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This study explores the arbitrage profitability of box spread strategies to test the KOSPI200 options market efficiency. using minute-by-minute data for the December 2003 - June 2004 period. The sample consists of 39.445 and 38.318 observations for small discrepancy and large discrepancy in exercise prices. respectively.

In the case of credit box spreads, there were 681 (2%) and 2.293 (6%) arbitrage observations for small and large discrepancies, while debit box spreads showed 831 (2%) and 3.098 (8%) observations for small and large discrepancies. In general, mean profit and median profit were different, and the arbitrage profit varied over time. The time to option expiration did not impact the arbitrage profit.

Also, the arbitrage profit of box spreads was significantly higher on Fridays for large discrepancy, and it varied across weekdays except the case of small-discrepancy debit box spread. Both arbitrage opportunities and profits substantially decreased as execution time increased. Our overall results suggest that the KOSPI200 options market has been efficient.

This study explores hedging strategies that use the KTB futures to hedge the price risk of the KTB spot portfolio. The study establishes the price sensitivity, risk-minimization, bivariate GARCH (1,1) models as hedging models, and analyzes their hedging performances. The sample period covers from September 29, 1999 to September 18, 2001. Time-matched prices at 11:00 (11:30) of the KTB futures and spot were used in the analysis. The most important findings may be summarized as follows. First, while the average hedge ration of the price sensitivity model is close to one, both the risk-minimization and GARCH model exhibit hedge ratios that are substantially lower than one. Hedge ratios tend to be greater for daily data than for weekly data. Second, for the daily in-sample data, hedging effectiveness is the highest for the GARCH model with time-varying hedge ratios, but the risk-minimization model with constant hedge ratios is not far behind the GARCH model in its hedging performance. In the case of out-of-sample hedging effectiveness, the GARCH model is the best for the KTB spot portfolio, and the risk-minimization model is the best for the corporate bond portfolio. Third, for daily data, the in-sample hedge shows a better performance than the out-of-sample hedge, except for the risk-minimization hedge against the corporate bond portfolio. Fourth, for the weekly in-sample hedges, the price sensitivity model is the worst and the risk-minimization model is the best in hedging the KTB spot portfolio. While the GARCH model is the best against the KTB +corporate bond portfolio, the risk-minimization model is generally as good as the GARCH model. The risk-minimization model performs the best for the weekly out-of-sample data, and the out-of-sample hedges are better than the in-sample hedges. Fifth, while the hedging performance of the risk-minimization model with daily moving window seems somewhat superior to the traditional risk-minimization model when the trading volume increased one year after the inception of the KTB futures, on the average the traditional model is better than the moving-window model. For weekly data, the traditional model exhibits a better performance. Overall, in the Korean bond markets, investors are encouraged to use the simple risk-minimization model to hedge the price risk of the KTB spot and corporate bond portfolios.

In the KOSPI2oo futures and option markets. additional fifteen minutes (15 : 00∼15 개5) after the underlying stock market close are given tor the adjustments of the futures and option positions. During the first five minutes. 15: 00∼15 : 05. a continuous auction trading is made. while the trading at a single clearing price is made for the remaining ten minutes. 15: 05∼15: 15.

Previous studies focused on the synchronous trading in terms of transaction time in the analysis of the lead-lag relationship. truncating the futures and option data during 15 : 00∼15 : 15. In this article. we explore how the KOSPI2oo futures and option returns for the extra fifteen minutes impact the next day's KOSPI200 cash returns, We also examine the lead-lag relationship during the reggular trading hours (9 : 00∼15 : 00) and the impact of the cash returns during 14 : 20∼15 : 00 on futures and option returns during 15 : 00∼15: 15. Our main findings are summarized as follows.

First. the KOSPI200 futures and option returns during 15 : 00∼15 : 15 lead the close-to-open KOSPI200 cash return, even though the trading volume and return volatility during 15: 00∼15: 15 are lower relative to the regular stock market session (9 : 00∼15: 00). The impact of the futures and option returns on the cash return lasts hlK) minutes and one minute‘ repectively. after the next day open. Second. the option return during the continuous auction trading session (15 : 00∼ 15 : 05) leads the close-to-open cash return. while the futures return of trading at a single clearing price during 15 : 05∼15 : 10 impacts the close-to-open cash return. Third, we found that the lead-lag relationships among the KOSPI200 futures, option, and cash returns are not constant during the reg비ar stock market session‘ In partieular. the impact of the KOSPI200 cash ret un during 14 : 40∼15 : 00 on the futures and option retuns for the 15 : 00∼15: 15 Interval is much stronger. compared with other time zones.

Finally. the KOSPI200 cash return during the last ten minutes of trading at a Single clearing price (14 : 50∼15 : 00). significantly impacts the option return during 15: 00∼15: 05. while there is no impact on the futures return (15 : 00∼15: 15).

This study examines the lead-lag relationship between KOSPI200 and the volatility index based on the implied volatility from the KOSPI200 options. The sample period covers from January 2, 2003 to June 30, 2004. Both daily and minute-by-minute data were used for the lead-lag analysis. The study also determines whether the response of volatil ity index to KOSPI200 is symmetric or not. The most important findings may be summarized as follows.

First, there is no lead-lag relationship between the change in volatility index and the KOSPI200 returns on a daily basis. However, on a minute-by-minute basis, volatility index leads KOSPI200 for the group of largest increases in volatility index, and the opposite is true for the group of largest decreases and least changes in volatility index. The option market appears to react more quickly to volatility increases, while the stock market seems more sensitive to volatility decreases. Second, the volatility increase in response to the stock market decline is more severe than the volatility decrease in response to the stock market rise for daily data. This evidence of asymmetry suggests that volatility index plays a role of investors’fear gauge. Our results show no asymmetric response of volatility index to stock market movements for weekly data.

This study explores the arbitrage profitability of the KOSPI200 futures spread, using intraday data during 10 days prior to the expiration day of each contract for the 9/3/2001 ∼ 6/912005 period. The theoretical frameworks for arbitrage strategies were developed for the analysis. Our results show that 97.36% of the total 8.633 observations were fairly priced. 1.46% (126 observations) were underpriced, and 1.18% (102 observations) were overpriced, in the ex post arbitrage profitability analysis between the futures spread and the calendar spread. Also, in the arbitrage profitability analysis based on the mispricing of the KOSPI200 futures spread against the theoretical price. 90.39% of the total 10.054 observations were fairly priced and 9.61 % (966 observations) were underpriced. There was no overpriced observation. The ajority of those underpriced observations were concentrated in the 3rd Quarter of 2001 and the 1st quarter of 2003. Overall, there were very few arbitrage opportunities except for the introductory period and some contracts with high uncertainty, implying that the KOSPI200 futures spread market has been generally efficient.

This study explores the Granger causal relationship between return and volume in the KOSPI200 spot and option markets for the period from December 13. 2002 to December 9. 2004. using minute-by-minute data. Specifically, we examine the lead-lag relationship among OPtion volume, option return, cash volume, and cash return to determine whether option volume and return impact cash return.

Our results show that option volume has no direct impact on cash return as cash return unilaterally leads option volume‘ While option volume impacts cash volume. cash return unilaterally leads cash volume. implying no indirect impact of option volume on cash return.

However, there is evidence that option return impacts cash return directly, given a bilateral causality between option return and casll return. Option return also impacts cash volume, but again cash volume has no impact on cash return. meaning no indirect impact of option return on cash return. Our findings were generally robust across days of the week and different maturities. Finally, we analyzed lead-lag relationship within the option market. and found a bilateral causality between option volume and option return. This implies that option volume may impact cash return indirectly via option return.

This paper investigates a retrospective on the Journal of Derivatives and Quantitative Studies (JDQS) on its 30th anniversary based on bibliometric. JDQSs yearly publications, citations, impact factors, and centrality indices grew up in early 2010s, and diminished in 2020. Keyword network analysis reveals the JDQS's main keywords including behavioral finance, implied volatility, information asymmetry, price discovery, KOSPI200 futures, volatility, and KOSPI200 options. Citations of JDQS articles are mainly driven by article age, demeaned age squared, conference, nonacademic authors and language. In comparison between number of views and downloads for JDQS articles, we find that recent changes in publisher and editorial and publishing policies have increased visibility of JDQS.

In this paper, we examine whether fund market reactions are affected by the characteristics of categorized features of fund. To investigate the goal of the paper, we consider macroeconomic factors as well as financial characters. We classify fund flow into four groups depending upon type of fund and fund characters to determine which category is better to increase fund flow for capital market after these financial occurrence. In this regard, our research suggests important evidence about the effect of financial factor on fund flow with a case of an detailed situation in Emerging market. In order to test the hypothesis, we use seemingly unrelated regression (SUR) model to choose significant factors among various types of fund market-related changes. Our sample consisted of fund flows from 2006 to 2016 collected by Korean Financial Association and Bank of Korea. The empirical results are summarized as follows : First, we find that capital market index, exchange rate affect fund flows with time-lagged value changes. Second, the stock index fund and banking sector fund sales show strong positive relations with the fund flow changes. Third, values of the fund flow are significantly related with fund sales by asset management’s affiliated financial institution. These results are consistent with the hypotheses that the increase and decrease in the fund flows due to capital market situation are more pronounced as the financial factors fit. Our results suggest that it is necessary to consider the fundamental characteristics of fund flow changes as well as the external economic environment to get a more efficient market performance and supervision.

The purpose of this paper is to present a new strategic framework of Supply Chain Management (SCM) in the automobile industry. For our purpose, we first had studied about the structure of relationship between supplier and buyer in Korean automobile industry. With this study, we searched for factors which compose a strategy of SCM, and whether or not the companies’ performance that are influenced by specific SCM strategy factors. Of course, our study based on existing researches, especially Cox et al.(1995) and Venkatraman et al.(1992), but the existing researches is differentiated in this paper that is treated several power factors as resource, value, environment and relation. So, results of our paper are what a good strategy of SCM composed by these factors and how to adopt this strategy on global logistics. And our methodology has some using of statistics method by SPSS 14(v) such as factor analysis, reliability analysis, and SEM(Structural Equation Model) with AMOS.

By using the annual time series data from 1986 to 2008 in South Korea (hereafter Korea) we will examine the possibility that the difference in trade liberalization is causal to the differentials in growth and measure the sensitivity of growth to changes in trade liberalization. For the estimation, we will use both alternative measures of trade liberalization; imports for the total factor cost of national income for trade openness (hereafter openness) and the sum of exports and imports for the total factor cost of national income for trade globalization (hereafter globalization). The regression results suggest that both openness and globalization make a substantial contribution towards Korea’s economic growth. In a comparison between these two indicators of trade liberalization, openness is more sensitive to growth. A negative and statistically significant error correction term implies that the null hypothesis of no co-integration is rejected when one period lag is used. The existence of co-integration means that openness and globalization policies can be targeted in order to bring about a desired long-run effect as well as a short run effect on growth.