• Communications for Statistical Applications and Methods
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• 제21권6호
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• pp.501-512
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• 2014

Stylized facts on asset return are fat-tail, asymmetry, volatility clustering and structure changes. This paper simultaneously captures these characteristics by introducing a multi-regime models: Finite mixture distribution and regime switching GARCH model. Analyzing the daily KOSPI return from $4^{th}$ January 2000 to $30^{th}$ June 2014, we find that a two-component mixture of t distribution is a good candidate to describe the shape of the KOSPI return from unconditional and conditional perspectives. Empirical results suggest that the equality assumption on the shape parameter of t distribution yields better discrimination of heterogeneity component in return data. We report the strong regime-dependent characteristics in volatility dynamics with high persistence and asymmetry by employing a regime switching GJR-GARCH model with t innovation model. Compared to two sub-samples, Pre-Crisis (January 2003 ~ December 2007) and Post-Crisis (January 2010 ~ June 2014), we find that the degree of persistence in the Pre-Crisis is higher than in the Post-Crisis along with a strong asymmetry in the low-volatility (high-volatility) regime during the Pre-Crisis (Post-Crisis).

• The Journal of Asian Finance, Economics and Business
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• 제7권12호
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• pp.1-10
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• 2020

This study investigates the problem of outlier detection based on discrete wavelet transform in the context of time series data where the identification and treatment of outliers constitute an important component. An outlier is defined as a data point that deviates so much from the rest of observations within a data sample. In this work we focus on the application of the traditional method suggested by Tukey (1977) for detecting outliers in the closed price series of the Saudi Arabia stock market (Tadawul) between Oct. 2011 and Dec. 2019. The method is applied to the details obtained from the MODWT (Maximal-Overlap Discrete Wavelet Transform) of the original series. The result show that the suggested methodology was successful in detecting all of the outliers in the series. The findings of this study suggest that we can model and forecast the volatility of returns from the reconstructed series without outliers using GARCH models. The estimated GARCH volatility model was compared to other asymmetric GARCH models using standard forecast error metrics. It is found that the performance of the standard GARCH model were as good as that of the gjrGARCH model over the out-of-sample forecasts for returns among other GARCH specifications.

• Communications for Statistical Applications and Methods
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• 제30권2호
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• pp.163-178
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• 2023

GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey's ladder, and Yeo-Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCHX(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey's ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen's independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey's ladder transformation.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.915-925
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• 2007

Multivariate GARCH has been useful to model dynamic relationships between volatilities arising from each component series of multivariate time series. Methodologies including EWMA(Exponentially weighted moving-average model), DVEC(Diagonal VEC model), BEKK and CCC(Constant conditional correlation model) models are comparatively reviewed for bivariate time series. In addition, these models are applied to evaluate VaR(Value at Risk) and to construct joint prediction region. To illustrate, bivariate stock prices data consisting of Samsung Electronics and LG Electronics are analysed.

• East Asian Economic Review
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• 제17권3호
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• pp.311-332
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• 2013

This paper examines the effect of initial margin requirements on long-run and short-run volatilities in the Japanese stock market using the Component GARCH model. Our empirical results show that when we do not divide the margin requirement into positive and negative changes, increasing margin requirement is effective for reducing long-run volatility, while not effective in short-run volatility. However, separating the positive and negative changes in margin requirements reveals the fact that the negative changes in margin requirements decrease long-run volatilities, while the higher margin requirements increase short-run volatilities in the Japanese stock market. This suggests that if the Japanese financial authorities intend to increase margin level to reduce volatility, unexpectedly, short-run volatility would be even higher.

• 산학경영연구
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• 제20권2호
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• pp.15-27
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• 2007

본 연구에서는 서울, 대전, 부산의 주택매매가격종합지수 변동성간의 상관관계에 대해 분석하였다. 기존의 연구에서는 시장에서 관찰되는 관측변동성을 이용하여 분석하였으나 본 연구에서는 통계적 방법을 이용하여 관측변동성을 내재가치의 변화에 의해 발생되는 기본적 변동성과 추종거래 등과 같은 잡음거래(noise trading)에 의해 발생되는 일시적 변동성으로 분해하여 락 변동성간의 관계를 분석하였다. 분석 결과 서울 주택매매가격 변동성과 두산 주택매매가격 변동성의 상관관계가 관측변동성 기본적 변동성, 일시적 변동성 모두 높게 나타나고 있다. 기본적 변동성의 경우는 관측변동성의 경우보다 상관관계가 놀게 나타났는데 기본적 변동성은 정보에 의해 발생하는 지속적인 변동성 부분이므로 각 시장에 공통적으로 영향을 주기 때문에 상관관계가 놀게 나타난 것으로 판단된다.

• 재무관리연구
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• 제26권3호
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• pp.139-169
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• 2009

본 연구는 새로운 VaR 추정모형으로 수정 VaR(modified VaR)을 소개하고, 수정 VaR의 예측성과를 역사적 시뮬레이션 모형이나 Riskmetrics 등 전통적인 모형들과 비교하였다. 수정 VaR은 분산뿐만 아니라 왜도, 첨도를 VaR 계산에 고려함으로써 금융자산분포의 비대칭성과 꼬리가 굵은 성질이 위험측정치에 반영될 수 있는 장점이 있다. 수정 VaR은 6개국의 주가지수 수익률을 이용한 표본외 예측성과검증에서 다른 모형들에 비해 가장 우수한 예측성과를 보였다. VaR 예측의 독립성검증에서는 Riskmetrics와 GARCH 모형이 우수한 것으로 나타났으나 수정 VaR에 대해 서도 독립성이 기각되지 않았다. 특정한 표본을 이용한 예측성과분석에서 나타날 수 있는 data snooping 문제를 해결하기 위해 skew t 분포를 이용한 시뮬레이션분석을 시도하였는데, 시뮬레이션 검증결과에서도 수정 VaR이 가장 양호한 예측성과를 보였다. 포트폴리오 VaR에 대한 표본외 예측성과에서도 수정 VaR은 단일변량모형이나 다변량 정규분포모형에 비해 우수한 성과를 보였다. 다변량 수정 VaR은 포트폴리오 구성자산 간의 선형상관관계뿐 아니라 공왜도(coskewness)와 공첨도(cokurtosis)를 통한 비선형 상호의존관계도 고려할 수 있다는 점에서 포트폴리오 위험에 대한 우수한 예측성과는 당연한 결과라고 할 수 있다. 6개국 주가지수로 구성된 포트폴리오의 VaR을 component VaR로 분할한 실증분석에서는 포트폴리오 VaR의 분할결과가 적극적인 위험관리와 포트폴리오 최적화를 위한 자산재배분에 효과적으로 활용될 수 있음을 확인하였다.

• 재무관리연구
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• 제13권1호
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• pp.341-357
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• 1996

Some empirical studies have shown that asset prices respond to announcements of economic news, however, others also have found little evidence. This study assesses how market participants of the S&P 500 Index Futures reacted to the U.S. economic news announcements. For this purpose, using a GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model, we use several U.S. news variables, its each surprise component and interest rates. We find that some economic news variables affected significantly on the S&P 500 Index Futures. In other words, we find that weekend variable, lagged volatility, and surprise component of trade deficit increased level of volatility. However, interest rate, M1, unemployment announcements caused the variance of the S&P 500 Index Futures to reduce, and each of the surprise component of M1 and trade deficit increased it. The result suggests that resolution of uncertainty, through economic news announcement, while, in some cases, causes market participants to reduce their forecast of volatility, a large difference between the market's forecast and the realization of the series causes the volatility to increase.

• The Journal of Asian Finance, Economics and Business
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• 제9권10호
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• pp.49-59
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• 2022

This paper examined the impact of real exchange rate volatility on trade balance in Malaysia by using quarterly data from year 2000 until 2019. Generalized Autoregressive Heteroscedasticity (GARCH) model was used to extract the volatility component of real exchange rate before examining its impact on trade balance. Furthermore, Autoregressive Distributed Lag (ARDL) model was used to investigate the long-run relationship and short-run dynamic between trade balance, money supply, national income and volatility of exchange rate. Empirical results show the existence of co-movement between variables under study in the long-run. However, the results also suggest that volatility of real exchange rate does not significantly affect trade balance neither in the long-run nor short-run. The risk which is associated in the movement of exchange rate do not influence trader's behaviour toward Malaysia exports and imports. Thus, it should be note that any depreciation or appreciation in Malaysian Ringgit do not have an impact towards trade balance either it is being further improved or deteriorates. Hence, exchange rate volatility may not be too concern for policymakers. This may be partially due to manage floating exchange rate regime that has been adopted by Malaysia eventually eliminated the element of risk in the currency market.