
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• 2018

When a financial time series consists of daily (closing) returns, traditional volatility models such as autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) are useful to figure out daily volatilities. With high frequency returns in a day, one may adopt various multivariate GARCH techniques (MGARCH) (Tsay, Multivariate Time Series Analysis With R and Financial Application, John Wiley, 2014) to obtain intraday volatilities as long as the high frequency is moderate. When it comes to the ultra high frequency (UHF) case (e.g., one minute prices are available everyday), a new model needs to be developed to suit UHF time series in order to figure out continuous time intraday-volatilities. Aue et al. (Journal of Time Series Analysis, 38, 3-21; 2017) proposed functional GARCH (fGARCH) to analyze functional volatilities based on UHF data. This article introduces fGARCH to the readers and illustrates how to estimate fGARCH equations using UHF data of KOSPI and Hyundai motor company.

• Management & Information Systems Review
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• v.32 no.3
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• pp.1-23
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• 2013

In this study, we try to investigate the spillover effects of volatility in international tourists arrivals between Korea and US, Japan, China by using the multivariate BEKK model from January 2005 to January 2013. In the results of this study, after the global financial crisis, we found a cointegration relationship and tourist arrivals of Japan were adjusted to recovery in the short term. Also tourists arrivals from China and Japan showed the long-term elasticity. In the conditional mean equation of a BEKK model, there were the spillover effects. And in the conditional variance equation, ARCH(${\epsilon}^2_t$) coefficients showed a strong influence on the arrivals of their own and the spillover effects and the asymmetric effects on the volatility of China and Japan arrivals. In GARCH(${\sigma}^2_t$) coefficients showed the asymmetric effects and the spillover effects of the conditional volatility among source arrivals. Therefore, we examined the asymmetric reaction of one-way or two-way tourist arrivals between source countries and Korea and the spillover effects related to tourists arrivals of source countries to Korea. We has confirmed a causal relationship between some of the tourists arrivals from source countries to korea.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.615-624
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• 2015

We consider the estimation of seasonal cointegration in the presence of conditional heteroskedasticity (CH) using a feasible generalized least squares method. We capture cointegrating relationships and time-varying volatility for long-run and short-run dynamics in the same model. This procedure can be easily implemented using common methods such as ordinary least squares and generalized least squares. The maximum likelihood (ML) estimation method is computationally difficult and may not be feasible for larger models. The simulation results indicate that the proposed method is superior to the ML method when CH exists. In order to illustrate the proposed method, an empirical example is presented to model a seasonally cointegrated times series under CH.

• 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 the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1449-1466
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• 2014

In this paper, we consider a hedge portfolio based on futures of underlying asset. A classical way to estimate a hedge ratio for a hedge portfolio of a spot and futures is a regression analysis. However, a regression analysis is not capable of reflecting long-run equilibrium between a spot and futures and volatility clustering in the conditional variance of financial time series. In order to overcome such defects, we analyzed KOSPI200 index and futures using VECM-CC-GARCH model and computed a hedge ratio from the estimated conditional covariance-variance matrix. In real data analysis, we compared a regression and VECM-CC-GARCH models in terms of hedge effectiveness based on variance, value at risk and expected shortfall of log-returns of hedge portfolio. The empirical results show that the multivariate GARCH models significantly outperform a regression analysis and improve hedging effectiveness in the period of high volatility.