• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.95-100
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• 2020

Nifty Bank Index has started trading in futures and options (F&O) segment from 13^th June 2005 in National Stock Exchange. The purpose of the study is to enhance the literature by examining expiration effect on the price volatility and price reversal of Underlying Index in India. Historical data used for the current study primarily comprise of daily close prices of Nifty Bank which is the only equity sectoral index in India which is traded in derivatives market and its Future contract value is derived from the underlying CNX Bank Index during the period 1^st January 2010 till 31^st March 2020. To check stationarity of the data, Augmented Dicky Fuller test was used. The study employed ARMA- EGARCH model for analysing the data. The empirical results revealed that there is no effect on the mean returns of underlying Index and EGARCH (1,1) model furthermore shows there is existence of leverage effect in the Bank Index i.e., negative shocks causes more fluctuations in the Index than positive news of similar magnitude. The outcome of the study specifies that there is no effect on volatility on the underlying sectoral index due to expiration days and also observed no price reversal effect once the expiration days are over.

• Journal of Digital Convergence
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• v.14 no.6
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• pp.157-165
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• 2016

The purpose of this study was to analyze the effect of the shipping industry real economy index on the stock prices of domestic shipping companies. The parameters used in this analysis were the stock price of H Company in South Korea and shipping industry real economy indices including BDI, CCFI and HRCI. The period analysis was from 2012 to 2015. The weekly data for four years of the stock price index of shipping companies, BDI, CCFI, and HRCI were used. The effects of CCFI and HRCI on the stock price index of domestic shipping companies were analyzed using the VAR model, and the effects of BDI on the stock price index of domestic shipping companies were analyzed using the VECM model. The VAR model analysis results showed that CCFI and HRCI had negative effects on the stock price index, and the VECM model analysis results showed that BDI also had a negative effect on the stock price index.

• Korean Journal of Construction Engineering and Management
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• v.6 no.1 s.23
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• pp.162-168
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• 2005

Index adjusted ratio method has been widely utilized in public construction sector for contract sum adjustment by price fluctuation. In this method, the Production Price Index are used for the base ratio, but the PPI can't reflect the character of construction project in respect of the selected items and weight. In this research we prove the problem of using the index adjusted ration method in contact sum adjustment by price fluctuation, and improve it by using the construction cost index, which has the property of construction project. And we figure out the efficiency of the proposed method by case study.

• Journal of Distribution Science
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• v.16 no.12
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• pp.5-11
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• 2018

Purpose - We investigate the relationship between economic policy uncertainty (EPU) of the US and China and housing distribution economy in Korea using EPU indexes of two countries and the economic indicators in Korea. Research design, data, and methodology - We use the data such as the Korean housing price stability index (HPSI), housing purchase price index (HPPI), housing lease price index (HLPI), banking stock index (BSI), and consumer price index (CPI) with EPU indexes from January 1999 to December 2017. As an empirical methodology, we select the vector error correction model (VECM) due to the existence of cointegration. Result - As results of the impulse response function, the impact of the US EPU index has initially a negative response on the Korean HPSI, HPPI, and HLPI referring the housing distribution market including the economic variables, BSI, and CPI. Likewise, the impact of index in China has initially a negative response on economic indicators except the BSI in Korea. Conclusions - This study shows that the EPU index of the US has significantly negative relationships on all economic indicators in Korea. In this study, we reveal EPU of the US and China has dynamic impact on housing distribution economy returns in Korea.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.202-211
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• 2014

Pairs trading is a type of arbitrage investment strategy that buys an underpriced security and simultaneously sells an overpriced security. Since the 1980s, investors have recognized pairs trading as a promising arbitrage strategy that pursues absolute returns rather than relative profits. Thus, individual and institutional traders, as well as hedge fund traders in the financial markets, have an interest in developing a pairs trading strategy. This study proposes pairs trading rules (PTRs) created from a price ratio between securities (i.e., stock index futures) using rough set analysis. The price ratio involves calculating the closing price of one security and dividing it by the closing price of another security and generating Buy or Sell signals according to whether the ratio is increasing or decreasing. In this empirical study, we generate PTRs through rough set analysis applied to various technical indicators derived from the price ratio between KOSPI 200 and S&P 500 index futures. The proposed trading rules for pairs trading indicate high profits in the futures market.

• Korean Journal of Construction Engineering and Management
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• v.4 no.4 s.16
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• pp.220-226
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• 2003

The Residential Building Cost Index which presents price variation of construction resources required in a residential building project in single value, has been compiled to evaluate appropriate level of the price variation. This research reviewed the compilation methodology for Residential Building Cost Index based on comparative analysis on domestic statistics and proposed the Residential Building Cost Index through a variety of case studies. It would facilitate monitoring the price variation of a residential building cost and contributes to enhancing the applicability of construction cost data.

• The Journal of Information Technology and Database
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• v.6 no.2
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• pp.115-130
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• 1999

The price structure of housing market varies depending upon market price policy rather than low or high price policy because of IMF. The object of this study is to develop an analysis system for analyzing housing market and its demand. The analysis system consists of four major categories: macro index analysis, market decision analysis, housing market analysis, and consumer analysis. We model each category by using a variety of techniques such as generalized linear model, categorical analysis, bubble analysis, drill-down analysis, price sensitivity meter analysis, optimum price index analysis, profit index measurement analysis, correspondence analysis, conjoint analysis, and multidimensional scaling analysis. Seoul apartment data is analyzed to demonstrate the practical usefulness of the system.

• Journal of Distribution Research
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• v.17 no.2
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• pp.93-115
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• 2012

This study empirically examines the impact of changes in market shares among retailing types on the price index. The retailing type is classified into 6 groups: department store, big mart, super market, convenient store, specialty merchant, and on-line store. The market shares of retailing types are calculated by the ratio of each retailing type monthly sales to total monthly retailing sales in which total retailing sales is the sum of each retailing type sales. We employed several price indices: consumer price index (CPI), CPI for living necessaries, and fresh food price index. In addition, this study used fundamental price indices based on 25 product families as well as 42 representative products. The empirical model also included several variables in order to control for the macroeconomic effects and those variables are the exchange rate, M1, an oil price, and the industrial production index. The data is monthly time-series data spanning over the period from January 2000 to December 2010. In order to test for the stability of data series, we conducted ADF test and PP test in which the model and length of lag were determined by the relevant previous literature and based on the AIC. The empirical results indicate that changes in market shares among retailing types have impacts on the price index. Table A shows that impacts differ as to which price index to use and which product families and products to use. For department store, it lowers the price of food and non-alcoholic beverages, home appliances, fresh food, fresh and vegetables, but it keeps the price high for fresh fruit. The big mart retailing type has a positive impact on the price of food, nut has a negative effect on clothing and foot wear, non-food, and fresh fruit. For super market, it has a positive impact on food and non-alcoholic beverages, fresh food, fresh shellfishes, but increases the price of CPI for living necessaries and non-food. The specialty merchant retailing type increases the price level of CPI for living necessaries and fresh fruit. For on-line store type, it keeps the price high for CPI for living necessaries and non-food as well as fresh fruit. For the analysis based on 25 product families shows that changes in market shares among retailing types also have different effects on the price index. Table B summarizes the different results. The 42 representative product level analysis is summerized in Table C and it indicates that changes in market shares among retailing types have different effects on the price index. The study offers the theoretical and practical implication to these findings and also suggests the direction for the further analysis.

• Journal of The Korean Digital Architecture Interior Association
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• v.12 no.1
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• pp.35-43
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• 2012

This study aimed to seek a method capable of hedging a rising risk of housing rent price by introducing derivatives with the target of Korean housing rent markets. The research model used in this thesis progressed a research by applying a futures contract method with the target of the rent price of major apartments in Gangnam and Gangbuk Regions of Seoul. As an analysis result, the rent price of all complexes has risen during its analysis period, so it could be confirmed that the CRB future index was also risen according to this. Finally, it was confirmed that the rising risk of the rent price can be hedged through a purchase position of futures. But, as the difference between rent price variation and CRB future index variation occurs, it appeared that 100% of hedge is difficult. However, it is judged that if considering that a method capable of hedging the rising risk of the existing rent price was nonexistent, the hedge trading effect utilizing the CRB future index on the rent price will be meaningful.

• Korea Real Estate Review
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• v.27 no.3
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• pp.71-86
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• 2017

The purpose of this study was to apply the deep running method to real estate price index predicting and to compare it with the time series analysis method to test the possibility of its application to real estate market forecasting. Various real estate price indices were predicted using the DNN (deep neural networks) and LSTM (long short term memory networks) models, both of which draw on the deep learning method, and the ARIMA (autoregressive integrated moving average) model, which is based on the time seies analysis method. The results of the study showed the following. First, the predictive power of the deep learning method is superior to that of the time series analysis method. Second, among the deep learning models, the predictability of the DNN model is slightly superior to that of the LSTM model. Third, the deep learning method and the ARIMA model are the least reliable tools for predicting the housing sales prices index among the real estate price indices. Drawing on the deep learning method, it is hoped that this study will help enhance the accuracy in predicting the real estate market dynamics.