• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.745-749
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• 2017

Climate change and energy security are major factors for future national energy policy. To resolve these issues, many countries are focusing on creating new growth industries and energy services such as smartgrid, renewable energy, microgrid, energy management system, and peer to peer energy trading. The financial and economic evaluation of new energy services basically requires energy savings estimation technologies. This paper presents the baseline load estimation method, which is used to calculate energy savings resulted from participating in the new energy program, using moving average model with heating degree days (HDD) and cooling degree days (CDD) adjustment. To demonstrate the improvement of baseline load estimation accuracy, the proposed method is tested. The results of case studies are presented to show the effectiveness of the proposed baseline load estimation method.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.697-707
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• 2017

In this paper, we propose a modified GARCH(p, q)-X model which is obtained by adding the exogenous variables to the modified GARCH(p, q) process. Some limiting properties are shown under various stationary and nonstationary exogenous processes which are generated by another process independent of the noise process. The proposed model extends the GARCH(1, 1)-X model studied by Han (2015) to various GARCH(p, q)-type models such as GJR GARCH, asymptotic power GARCH and VGARCH combined with exogenous process. In comparison with GARCH(1, 1)-X, we expect that many stylized facts including long memory property of the financial time series can be explained effectively by modified GARCH(p, q) model combined with proper additional covariate.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.741-744
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• 1996

Artifical Neural Network(ANN) models were used for forecasting interest rate as a new methodology, which has proven itself successful in financial domain. This research intended to construct ANN models which can maximize the performance of prediction, regarding Corporate Bond Yield (CBY) as interest rate. Synergistic Market Analysis (SMA) was applied to the construction of models [Freedman et al.]. In this aspect, while the models which consist of only time series data for corporate bond yield were devloped, the other models generated through conjunction and reorganization of fundamental variables and market variables were developed. Every model was constructed to predict 1,6, and 12 months after and we obtained 9 ANN models for interest rate forecasting. Multi-layer perceptron networks using backpropagation algorithm showed good performance in the prediction for 1 and 6 months after.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.561-569
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• 2008

In this paper, we propose the double robust estimators which are the solutions of the double robust estimating equations to analyze and treat the outliers in the stock market data in Korea including the IMF period. The feasibility study shows that the proposed estimators work quitely better than the least squares estimators and the conventional robust estimators.

• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.12
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• pp.1285-1296
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• 2012

This study investigates the effects of changes in price, total consumption expenditures and economic sitations on Korean household demands for clothing and footwear using time-series data. The clothing and footwear category was reclassified as clothing, footwear and clothing services items for the demand analysis. This study utilized the Linearized Almost Ideal Demand System (LAIDS) model to analyze household demand. The results indicate that price and total consumption expenditures are significantly related to Korean household consumption expenditure allocations for clothing and footwear items. The effects of the IMF bailout crisis in 1997 and the global financial crisis in 2008 on household expenditure shares for clothing and footwear items were very weak and statistically insignificant. All the demand elasticities were estimated with respect to total consumption expenditures and prices. Clothing was expenditure elastic (greater than one) and other items were classified as inelastic. All the own price elasticities of demands were negative (other than clothing). Through the estimations of cross price elasticity the relationships between the demands for items and other item prices were evaluated (i.e., substitutes and complements).

• Management & Information Systems Review
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• v.4
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• pp.327-348
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• 2000

There are three major motives for M&A, financial synergy effect, operating & managerial synergy effect, and tax effect. The purpose of this study is to prove the operating & managerial synergy effect of M&A. To do this, we analyze the market-ripple effect of M&A, focusing on the increase in market power. Specifically we use cross-sectional data from 1985 to 1998 to show whether a market power of mergers is higher than that of a matched non-merging control group. we use time series data to show whether a market power of merger is higher than that of pre-merger. Also we use the event study using market model to show the stock price movement after mergers. The result is that although revenue increase after mergers, profit of the firms does not improve after mergers. Also there is sufficient evidence to say that there is a cumulative abnormal return for the firms after mergers.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.713-722
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• 2020

Contrasted with the standard symmetric GARCH models, we consider a broad class of threshold-asymmetric models to analyse financial time series exhibiting asymmetric volatility. By further introducing power transformations, we add more flexibilities to the asymmetric class, thereby leading to power transformed and asymmetric volatility models. In particular, the paper is concerned with the nonstationary volatilities in which conditions for integrated volatility and explosive volatility are separately discussed. Dow Jones Industrial Average is analysed for illustration.

• Asia-Pacific Journal of Business
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• v.11 no.4
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• pp.255-269
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• 2020

Purpose - This paper analyzes the market efficiency focusing on the long memory properties of the domestic futures market. By decomposing futures prices into yield and volatility and looking at the long memory properties of the time series, this study aims to understand the futures market pricing and change behavior and risks, specifically and in detail. Design/methodology/approach - This study analyzes KOSPI 200 futures, KOSDAQ 150 futures, 3 and 10-year government bond futures, US dollar futures, yen futures, and euro futures, which are among the most actively traded on the Korea Exchange. To analyze the long memory and market efficiency, we used the Variance Ratio, Rescaled-Range(R/S), Geweke and Porter-Hudak(GPH) tests as semi- parametric methods, and ARFIMA-FIGARCH model as the parametric method. Findings - It was found that all seven futures supported the efficiency market hypothesis because the property of long memory turned out not to exist in their yield curves. On the other hand, in futures volatility, all 7 futures showed long memory properties in the analysis, which means that if new information is generated in the domestic futures market and the market volatility once expanded due to the impact, it does not decrease or shrink for a long period of time, but continues to affect the volatility. Research implications or Originality - The results of this paper suggest that it can be useful information for predicting changes and risks of volatility in the domestic futures market. In particular, it was found that the long memory properties would be further strengthened in the currency futures and bond rate futures markets after the global financial crisis if the regime changes of the domestic financial market are taken into account in the analysis.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.39-44
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• 2018

The application of deep neural networks to finance has received a great deal of attention from researchers because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from large sets of data, which is required to describe nonlinear input-output relations of financial time series. The paper presents a new deep neural network model where single layered autoencoder and 4 layered neural network are serially coupled for stock price forecasting. The autoencoder extracts deep features, which are fed into multi-layer neural networks to predict the next day's stock closing prices. The proposed deep neural network is progressively learned layer by layer ahead of the final learning of the total network. The proposed model to predict daily close prices of KOrea composite Stock Price Index (KOSPI) is built, and its performance is demonstrated.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.61-73
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• 2016

We consider the wild bootstrap Ljung-Box (LB) test for autocorrelation in residuals of fitted multivariate time series models. The asymptotic chi-square distribution under the IID assumption is traditionally used for the LB test; however, size distortion tends to occur in the usage of the LB test, due to the conditional heteroskedasticity of financial time series. In order to overcome such defects, we propose the wild bootstrap LB test for autocorrelation in residuals of fitted vector autoregressive and error correction models. The simulation study and real data analysis are conducted for finite sample performance.