• Strategy21
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• s.46
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• pp.239-276
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• 2020

The failure of early economic sanctions aimed at hurting the overall economies of targeted states called for a more sophisticated design of economic sanctions. This paved way for the advent of 'smart sanctions,' which target the supporters of the regime instead of the public mass. Despite controversies over the effectiveness of economic sanctions as a coercive tool to change the behavior of a targeted state, the transformation from 'comprehensive sanctions' to 'smart sanctions' is gaining the status of a legitimate method to impose punishment on states that do not conform to international norms, the nonproliferation of weapons of mass destruction in this particular context of the paper. The five permanent members of the United Nations Security Council proved that it can come to an accord on imposing economic sanctions over adopting resolutions on waging military war with targeted states. The North Korean nuclear issue has been the biggest security threat to countries in the region, even for China out of fear that further developments of nuclear weapons in North Korea might lead to a 'domino-effect,' leading to nuclear proliferation in the Northeast Asia region. Economic sanctions had been adopted by the UNSC as early as 2006 after the first North Korean nuclear test and has continually strengthened sanctions measures at each stage of North Korean weapons development. While dubious of the effectiveness of early sanctions on North Korea, recent sanctions that limit North Korea's exports of coal and imports of oil seem to have an impact on the regime, inducing Kim Jong-un to commit to peaceful talks since 2018. The purpose of this paper is to add a variable to the factors determining the success of economic sanctions on North Korea: preventing North Korea's evasion efforts by conducting illegal transshipments at sea. I first analyze the cause of recent success in the economic sanctions that led Kim Jong-un to engage in talks and add the maritime element to the argument. There are three conditions for the success of the sanctions regime, and they are: (1) smart sanctions, targeting commodities and support groups (elites) vital to regime survival., (2) China's faithful participation in the sanctions regime, and finally, (3) preventing North Korea's maritime evasion efforts.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.14 no.2
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• pp.47-58
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• 1981

Relationships between the electrical conductivity and the contents of the chloride, sulfate, calcium, magnesium, sodium, potassium and total major inorganic ions, and between each, chemical conservative constituents were calculated with the data which sampled at the lesions of Mulgeum and between Namji and Wondong from March 1974 to April 1980. Semilogarithmic relations were found between the electrical conductivity and the contents of monovalent ions, and logarithmic relations were found between the electrical conductivity and the contents of divalent ions at the both regions. The relational equations between the electrical conductivity $\lambda_{25}$and the contents of the major inorganic ions at Mulgeum are as follows: $log\;Cl(ppm)\;=\;2.37{\cdot}\lambda_{25}(m{\mho}/cm)+0.733{\pm}0.141$, $log\;SO_4(ppm)=1.12{\cdot}log\lambda_{25}(m{\mho}/cm)+2.14{\pm}0.18$, $log\;Ca(ppm)=0.615{\cdot}log\lambda_{25}(m{\mho}/cm)+1.67{\pm}0.12$, $log\;Mg(ppm)=0.756{\cdot}log\lambda_{25}(m{\mho}/cm)+1.27{\pm}0.11$, $log\;Na(ppm)=2.82{\cdot}\lambda_{25}(m{\mho}/cm)+0.551{\pm}0.133$, $log\;K(ppm)=1.33{\cdot}\lambda_{25}(m{\mho}/cm)+0.136{\pm}0.095$, and total inorganic ions $C(ppm)=399{\cdot}\lambda_{25}(m{\mho}/cm)-0.9{\pm}14.6$. The relational equations between the electrical conductivity ($\lambda_{25}$) and the contents of the major inorganic ions at the region between Namji and Wondong a.e as follows: $log\;Cl(ppm)=4.27{\cdot}\lambda_{25}(m{\mho}/cm)+0.380{\pm}0.138$, $log\;SO_4(ppm)=0.915{\cdot}log\lambda_{25}(m{\mho}/cm)+1.95{\pm}0.18$, $log\;Ca(ppm)=0.756{\cdot}log\lambda_{25}(m{\mho}/cm)+1.74{\pm}0.12$, $log\;Mg(ppm)=1.00{\cdot}log\lambda_{25}(m{\mho}/cm)+1.41{\pm}0.10$. $log\;Na(ppm)=2.47{\cdot}\lambda_{25}(m{\mho}/cm)+0.614{\pm}0.065$, $log\;K(ppm)=1.62{\cdot}\lambda_{25}(m{\mho}/cm)+0.030{\pm}0.060$, and total inorganic ions $C(ppm)=323{\cdot}\lambda_{25}(m{\mho}/cm)+11.7{\pm}9.3$. Logarithmic relations were found between each chemical conservative constituents at Mulgeum and the equations are as follows: $log\;Cl(ppm)=0.711{\cdot}log\;SO_4(ppm)+0.488{\pm}0.206$, $log\;Cl(ppm)=0.337{\cdot}log\;Ca(ppm)+0.822{\pm}0.130$, $log\;Cl(ppm)=0.605{\cdot}log\;Mg(ppm)-0.017{\pm}0.154$, $Cl(ppm)=0.676{\cdot}Na(ppm)+2.31{\pm}4.67$, $log\;Cl(ppm)=0.406{\cdot}log\;K(ppm)-0.092{\pm}0.112$, $log\;SO_4(ppm)=0.378{\cdot}log\;Ca(ppm)+0.721{\pm}0.125$, $log\;SO_4(ppm)=0.462{\cdot}log\;Mg(ppm)+0.107{\pm}0.118$, $log\;SO_4(ppm)=0.592{\cdot}log\;Na(ppm)+0.313{\pm}0.191$, $log\;SO_4(ppm)=0.308{\cdot}log\;K(ppm)-0.019{\pm}0.120$, $Ca(ppm)=0.262{\cdot}Mg(ppm)+0.74{\pm}1.71$. $log\;Ca(ppm)=1.10{\cdot}log\;Na(ppm)-0.243{\pm}0.239$, $Ca(ppm)=0.0737{\cdot}K(ppm)+1.26{\pm}0.73$, $log\;Mg(ppm)=0.0950{\cdot}Na(ppm)+0.587{\pm}0.159$, $log\;Mg(ppm)=0.0518{\cdot}K(ppm)+0.111{\pm}0.102$, and $Na(ppm)=0.0771{\cdot}K(ppm)+1.49{\pm}0.59$. Logarithmic relations were found between each chemical conservative constituents except a relationship between the chloride and calcium contents at the region between Namji and Wondong, and the equations are as follows : $log\;Cl(ppm)=0.312{\cdot}log\;SO_4(ppm)+0.907{\pm}0.210$, $log\;Cl(ppm)=0.458{\cdot}log\;Mg(ppm)+0.135{\pm}0.130$, $Cl(ppm)=0.484{\cdot}logNa(ppm)+0.507{\pm}0.081$, $Cl(ppm)=0.0476{\cdot}K(ppm)+1.41{\pm}0.34$, $log\;SO_4(ppm)=0.886{\cdot}log\;Ca(ppm)+0.046{\pm}0.050$, $log\;SO_4(ppm)=0.422{\cdot}log\;Mg(ppm)+0.139{\pm}0.161$, $log\;SO_4(ppm)=0.374{\cdot}log\;Na(ppm)+0.603{\pm}0.140$, $log\;SO_4(ppm)=0.245{\cdot}log\;K(ppm)+0.023{\pm}0.102$, $log\;Ca(ppm)=0.587{\cdot}log\;Mg(ppm)+0.003{\pm}0.088$, $log\;Ca(ppm)=0.892{\cdot}log\;Na(ppm)+0.028{\pm}0.109$, $log\;Ca(ppm)=0.294{\cdot}log\;K(ppm)-0.001{\pm}0.085$, $log\;Mg(ppm)=0.600{\cdot}log\;Na(ppm)+0.674{\pm}0.120$, $log\;Mg(ppm)=0.440{\cdot}log\;K(ppm)+0.038{\pm}0.081$, and $log\;Na(ppm)=0.522{\cdot}log\;K(ppm)-0.260{\pm}0.072$.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.