• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.221-230
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• 2016

This paper investigates volatilities of stock returns based on high frequency data from stock market. Incorporating the price duration as one of the factors in volatility, we employ the autoregressive conditional duration (ACD) model for the price duration in addition to the GARCH model to analyze stock volatilities. A combined ACD-GARCH model is analyzed in which a double-threshold is introduced to accommodate asymmetric features on stock volatilities.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.601-611
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• 2021

There are many asset prices which are interlinked and have a bearing on the stock market index. Studies have shown that the interrelationship among these asset prices vary and are inconsistent. The ultimate aim of this study is to examine the dynamic relationship between gold price, oil price, exchange rate and stock index. Monthly time series data has been utilized by the researcher to examine the interrelationship between four variables. The relationship among stock exchange rate index, oil price and gold price have been undertaken using regression and granger causality test. The results indicate that the exchange rate and oil price have an indirect influence on NIFTY; whereas gold price had a direct impact on NIFTY. It is evident from the results that volatility in the price of gold is mainly dependent on the exchange rate and vice versa. All the variables affect NIFTY in some way or the other. However, gold has a direct and vital relationship. From the study findings, it can be concluded that macroeconomic variables like commodity prices and foreign exchange rate, gold and oil, have a strong relationship on the return on securities at the national stock exchange of India.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.65-74
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• 2021

The rapid change in gold price is an issue of concern in the global economy and financial markets. Gold has been used as a means for trading and transaction around the world for long period of time and it plays an integral role in monetary, business, commercial and financial activities. More importantly, it is used as economic measure for the global economy and will continue to play an important economic vital role - both locally and globally. There has been an explosive growth in demand for efficient and effective scheme to predict gold price due its volatility and fluctuation. Hence, there is need for the development of gold price prediction scheme to assist and support investors, marketers, and financial institutions in making effective economic and monetary decisions. This paper primarily proposed an intelligent based system for predicting and characterizing the gold market trend. The simulation result shows that the proposed intelligent gold price scheme has been able to predict the gold price with high accuracy and precision, and ultimately it has significantly reduced the prediction error when compared to baseline neural network (NN).

• The Korean Journal of Financial Studies
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• v.3 no.1
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• pp.233-265
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• 1996

We examine the impact of public and private information on price movements using the thirty DJIA stocks and twenty-one NASDAQ stocks. We find that the standard deviation of daily returns on information days (dividend announcement, earnings announcement, insider purchase, or insider sale) is much higher than on no-information days. Both public information matters at the NYSE, probably due to masked identification of insiders. Earnings announcement has the greatest impact for both DJIA and NASDAQ stocks, and there is some evidence of positive impact of insider asle on return volatility of NASDAQ stocks. There has been considerable debate, e.g., French and Roll (1986), over whether market volatility is due to public information or private information-the latter gathered through costly search and only revealed through trading. Public information is composed of (1) marketwide public information such as regularly scheduled federal economic announcements (e.g., employment, GNP, leading indicators) and (2) company-specific public information such as dividend and earnings announcements. Policy makers and corporate insiders have a better access to marketwide private information (e.g., a new monetary policy decision made in the Federal Reserve Board meeting) and company-specific private information, respectively, compated to the general public. Ederington and Lee (1993) show that marketwide public information accounts for most of the observed volatility patterns in interest rate and foreign exchange futures markets. Company-specific public information is explored by Patell and Wolfson (1984) and Jennings and Starks (1985). They show that dividend and earnings announcements induce higher than normal volatility in equity prices. Kyle (1985), Admati and Pfleiderer (1988), Barclay, Litzenberger and Warner (1990), Foster and Viswanathan (1990), Back (1992), and Barclay and Warner (1993) show that the private information help by informed traders and revealed through trading influences market volatility. Cornell and Sirri (1992)' and Meulbroek (1992) investigate the actual insider trading activities in a tender offer case and the prosecuted illegal trading cased, respectively. This paper examines the aggregate and individual impact of marketwide information, company-specific public information, and company-specific private information on equity prices. Specifically, we use the thirty common stocks in the Dow Jones Industrial Average (DJIA) and twenty one National Association of Securities Dealers Automated Quotations (NASDAQ) common stocks to examine how their prices react to information. Marketwide information (public and private) is estimated by the movement in the Standard and Poors (S & P) 500 Index price for the DJIA stocks and the movement in the NASDAQ Composite Index price for the NASDAQ stocks. Divedend and earnings announcements are used as a subset of company-specific public information. The trading activity of corporate insiders (major corporate officers, members of the board of directors, and owners of at least 10 percent of any equity class) with an access to private information can be cannot legally trade on private information. Therefore, most insider transactions are not necessarily based on private information. Nevertheless, we hypothesize that market participants observe how insiders trade in order to infer any information that they cannot possess because insiders tend to buy (sell) when they have good (bad) information about their company. For example, Damodaran and Liu (1993) show that insiders of real estate investment trusts buy (sell) after they receive favorable (unfavorable) appraisal news before the information in these appraisals is released to the public. Price discovery in a competitive multiple-dealership market (NASDAQ) would be different from that in a monopolistic specialist system (NYSE). Consequently, we hypothesize that NASDAQ stocks are affected more by private information (or more precisely, insider trading) than the DJIA stocks. In the next section, we describe our choices of the fifty-one stocks and the public and private information set. We also discuss institutional differences between the NYSE and the NASDAQ market. In Section II, we examine the implications of public and private information for the volatility of daily returns of each stock. In Section III, we turn to the question of the relative importance of individual elements of our information set. Further analysis of the five DJIA stocks and the four NASDAQ stocks that are most sensitive to earnings announcements is given in Section IV, and our results are summarized in Section V.

• Journal of Korea Technology Innovation Society
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• v.20 no.3
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• pp.732-753
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• 2017

Recently, when evaluating the technology values in the fields of biotechnology, pharmaceuticals and medicine, we have needed more to estimate those values in consideration of the period and cost for the commercialization to be put into in future. The existing discounted cash flow (DCF) method has limitations in that it can not consider consecutive investment or does not reflect the probabilistic property of commercialized input cost of technology-applied products. However, since the value of technology and investment should be considered as opportunity value and the information of decision-making for resource allocation should be taken into account, it is regarded desirable to apply the concept of real options, and in order to reflect the characteristics of business model for the target technology into the concept of volatility in terms of stock price which we usually apply to in evaluation of a firm's value, we need to consider 'the continuity of stock price (relatively minor change)' and 'positive condition'. Thus, as discussed in a lot of literature, it is necessary to investigate the relationship among volatility, underlying asset values, and cost of commercialization in the Black-Scholes model for estimating the technology value based on real options. This study is expected to provide more elaborated real options model, by mathematically deriving whether the ratio of the present value of the underlying asset to the present value of the commercialization cost, which reflects the uncertainty in the option pricing model (OPM), is divided into the "no action taken" (NAT) area under certain threshold conditions or not, and also presenting the estimation logic for option values according to the observation variables (or input values).

• Management & Information Systems Review
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• v.31 no.3
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• pp.307-321
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• 2012

This paper examines the spillover effect between Korea stock market and Chinese stock market according to increasing economic power of Chinese. Chinese stock market start the transaction one hour and half early than Korea stock market. Especially we focuses the response of Korea stock market after Chinese stock market starts. So we analyze the return an volatility of Korea stock market after 10:30. We employee daily and intraday stock return and volatility. The sample period ranges from January 2008 to April 2010 total 28 months. Our results show that the gap of open price between Korea and Chinese stock market affect the five minute return and volatility of Korea stock market but don't affect the ten minute return and volatility. Recently, this spillover effect has increased more and more. This shows the rapid increase of economic power of Chinese to affect the Korea capital market.

• Journal of Korea Port Economic Association
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• v.35 no.2
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• pp.61-76
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• 2019

The purpose of this study is to use the EGARCH model and Granger causality test to analyze how the change in the BDI affects the Korean stock price volatility. The main analysis results are summarized as follows. First, according to the results of the mean equation, the change in the BDI is significant in large-cap stocks, as well as in the manufacturing, service, and chemistry indexes, but not in others. This implies that the Korean stock market does not respond appropriately to the maritime market situation; further, the increase in demand for raw materials has not led to a real economic recovery. Second, in the result of the variance equation, the coefficient on the change in the BDI is negative(-), and the change in the BDI is significant for all size indexes. Particularly, the change in the BDI has a greater impact on the volatility of small-cap stocks than that of large-cap stocks. The results of the analysis of the sector indexes were statistically significant for the service, financial, construction, and electric and electronics industries, but not for the manufacturing and chemical industries. In particular, the changes in the BDI have the greatest impact on the construction industry. Third, according to the Granger causality test results, the change in the BDI leads the financial industry and construction industry. There is, however, no relationship between the BDI and the other indexes. This shows that change in the shipping freight index can be used to predict the volatility in the Korean stock market. This can help investors and policymakers make better decisions.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.81-93
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• 2022

The stock investing is one of the most popular investment techniques. However, since it is not easy to obtain a return through actual investment, various strategies have been devised and tried in the past to obtain an effective and stable return. Among them, the volatility breakout strategy identifies a strong uptrend that exceeds a certain level on a daily basis as a breakout signal, follows the uptrend, and quickly earns daily returns. It is one of the popular investment strategies that are widely used to realize profits. However, it is difficult to predict stock prices by understanding the price trend pattern of stocks. In this paper, we propose a method of buying and selling stocks by predicting the return in trading based on the volatility breakout strategy using a bi-directional long short-term memory deep neural network that can realize a return in a short period of time. As a result of the experiment assuming actual trading on the test data with the learned model, it can be seen that the results outperform both the return and stability compared to the existing closing price prediction model using the long-short-term memory deep neural network model.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.41-57
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• 2023

The drybulk shipping market has high freight rate volatility in the chartering market and various and complex factors affecting the market. In the unstable economic situation caused by the COVID-19 pandemic in 2020, the BDI plunged due to a decrease in trade volume, but turned from the end of 2020 and maintained a booming period until the end of 2022. The main reason for the market change is the decrease in the available fleet that can actually be operated for cargo transport due to port congestion by the COVID-19 pandemic, regardless of the fleet and trade volume volatility that have affected the drybulk shipping market in the past. A decrease in the actual usable fleet due to vessel waiting at port by congestion led to freight increase, and the freight increase in charting market led to an increase in second-hand ship and new-building ship price in long-term equilibrium relationship. In the past, the drybulk shipping market was determined by the volatility of fleet and trade volume. but, in the future, available fleet volume volatility by pandemics, environmental regulations and climate will be the important factors affecting BDI. To response to the IMO carbon emission reduction in 2023, it is expected that ship speed will be slowed down and more ships are expected to be needed to transport the same trade volume. This slowdown is expected to have an impact on drybulk shipping market, such as a increase in freight and second-hand ship and new-building ship price due to a decrease in available fleet volume.

• Culinary science and hospitality research
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• v.22 no.3
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• pp.212-223
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• 2016

The purpose of this study is to verify information spillover effects using returns of macroeconomic variables and hotel leisure stock index daily data from January 4, 2000 to December 30, 2015. The findings and implications of the research can be summarized as follows. First, based on time-varying AR(1)-GARCH(1,1) models no evidence of statistically significant conditional mean and volatility spillover effects from returns of macroeconomic variables on the hotel leisure stock index was observed. In addition, no evidence of price volatility spillover from macroeconomic variables on the hotel leisure market was observed. Second, it was discovered that there exists a significantly negative relationship between the return of ER and hotel leisure stock prices, but a positive relationship between the KOSPI and hotel leisure stock prices. Finally, the study also found that was a significantly positive relationship between the volatility of DUB and hotel leisure market, and an adversely negative relationship between the volatility of ER and hotel leisure market. The results of this study are expected to contribute by providing useful information for investment strategies, as well as for risk management for investors and managers.