• Communications for Statistical Applications and Methods
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• v.21 no.6
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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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• v.7 no.12
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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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• v.30 no.2
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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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• v.18 no.4
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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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• v.17 no.3
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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.

• Korean Business Review
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• v.20 no.2
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• pp.15-27
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• 2007

This paper analyzed the relationship between Housing Purchase Price volatility of Seoul and Housing Purchase Price volatility of local large city. Other studies investigates the effect on the observed volatility Observed volatility consists of fundamental volatility and transitory volatility. Fundamental volatility is caused by information arrival and transitory volatility is caused by noise trading. Fundamental volatility is trend component and is modelled as a random walk with drift. Transitory volatility is cyclical component and is modelled as a stationary process. In contrast to other studies, this study investigates the effect on the fundamental volatility and transitory volatility individually. Observed volatility is estimated by GJR GARCH(1,1) model. We find that GJH GARCH model is superior to GARCH model and good news is more remarkable effect on volatility than bad news. This study decomposes the observed volatility into fundamental volatility and transitory volatility using Kalman filtering method. The findings in this paper is as follows. The correlation between Seoul housing price volatility and Busan housing price volatility is high. But, the correlation between Seoul and Daejeon is low. And the correlation between Daejeon and Busan is low. As a distinguishing feature, the correlation between fundamental volatilities is high in the case of all pairs. But, the correlation between transitory volatilities turns out low. The reason is as follows. When economic information arrives, Seoul, Daejeon, and Busan housing markets, all together, are affected by this information.

• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.139-169
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• 2009

This paper introduces the modified VaR which takes into account the asymmetry and fat-tails of financial asset distribution, and then compares its out-of-sample forecast performance with traditional VaR model such as historical simulation model and Riskmetrics. The empirical tests using stock indices of 6 countries showed that the modified VaR has the best forecast accuracy. At the test of independence, Riskmetrics and GARCH model showed best performances, but the independence was not rejected for the modified VaR. The Monte Carlo simulation using skew t distribution again proved the best forecast performance of the modified VaR. One of many advantages of the modified VaR is that it is appropriate for measuring VaR of the portfolio, because it can reflect not only the linear relationship but also the nonlinear relationship between individual assets of the portfolio through coskewness and cokurtosis. The empirical analysis about decomposing VaR of the portfolio of 6 stock indices confirmed that the component VaR is very useful for the re-allocation of component assets to achieve higher Sharpe ratio and the active risk management.

• The Korean Journal of Financial Management
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• v.13 no.1
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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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• v.9 no.10
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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.