• 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.

• Bulletin of the Korean Mathematical Society
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• v.51 no.1
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• pp.221-227
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• 2014

Our goal is to relax a sufficient condition for the exponential almost sure stability of a certain class of stochastic differential equations. Compared to the existing theory, we prove the almost sure stability, replacing Lipschitz continuity and linear growth conditions by the existence of a strong solution of the underlying stochastic differential equation. This result is extendable for the regime-switching system. An explicit example is provided for the illustration purpose.

• Journal of applied mathematics & informatics
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• v.38 no.5_6
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• pp.439-461
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• 2020

A step-up option is a newly developed financial instrument that simultaneously provides higher security and profitability. This paper introduces two step-up options: step-up type1 and step-up type2 options, and derives the option pricing formulas using the Laplace transform. We assume that the underlying equity price follows a regime-switching model that reflects the long-term maturity of these options. The option prices are calculated for the two types of funds, a pure stock fund composed of risky assets only and a mixed fund composed of stocks and bonds, to reflect possible variety in the fund underlying asset mix. The impact of changes in the model parameters on the option prices is analyzed. This paper provides information crucial to product developments.

• Management & Information Systems Review
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• v.34 no.3
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• pp.17-39
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• 2015

This study examined the structural changes and volatility in the global stock markets using a Markov Regime Switching ARCH model developed by the Hamilton and Susmel (1994). Firstly, the US, Italy and Ireland showed that variance in the high volatility regime was more than five times that in the low volatility, while Korea, Russia, India, and Greece exhibited that variance in the high volatility regime was increased more than eight times that in the low. On average, a jump from regime 1 to regime 2 implied roughly three times increased in risk, while the risk during regime 3 was up to almost thirteen times than during regime 1 over the study period. And Korea, the US, India, Italy showed ARCH(1) and ARCH(2) effects, leverage and asymmetric effects. Secondly, 278 days were estimated in the persistence of low volatility regime, indicating that the mean transition probability between volatilities exhibited the highest long-term persistence in Korea. Thirdly, the coefficients appeared to be unstable structural changes and volatility for the stock markets in Chow tests during the Asian, Global and European financial crisis. In addition, 1-Step prediction error tests showed that stock markets were unstable during the Asian crisis of 1997-1998 except for Russia, and the Global crisis of 2007-2008 except for Korea and the European crisis of 2010-2011 except for Korea, the US, Russia and India. N-Step tests exhibited that most of stock markets were unstable during the Asian and Global crisis. There was little change in the Asian crisis in CUSUM tests, while stock markets were stable until the late 2000s except for some countries. Also there were stable and unstable stock markets mixed across countries in CUSUMSQ test during the crises. Fourthly, I confirmed a close relevance of the volatility between Korea and other countries in the stock markets through the likelihood ratio tests. Accordingly, I have identified the episode or events that generated the high volatility in the stock markets for the financial crisis, and for all seven stock markets the significant switch between the volatility regimes implied a considerable change in the market risk. It appeared that the high stock market volatility was related with business recession at the beginning in 1990s. By closely examining the history of political and economical events in the global countries, I found that the results of Lamoureux and Lastrapes (1990) were consistent with those of this paper, indicating there were the structural changes and volatility during the crises and specificly every high volatility regime in SWARCH-L(3,2) student t-model was accompanied by some important policy changes or financial crises in countries or other critical events in the international economy. The sophisticated nonlinear models are needed to further analysis.

• Proceedings of the Korean Magnestics Society Conference
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• 2007.05a
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• pp.12.2-12.2
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• 2007

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.824-825
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• 2005

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.15-22
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• 2021

This study aims to obtain a stock investment strategy model based on the industrial sector in Indonesia Stock Exchange (IDX). This study uses IDX data for the period of January 1996 to December 2016. This study uses the Markov Regime Switching Model to identify trends in market conditions that occur in industrial sectors on IDX. Furthermore, by using the Logit Regression Model, we can see the influence of economic factors in determining trends in market conditions sectorally and the probability of trends in market conditions. This probability can be the basis for determining stock investment decisions in certain sectors. The results showed descriptively that the stocks of the consumer goods industry sector had the highest average return and the lowest standard deviation. The trend in sectoral stock market conditions that occur in IDX can be divided into two conditions, namely bullish condition (high returns and low volatility) and bearish condition (low returns and high volatility). Differences in the conditions are mainly due to differences in volatility. The use of a Logit Regression Model to produce probability of market conditions and to estimate the influence of economic factors in determining stock market conditions produces models that have varying predictive abilities.

• The Korean Journal of Financial Management
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• v.21 no.2
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• pp.183-209
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• 2004

This paper uses both the bivariate GARCH type BEKK error correction model and Bivariate-AR(1)-Markov-Switching-VECM model to estimate the volatility, time-varying correlation and hedge ratio for the KTB spot and futures indexes, sampled daily over 1/4/2000-10/30/2003. This study suggests that the volatility regime has more significant influence on KTB markets than incline/decline regime does. The results support the importance of the bivariate model in stead of univariate model between KTB spot and futures markets, which may consider not only individual variance process but also covariance process at the same time.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.49 no.2
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• pp.113-115
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• 2000

The gradual hole injection LIGBT (GI-LIGBT) which employs the dual gate and the p+ injector, was fabricated for eliminating a negative resistance regime and reducing a forward voltage drop in SA-LIGBT. The elimination of the negative resistance regime is successfully achieved by initiating the hole injection gradually. Furthermore, the experimental results show that the forward voltage drop of GI-LIGBT decreases by lV at the current density of 200 $A/cm^2$, when compared with that of the conventional SA-LIGBT. It is also found that the improvement in the on-state characteristics can be obtained without sacrificing the inherent fast switching characteristics of SA-LIGBT.

• The Pure and Applied Mathematics
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• v.18 no.2
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• pp.129-139
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• 2011

We consider an optimal trading rule in this paper. We assume that the underlying asset follows a mean-reverting process and the transaction consists of one buying and one selling. To maximize the profit, we find price levels to buy low and to sell high. Associated HJB equations are used to formulate the value function. A verification theorem is provided for sufficient conditions. We conclude the paper with a numerical example.