• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.487-497
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• 2007

As is pointed out by Gourieroux (1997), the volatility effects in financial time series vary according to the signs of the return rates and therefore asymmetric Threshold-GARCH (TGARCH, henceforth) processes are natural extensions of the standard GARCH toward asymmetric volatility modeling. For preliminary detection of asymmetry in volatility, we suggest graphs of squared-log-returns for various financial time series including KOSPI, KOSDAQ and won-Euro exchange rate. Next, asymmetric TGARCH(1,1) model fits are provided in comparisons with standard GARCH(1.1) models.

• Korea Trade Review
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• v.47 no.4
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• pp.215-231
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• 2022

Analysis and forecasting of the Baltic Capsize Index (BCI) is important for managing an entity's losses and risks from the uncertainty and volatility of the fast-changing maritime transport market in the future. This study conducted volatility transition analysis through the GARCH model, using BCI which is highly related to steel raw materials. As for the data, 2,385 monthly data were used from March 1999 to March 2021. In this study, after basic statistical analysis, unit root and cointegration test, the GARCH, EGARCH, and DCC-GARCH models were used for volatility transition analysis. As the results of GARCH and EGARCH model, we confirmed that all variables had no autocorrelation between the standardized residuals for error terms and the square of residuals, that the variability of all variables at this time was likely to persist in the future, and that the variability of the time-series error term impact according to Iron ore trade (IoT). In addition, through the EGARCH model, the magnitude convenience of all variables except the Iron ore price (IOP) and Capesize bulk fleet (BCF) variables was greater than the positive value (+). As a result of analyzing the DCC-GARCH (1,1) model, partial linear combinations were confirmed over the entire period. Estimating the effect of variability transition on BCF and C5 with statistically significant linear combinations with BCI confirmed that the impact of BCF on BCI was greater than the impact of BCI itself.

• Asia-Pacific Journal of Business
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• v.11 no.1
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• pp.61-75
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• 2020

Purpose - The purpose of this study was to examine the efficiency of the foreign exchange markets in Korea, Japan and China. Design/methodology/approach - This study collected 1327 observations each of the daily closing exchange rates of the three currencies against the US dollar for the sample period from January 1, 2015 to January 31, 2020, based on the tests for autocorrelation, unit root tests and GARCH-M(1,1) model estimation. Findings - We have found that the autocorrelation test indicates the lack of autocorrelation and unit root test confirms the existence of unit roots in all times series of the three currencies, respectively. The GARCH-M(1,1) test results, however, suggest that the exchange rates do not follow a random walk process. In conclusion, the recent spot foreign exchange markets in Korea, Japan and China are believed to be informationally inefficient. Research implications or Originality - These findings have practical implications for both individual and institutional investors to be able to obtain excess returns on their investments in the foreign exchange markets in three countries by using appropriate risk management, portfolio strategy, technical analysis, etc. This study provides the first empirical examination on the foreign exchange market efficiency in the three biggest economies in Asia including China, which has been excluded from research due to its exchange rate regime.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.189-204
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• 2018

This study is on forecasting the electricity consumption of an industrial manufacturing building called GGM from January 2014 to April 2017. We fitted models using SARIMA, SARIMA + GARCH, Holt-Winters method and ARIMA with Fourier transformation. We also forecasted electricity consumption for one month ahead and compared the predicted root mean square error as well as the predicted error rate of each model. The electricity consumption of GGM fluctuates weekly and annually; therefore, SARIMA + GARCH model considering both volatility and seasonality, shows the best fit and prediction.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.713-722
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• 2020

Contrasted with the standard symmetric GARCH models, we consider a broad class of threshold-asymmetric models to analyse financial time series exhibiting asymmetric volatility. By further introducing power transformations, we add more flexibilities to the asymmetric class, thereby leading to power transformed and asymmetric volatility models. In particular, the paper is concerned with the nonstationary volatilities in which conditions for integrated volatility and explosive volatility are separately discussed. Dow Jones Industrial Average is analysed for illustration.

• Management & Information Systems Review
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• v.38 no.1
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• pp.181-199
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• 2019

The purpose of this study is to examine whether or not the volatility of the 1997~1998 Asian crisis still affects the monthly stock returns of Korea, Japan, Singapore, Hong Kong and China from 1980 to 2018. This study investigated whether the volatility has already fallen to pre-crisis levels. To illustrate the possible structural changes in the unconditioned variance due to the Asian financial crisis, we use the MRS-GARCH model, which is a regime switching model. The main results of this study were as follows: First, the stock return of each country was weak in the high volatility regime except Japan resulted by the Asian financial crisis from 1997 to 1998 until March 2018, and the Asian stock market has not yet calmed down except for the global financial crisis period of 2007 and 2008. Second, the conditional volatility has been significantly and persistently decreased and eliminated after the Asian financial crisis. Thus, we could be judged that the Asian stock market was not fully recovered(stable) due to the Asian crisis including the capital liberalization high inflation, worsening current account deficit, overseas low interest rates and expansion of credit growth in 1997 and 1998, but the Asian stock market was largely settled down, except for the 2007 and 2008 in Global financial crises. Considering the similarity between the Asian stock markets and the similar correlation of the regime switching, it may be worthwhile to analyze the MRS-GARCH model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.212-217
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• 2020

The purpose of this study was to analyze the volatility and properties of a time series for tangerine prices in Jeju using the GARCH model of Bollerslev(1986). First, it was found that the time series for the rate of change in tangerine prices had a thicker tail rather than a normal distribution. At a significance level of 1%, the Jarque-Bera statistic led to a rejection of the null hypothesis that the distribution of the time series for the rate of change in tangerine prices is normally distributed. Second, the correlation between the time series was high based on the Ljung-Box Q statistic, which was statistically verified through the ARCH-LM test. Third, the results of the GARCH(1,1) model estimation showed statistically significant results at a significance level of 1%, except for the constant of the mean equation. The persistence parameter value of the variance equation was estimated to be close to 1, which means that there is a high possibility that a similar level of volatility will be present in the future. Finally, it is expected that the results of this study can be used as basic data to optimize the government's tangerine supply and demand control policy.

• Communications for Statistical Applications and Methods
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• v.20 no.3
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• pp.207-215
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• 2013

In this paper, we implement an empirical study to test whether the time series of daily returns in stock and Won/USD exchange markets is strictly stationary or explosive. The results indicate that only a few series show nonstationary volatility when dramatic events erupted; in addition, this nonstationary behavior occurs more often in the Won/USD exchange market than in the stock market.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.143-150
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• 2020

This study aims to investigate the effect of oil price and exchange rate on the two Vietnamese stock market indices: VN index and HXN index. This study uses the daily data from August 1^st 2000 to October 25^th 2019 of the two Vietnamese stock indices: VN index and HNX index, the two oil price indices: BRENT and WTI, and the two exchange rates: US dollar to Vietnamese dong and Euro to Vietnamese dong. Due to the presence of heteroskedasticity in our data, we use GARCH (1,1) regression model to perform our analysis. Our findings show that the oil price has a significant positive effect on the two Vietnamese stock market indices. In terms of the stock index volatility, both the VN index and HNX index volatilities are negatively impacted by the return of oil price. While the conclusion about the impact of oil price remained consistent through all three robustness tests, the effect of exchange rate on Vietnamese stock market indices is not consistent. We find thatchanges of the USD/VND exchange rate significantly impact the return and volatility of HNX index only in GARCH (1,1) setting. Our analysis also survives a number of robustness tests.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1373-1383
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• 2016

The wind energy industry and wind power generation have increased; consequently, the stable supply of the wind power has become an important issue. It is important to accurately predict the wind power with short-term basis in order to make a reliable planning for the power supply and demand of wind power. In this paper, we first analyzed the speed, power and the directions of the wind. The neural network and the time series models (ARMA, ARMAX, ARMA-GARCH, Holt Winters) for wind power generation forecasting were compared based on mean absolute error (MAE). For one to three hour-ahead forecast, ARMA-GARCH model was outperformed, and the neural network method showed a better performance in the six hour-ahead forecast.