• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.181-192
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• 2016

We consider several methods to approximate option prices with correction terms to the Black-Scholes option price. These methods are able to compute option prices from various risk-neutral distributions using relatively small data and simple computation. In this paper, we compare the performance of Edgeworth expansion, A-type and C-type Gram-Charlier expansions, a method of using Normal inverse gaussian distribution, and an asymptotic method of using nonlinear regression through simulation experiments and real KOSPI200 option data. We assume the variance gamma model in the simulation experiment, which has a closed-form solution for the option price among the pure jump $L{\acute{e}}vy$ processes. As a result, we found that methods to approximate an option price directly from the approximate price formula are better than methods to approximate option prices through the approximate risk-neutral density function. The method to approximate option prices by nonlinear regression showed relatively better performance among those compared.

• Journal of Information Technology Applications and Management
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• v.11 no.3
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• pp.23-33
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• 2004

Internet bid systems have been widely used recently. In those systems, the bid price is provided by the seller. When the bid price is set too high compared with the normal price, the successful bid rate can be decreased. Otherwise, when it is set too low based on inaccurate information, it can result in a successful bid with no profit at all. To resolve this problem, we propose an agent that automatically generates bid prices for sellers based on various costing methods such as the high-low point method, the scatter diagram method, and the learning curve method. Through performance experiments, we have found that the number of successful bids with appropriate profit can be increased using the bid pricing agent. Among the costing methods, the learning curve method has shown the best performance. Also, we discuss about how to design and implement the bid pricing agent.

• Transactions of the Korean hydrogen and new energy society
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• v.30 no.5
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• pp.473-477
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• 2019

In this paper, cold heat price contained in the 1 ton/h of LNG has been evaluated using PRO/II with PROVISION release 10.2 from Aveva company when LNG is used to liquefy several refrigerants instead of using vapor recompression refrigeration cycle. Normal butane, R134a, NH3, R22, propane and propylene refrigerants were selected for the modeling of refrigeration cycle. It was concluded that LNG cold heat price was inversely proportional to the refrigerant supply temperature, even though LNG supply flow rate is not varied according to the refrigerant supply temperature.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• Journal of the Korean Society of Costume
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• v.55 no.5 s.95
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• pp.43-54
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• 2005

This study was designed towards female college students to find out how increasing promotional marketing means are affecting the evaluation of clothing products; qualify perception, value perception, and purchase intention. 704 female college students participated in this study and SPSS package was used to analyze gathered data. The results of this study were as follows: First, the use of sales promotional means and preference had a significant difference among students demographic factors(residence, whole Income of the family, allowance, and clothing expenses). Second, qualify perception, value perception, and purchasing intention were the three factors of clothing product evaluation. Third, normal price and $30\%$ sale price clothing was perceived as high quality product and $50\%$ sale price clothing was perceived as high valued product. Purchasing intention was high when low price was suggested or promotional gift was given. Fourth, when considering product price as the factor of product evaluation, there were significant difference between the prices of product. And also, considering the product price, there were significant difference among factors of product evaluation and sales promotional means. Fifth, there was significant correlation between qualify perception, value perception, purchasing intention, usage and preference of promotional means. Further more, value perception was main factor that affected purchasing intention.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.277-283
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• 2005

In general, stock prices do not follow normal distributions and mean trend indexes, volatility indexes, and volume indicators relating to these non-normal stock price are widely used as buy-sell strategies. These general buy-sell strategies are rather intuitive than statistical reasoning. The non-normality problem can be solved by normalizing process and statistical buy-sell strategy can be obtained by using mean trend and volatility indexes together with normalized stock prices. In this paper, buy-sell strategy based on mean trend and volatility index with normalized stock prices are proposed and applied to KOSPI200 data to see the feasibility of the proposed buy-sell strategy.

• Management Science and Financial Engineering
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• v.18 no.2
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• pp.1-4
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• 2012

We study optimal trading strategy of a market maker with stock inventory. Following Avellaneda and Stoikov (2008), we assume the stock price follows a normal distribution. However, we take a constant expected rate of the stock return and assume that the stock volatility is an inverse function of the stock price level. We show that the optimal bid-ask spread of the market maker is wider for a higher expected rate of stock returns.

• International Area Studies Review
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• v.13 no.2
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• pp.66-88
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• 2009

This paper studies optimal discretionary monetary policy in the presence of uncertainty in the housing sector. In particular, we allow two possible regimes regarding the evolution of housing price inflation and the effects of housing price inflation on the aggregate demand. Estimation results with the US data confirm the presence of two distinctive regimes, one 'normal' and the other more akin to the housing price 'bubble' state. The optimal policy is 'asymmetric' in that the optimal responses in the 'normal' regime require the central bank to lean against the wind to inflationary pressure from CPI and housing inflation, while the central bank is recommended to accommodate it in the other regime.

• Korean Journal of Veterinary Service
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• v.46 no.3
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• pp.243-247
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• 2023

This study examined the dermatophytes of calves aged between 6 and 12 months in the Hanwoo calf auction market. Moreover, the research analyzed how dermatophytosis affected the auction price of Hanwoo calves based on their sex and age. The incidence rate of dermatophytosis was found to be 85 cases out of 1,955 calves (4.3%). The major location of dermatophytosis lesions were in the head region. Specifically, the highest prevalence was observed in the forehead (42.4%), followed by the eyes (30.1%), and the ears (18.8%). The auction prices of Hanwoo calves were observed that the average price for normal calves was 2,936,428 won, while calves with dermatophytosis were sold at 2,767,059 won. Comparing auction prices according to gender and age, it was observed that male calves and calves aged between 8 and 12 months had significantly lower auction prices compared to normal calves. The results provided valuable insights into the current situation of dermatophytosis in Hanwoo calves. Moreover, analyzing the impact of dermatophytosis on the auction prices of these calves, it has generated essential data that can serve as a foundation for implementing and enhancing ongoing management and prevention measures for dermatophytosis in cattle.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.243-257
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• 2017

In this paper, we compare several methods to approximate option prices: Edgeworth expansion, A-type and C-type Gram-Charlier expansions, a method using normal inverse gaussian (NIG) distribution, and an asymptotic method using nonlinear regression. We used two different types of approximation. The first (called the RNM method) approximates the risk neutral probability density function of the log return of the underlying asset and computes the option price. The second (called the OPTIM method) finds the approximate option pricing formula and then estimates parameters to compute the option price. For simulation experiments, we generated underlying asset data from the Heston model and NIG model, a well-known stochastic volatility model and a well-known Levy model, respectively. We also applied the above approximating methods to the KOSPI200 call option price as a real data application. We then found that the OPTIM method shows better performance on average than the RNM method. Among the OPTIM, A-type Gram-Charlier expansion and the asymptotic method that uses nonlinear regression showed relatively better performance; in addition, among RNM, the method of using NIG distribution was relatively better than others.