• Communications for Statistical Applications and Methods
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• v.22 no.5
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• pp.495-506
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• 2015

Volatility is a variation measure in finance for returns of a financial instrument over time. GARCH models have been a popular tool to analyze volatility of financial time series data since Bollerslev (1986) and it is said that volatility is highly persistent when the sum of the estimated coefficients of the squared lagged returns and the lagged conditional variance terms in GARCH models is close to 1. Regarding persistence, numerous methods have been proposed to test if such persistency is due to volatility shifts in the market or natural fluctuation explained by stationary long-range dependence (LRD). Recently, Lee et al. (2015) proposed a residual-based cumulative sum (CUSUM) test statistic to test volatility shifts in GARCH models against LRD. We propose a bootstrap-based approach for the residual-based test and compare the sizes and powers of our bootstrap-based CUSUM test with the one in Lee et al. (2015) through simulation studies.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.291-305
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• 2014

Persistence is one of the typical characteristics appearing in the volatility of financial time series. According to the recent researches, the volatility persistence may be due to either volatility shifts or long-range dependence. In this paper, we consider residual-based CUSUM tests to distinguish volatility persistence, long-range dependence and volatility shifts in GARCH models. It is observed that this test procedure achieve reasonable powers without a size distortion. Moreover, we employ AIC and BIC criteria to estimate the change points and the number of change points in volatility. We demonstrate the superiority of residual-based CUSUM tests on various Monte Carlo simulations and empirical data analysis.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.177-182
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• 2012

A structural break in the level as well as in the innovation variance has often been exhibited in economic time series. In this paper we propose robust unit root tests based on a sign-type test statistic when a time series has a shift in its level and the corresponding volatility. The proposed tests are robust to a wide class of partially stationary processes with heavy-tailed errors, and have an exact binomial null distribution. Our tests are not affected by the size or location of the break. We set the structural break under the null and the alternative hypotheses to relieve a possible vagueness in interpreting test results in empirical work. The null hypothesis implies a unit root process with level shifts and the alternative connotes a stationary process with level shifts. The Monte Carlo simulation shows that our tests have stable size than the OLSE based tests.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.843-851
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• 2017

The rise of the Robo-advisor represents one of the most profound shifts in FinTech. It also raises concerns about their financial management. As the most Robo-Advisors utilize ETFs, we seek to determine the appropriate risk management model in estimating 95% Value-at-Risk (VaR) and 99% VaR in this paper. The GARCH and the Markov regime wwitching GARCH are evaluated in terms of the accuracy of probability, the independence of extreme events occurrence and both. The result shows that the Markov regime switching GARCH can be a good ETF risk management tool since it can reflect financial market structural changes into the volatility.

• Resources Recycling
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• v.32 no.6
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• pp.67-78
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• 2023

The rare metals used as raw materials in high-tech industries undergo changes in demand structures and supply chains following domestic industrial structural shifts and technological advancements, exhibiting high price volatility. Therefore, it is necessary to periodically analyze changes in the demand structures of rare metals. Since domestic demand for most rare metals relies on imports in Korea, the changes in domestic demand for rare metals can be identified by analyzing changes in their trade structure. In the present study, we analyze the changes in trade volume, trade growth rate, trade rankings, and trading countries from 2000 to 2022 for 35 rare metals, categorized into five types-ores, metals, alloys, compounds, and scrap. The trade of the raw materials of rare metals in Korea has generally increased since the 2000s, except for a significant decline in 2009 and 2016. The total trade volume, encompassing both exports and imports, has increased by approximately tenfold in 2022 compared to 2001. Until the mid-2010s, the trade of the raw materials of rare metals was primarily focused on those used in steel-manufacturing such as silicon, nickel, chrome, molybdenum, manganese, and others. However, after that period, there has been an increase in the trade of platinum group metals like palladium, rhodium, platinum, and the raw materials of rare metals for secondary battery-manufacturing such as lithium and cobalt. Particularly in 2022, lithium has become the largest share in trade of the raw materials of rare metals in Korea, due to the price surge and increase in demand.