• Title/Summary/Keyword: 다변량 GARCH

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Asymmetric CCC Modelling in Multivariate-GARCH with Illustrations of Multivariate Financial Data (금융시계열 분석을 위한 다변량-GARCH 모형에서 비대칭-CCC의 도입 및 응용)

  • Park, R.H.;Choi, M.S.;Hwan, S.Y.
    • The Korean Journal of Applied Statistics
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    • v.24 no.5
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    • pp.821-831
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    • 2011
  • It has been relatively incomplete in the field of financial time series to adapt asymmetric features to multivar ate GARCH processes (McAleer et al., 2009). Retaining constant conditional correlation(CCC) structure, this article pursues to introduce asymmetric GARCH modelling in analysing multivariate volatilities in time series in a practical point of view. Multivariate Korean financial time series are analyzed in detail to compar our theory with conventional methodologies including GARCH and EGARCH.

Multivariate volatility for high-frequency financial series (다변량 고빈도 금융시계열의 변동성 분석)

  • Lee, G.J.;Hwang, Sun Young
    • The Korean Journal of Applied Statistics
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    • v.30 no.1
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    • pp.169-180
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    • 2017
  • Multivariate GARCH models are interested in conditional variances (volatilities) as well as conditional correlations between return time series. This paper is concerned with high-frequency multivariate financial time series from which realized volatilities and realized conditional correlations of intra-day returns are calculated. Existing multivariate GARCH models are reviewed comparatively with the realized volatility via canonical correlations and value at risk (VaR). Korean stock prices are analysed for illustration.

On multivariate GARCH model selection based on risk management (리스크 관리 측면에서 살펴본 다변량 GARCH 모형 선택)

  • Park, SeRin;Baek, Changryong
    • 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.

Hedging effectiveness of KOSPI200 index futures through VECM-CC-GARCH model (벡터오차수정모형과 다변량 GARCH 모형을 이용한 코스피200 선물의 헷지성과 분석)

  • Kwon, Dongan;Lee, Taewook
    • 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.

Analysis of Multivariate-GARCH via DCC Modelling (DCC 모델링을 이용한 다변량-GARCH 모형의 분석 및 응용)

  • Choi, S.M.;Hong, S.Y.;Choi, M.S.;Park, J.A.;Baek, J.S.;Hwang, S.Y.
    • 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.

Extended Constant Conditional Correlation (ECCC) Model for Multivariate GARCH Time Series: an Illustration (다변량 GARCH 모형의 CCC 및 ECCC 비교분석)

  • Lee, Seung Yeon;Hwang, S.Y.
    • The Korean Journal of Applied Statistics
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    • v.27 no.7
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    • pp.1219-1228
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    • 2014
  • Constant conditional correlation (CCC) is frequently employed for parsimony in the field of multivariate GARCH time series. An extended-CCC (ECCC) model is further developed in order to allow interactions between multivariate volatilities. The paper introduces both CCC model and ECCC model to the domestic financial time series. The CCC and ECCC models are fitted and then compared with each other through various multivatiate time series.

Performance Comparison of Estimation Methods for Dynamic Conditional Correlation (DCC 모형에서 동태적 상관계수 추정법의 효율성 비교)

  • Lee, Jiho;Seong, Byeongchan
    • 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.

Assessments for MGARCH Models Using Back-Testing: Case Study (사후검증(Back-testing)을 통한 다변량-GARCH 모형의 평가: 사례분석)

  • Hwang, S.Y.;Choi, M.S.;Do, J.D.
    • The Korean Journal of Applied Statistics
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    • v.22 no.2
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    • pp.261-270
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    • 2009
  • Current financial crisis triggered by shaky U.S. banking system adds to the emphasis on the importance of the volatility in controlling and understanding financial time series data. The ARCH and GARCH models have been useful in analyzing economic time series volatilities. In particular, multivariate GARCH(MGARCH, for short) provides both volatilities and conditional correlations between several time series and these are in turn applied to computations of hedge-ratio and VaR. In this short article, we try to assess various MGARCH models with respect to the back-testing performances in VaR study. To this end, 14 korean stock prices are analyzed and it is found that MGARCH outperforms rolling window, and BEKK and CCC are relatively conservative in back-testing performance.

An Analysis on Mutual Shock Spillover Effects among Interest Rates, Foreign Exchange Rates, and Stock Market Returns in Korea (한국에서의 금리, 환율, 주가의 상호 충격전이 효과 분석)

  • Kim, Byoung Joon
    • International Area Studies Review
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    • v.20 no.1
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    • pp.3-22
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    • 2016
  • In this study, I examine mutual shock spillover effects among interest rate differences, won-dollar foreign exchange change rates, and stock market returns in Korea during the daily sample period from the beginning of 1995 to the October 16, 2015, using the multivariate GARCH (generalized autoregressive conditional heteroscedasticity) BEKK (Baba-Engle-Kraft-Kroner) model framework. Major findings are as follows. Throughout the 6 model estimation results of variance equations determining return spillovers covered from symmetric and asymmetric models of total sample period and two crisis sub-sample periods composed of Korean FX Crisis Times and Global Financial Crisis Times, shock spillovers are shown to exist mainly from stock market return shocks. Stock market shocks including down-shocks from the asymmetric models are shown to transfer to those other two markets most successfully. Therefore it is most important to maintain stable financial markets that a policy design for stock market stabilization such as mitigating stock market volatility.

A Study on the Volatilities of Inbound Tourists Arrivals using the Multivariate BEKK model (다변량 BEKK모형을 이용한 방한 외래 관광객의 변동성에 대한 연구)

  • Kim, Kyung-Soo;Lee, Kyung-Hee
    • 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.

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