• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.369-378
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• 2013

The Korean Won-Dollar exchange markets showed radical price movements in the late 1990s and 2008. Therefore it provides good sources for studying volatility phenomena. Using the GARCH option models, I analysed how the prices of foreign exchange options react volatilities in the foreign exchange spot prices. For this I compared the explanatory power of three option models(Black and Scholes, Duan, Heston and Nandi), using the Won-Dollar OTC option markets data from 2006 to 2013. I estimated the parameters using MLE and calculated the mean square pricing errors. According to the my empirical studies, the pricing errors of Duan, Black and Scholes models are 0.1%. And the pricing errors of the Heston and Nandi model is greatest among the three models. So I would like to recommend using Duan or Black and Scholes model for hedging the foreign exchange risks. Finally, the historical average of spot volatilities is about 14%, so trading the options around 5% may lead to serious losses to sellers.

• The Journal of the Korea Contents Association
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• v.13 no.6
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• pp.469-480
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• 2013

This study suggests 12 products regarding the pending question of tourism package price mechanism which is linked directly and indirectly to Jeju Tourism, and shows the following conclusions through inspection in mental accounting principles and framing effect based on prospect theory. First, when presenting the price list of the tourism package, it is needed to present in price bundling. Second, it is proven that it is desirable that information about discount prices open the individual discount information of the basic package and option package to public. Third, it is discovered that experienced tourists in purchasing tourism products are more sensitive to price information (price discount) than inexperienced tourists, so that framing effect conform to Knowledge-assembly theory. The current questions of this study are that 'no discount' information should be presented in bundling, that the method of framing is important in presenting discount product information. It is required not only to grasp the viewpoint of modern people in purchasing tourism products, but also to present ready-to-serve products which can save time, effort, cost to give stability in mental accounting principles.

• Korean Journal of Construction Engineering and Management
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• v.12 no.2
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• pp.121-132
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• 2011

One of the advantages of BOT PF(Project Finance) is the government can be protected from risks involved in projects as the private finances, builds, and operates relevant projects. Moreover, the private may avoid outstanding responsibility in case of default thanks to BOT PF's unique financial structure and characteristics. However, despite increasing attention on risk mitigation effect of financial structure and characteristic of BOT PF to default risk with emerging controversies of capital crunch, introduction of IFRS, and contingent liabilities, valuation of default risk mitigation effect caused by financial structure and characteristics of BOT PF still seems sophisticated due to uncertain cash flows, complexly layered contracts, and their interaction. So, this paper is to show the theoretical frame to assess the default risk mitigation effect of financial structure and characteristic of BOT PF with option pricing and related financial economic theories and to provide some meaningful implications. Finally, this research shows that the financial structure and characteristics of BOT PF help mitigate the default risk and default risk mitigation effect increases as change of relevant variables on financial feasibility gets the BOT project less financially feasible.

• The Korean Journal of Financial Management
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• v.23 no.2
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• pp.209-237
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• 2006

Since previous default forecasting models for the firms evaluate the probability of default based upon the accounting data from book values, they cannot reflect the changes in markets sensitively and they seem to lack theoretical background. The market-information based models, however, not only make use of market data for the default prediction, but also have strong theoretical background like Black-Scholes (1973) option theory. So, many firms recently use such market based model as KMV to forecast their default probabilities and to manage their credit risks. Korean firms also widely use the KMV model in which default point is defined by liquid debt plus 50% of fixed debt. Since the debt structures between Korean and American firms are significantly different, Korean firms should carefully use KMV model. In this study, we empirically investigate the importance of debt structure. In particular, we find the following facts: First, in Korea, fixed debts are more important than liquid debts in accurate prediction of default. Second, the percentage of fixed debt must be less than 20% when default point is calculated for Korean firms, which is different from the KMV. These facts give Korean firms some valuable implication about default forecasting and management of credit risk.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1118-1125
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• 2014

The past IMF foreign exchange crisis and subprime financial crisis had a big influence on variability of macroeconomics, even if the origin of its occurrence might be different. This not only had a significant infrequence on the overall industries, but also produced many insolvent companies by being closely linked with a management environment of an individual construction company leading the construction industry. The purpose of this research is to investigate characteristics of management condition of construction company according to the size of construction company using KMV model developed on the basis of the Black & Scholes option pricing theory. This research has set 28 construction companies listed to KOSPI/KOSDAQ for applying the KMV model and measuring the level of the default risk of construction companies. The data was retrieved from TS2000 established by Korea Listed Companies Association (KLCA), Statistics Korea. The analysis period is between first quarter of 2004 and fourth quarter of 2010. This research examine characteristics of the level and fluctuation process of the management condition of construction company according to the size of construction company.

• 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.