• KDI Journal of Economic Policy
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• v.41 no.4
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• pp.67-99
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• 2019

This paper, focusing on the renewable portfolio standard (RPS), evaluates alternative renewable energy policies. We propose a tractable equilibrium model which provides a structural representation of Korea's electricity market, including its energy settlement system and renewable energy certificate (REC) transactions. Arbitrage conditions are used to define the core value of REC prices to identify relevant competitive equilibrium conditions. The model considers R&D investments and learning effects that may affect the development of renewable energy technologies. The model is parameterized to represent the baseline scenario under the currently scheduled RPS reinforcement for a 20% share of renewable generation, and then simulated for alternative scenarios. The result shows that the reinforcement of the RPS leads to higher welfare compared to weakening it as well as repealing it, though there remains room to enhance welfare. It turns out that subsidies are welfare-inferior to the RPS due to financial burdens and that reducing nuclear power generation from the baseline yields lower welfare by worsening environmental externalities.

• Environmental and Resource Economics Review
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• v.20 no.2
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• pp.335-356
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• 2011

In this paper, we attempted to suggest a way to evaluate appropriateness and efficiency for the energy consumption structure. For this, based on Markowitz (1952)' mean-variance portfolio model, we constructed an optimal fossil fuel mix. In constructing the optimal mix, we first defined returns on fossil fuels (oil, coal and natural gas) as TOE (Ton of Oil Equivalent) per $1. Then, by using the dynamic latent common factor model, we decomposed the growth rates of the returns on each fossil fuel into two parts : the common part and the idiosyncratic part. Finally, based on the results from the dynamic latent common factor model, we constructed the optimal fossil fuel mix implied by the mean-variance portfolio model. Our results indicate that for the fossil fuel mix to be on the efficient frontier, it is crucial to reduce oil consumption as low as possible. Moreover, our results imply that it is more efficient to increase natural gas consumption rather than coal consumption in reducing oil consumption. These results are in line with the strategies for the future energy consumption structure pursued by Korea and indicate that reduction in oil use can improve overall efficiency in energy consumption.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.135-150
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• 2012

This paper presents a portfolio model for a long-term power generation mix problem. The proposed portfolio model evaluates generation mix by considering the tradeoffs between the expected cost for power generation and its variability. Unlike conventional portfolio models measuring variance, we introduce Conditional Value-at-Risk (CVaR) in designing the variability with aims to considering events that are enormously expensive but are rare such as nuclear power plant accidents. Further, we consider uncertainties associated with future electricity demand, fuel prices and their correlations, and capital costs for power plant investments. To obtain an objective generation by each energy source, we employ the sample average approximation method that approximates the stochastic objective function by taking the average of large sample values so that provides asymptotic convergence of optimal solutions. In addition, the method includes Monte Carlo simulation techniques in generating random samples from multivariate distributions. Applications of the proposed model and method are demonstrated through a case study of an electricity industry with nuclear, coal, oil (OCGT), and LNG (CCGT) in South Korea.

• KIEE International Transactions on Power Engineering
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• v.5A no.1
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• pp.70-78
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• 2005

This paper analyzes the environmental impact and economic effect of introducing the Renewable Portfolio Standard (RPS) into the Korean electricity market using the DECADES (Database and Methodologies for Comparative Assessment of Different Energy Sources for Electricity Generation) model, a comparative assessment tool developed by IAEA. A bottom up approach is adopted for the evaluation of air pollutant emission and its impact of several RPS scenarios. The environmental damage costs of RPS scenarios are evaluated based on the Extern-E results and the Thailand externality study carried out by EGAT. The results of this study can be applied in determining or analyzing the national electricity policy and energy policy.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.5
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• pp.275-284
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• 2004

This paper analyzes the environmental impact and economic effect of introducing the Renewable Portfolio Standard (RPS) into Korean electricity market using the DECADES (Database and Methodologies for Comparative Assessment of Different Energy Source for Electricity Generation) model, a comparative assessment tool developed by IAEA. A bottom up approach is adopted for the evaluation of air pollutant emission and its impact of several RPS scenarios. The environmental damage costs of RPS scenarios are evaluated based on the Extern-E results and Thailand externality study carried by EGA T. The results of this study can be applied in determining or analyzing the national electricity policy and energy policy.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.134.1-134.1
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• 2011

As eco-friendly energy is in the spotlight, lots of countries are out to develop and disseminate renewable energy. However, Korea still remains in relative newcomer position for renewable energy. To overcome the present condition, it is necessary to carry out a study of establishment of renewable energy policy that is appropriate the present situation of Korea from examples of advanced countries. Among several renewable energies, wind energy is known to be the most reliable energy. Germany that has increased the share of wind energy in total energy supply is a representative leading country for wind energy. Therefore wind energy policy in Germany will be a role model for that in Korea. In this study, as a research of portfolio for wind energy policy of advanced countries, a wide survey for current status of wind energy and energy policy in Germany is carried out.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.1
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• pp.85-92
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• 2013

Many efforts will have to be made on securing the stable supply of the energy due to the worldwide trend of controlling the utilization fossil fuels inducing global climate change. Renewable portfolio standard enforced to power companies over 500 MW capacity from 2012. Tidal current energy is one of the most interesting renewable and clean energy resources that have been less exploited. Especially, Korea has worldwide outstanding tidal current energy resources and it is highly required to develop a tidal current energy conversion system(TECS) in coastal region. So, we examine a tidal in-stream energy using a numerical model and estimate a tidal current potential for commercialization of tidal current power plant in the sea of the Bunamgun-do. Available tidal energy resources is also analytically estimated using a tidal farm method and the annual energy production of an optimal TECS arrays will be calculated with taking into account interference of lateral and longitudinal spacing.

• Korean Management Science Review
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• v.30 no.1
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• pp.139-148
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• 2013

A nuclear energy has been one of the most important sources to securely supply electricity in South Korea. Its weight in the national electricity supply has kept increasing since the first nuclear reactor was built in 1978. The country relies on the nuclear approximately 31.4% in 2012 and it is expected to increase to 48.5% in 2024 based on the long-term electricity supply plan announced by the Korean government. However, Fukushima disaster due to 9.0 magnitude earthquake followed by the tsunami has raised deep concerns on the security of the nuclear power plants. The policy makers of the country are much interested in analyzing the cost structure of the power supply in the case that the nuclear is diminished from the current supply portion. This research uses a stochastic model that aims to evaluate the long-term power supply plan and provides an extensive cost analysis on the changes of the nuclear power supply. To evaluate a power supply plan, the research develops a few plausible energy mix scenarios by changing the installed capacities of energy sources from the long-term electricity supply plan. The analyses show that the nuclear is still the most attractive energy source since its fuel cost is very much stable compared to the other sources. Also the results demonstrate that a large amount of financial expenditure is additionally required every year if Koreans agree on the reduction of nuclear to increase national security against a nuclear disaster.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.