• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6084-6091
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• 2014

This paper presents the GJR GARCH model (Glosten et. al, 1993) to analyze the influences of volume volatilities on price volatilities in the fishery market. For the analysis, this study used the monthly price and volume data of aquacultural flatfish in Jeju. As a result, empirical analysis suggested volatility clustering. The persistency parameter(${\lambda}$) was estimated to be approximately 1 in aquacultural flatfish. The results showed that there is a significant negative relationship between the conditional variance of supply and that of price for aquacultural flatfish. This means that the general law of supply is valid. Finally, the empirical analysis was that an asymmetric coefficient (${\gamma}$) of GJR GARCH model was negative (-). This means that the higher volatility of volume leads to lower price volatility. That is, it is useful to make government policies that can adjust the volume (stockpiling, stabilizing supply and demand).

• New & Renewable Energy
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• v.10 no.1
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• pp.41-49
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• 2014

For a proper valuation of wind power project, it is necessary to consider volatilities of key parameters such as annual energy production, electricity sales price, and long term interest rate. Real option methodology allows to calculate option values of these parameters. Volatilities to be considered in wind project valuation are 1) annual energy production (AEP) estimation due to meteorological variation and estimation errors in wind speed distribution, 2) changes in system marginal price (SMP), and 3) interest rate fluctuation of project financing which provides refinancing option to be exercised during a loan tenor for commercial scale projects. Real option valuation turns out to be more than half of the sales value based on a case study for a FIT scheme wind project that was sold to a financial investor.

• Journal of the Semiconductor & Display Technology
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• v.10 no.4
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• pp.71-77
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• 2011

This paper presents a production planning methodology for semiconductor manufacturing supply chain network with volatilities caused by uncertainties such as unstable demand and price. In order to take volatilities into account, we develop two approaches; 1) stochastic model with consideration of various cases and 2) deterministic model considering replanning cost, and propose efficient solution methods. Computational experiments show that the performance of the proposed method is superior to that of deterministic approach using various scenarios.

• Proceedings of the Safety Management and Science Conference
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• 2005.11a
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• pp.537-557
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• 2005

The estimate on volatility of stock price is related with optimum of portfolio and Important for allocation of capital asset. If the volatility of stock price is varied according to macroeconomic variables on monetary policy and industrial production, it will assist capital asset to allocate. This paper is related with stock market volatilities on macroeconomic variables in U.S. and Europe, Korea. And, it Is pertain to vary in time of this variables. Thus, this paper is related with volatilities of monetary and physical macroeconomic variables on basis of statistics. And, it is ranged front capital investment to portfolio allocation. Also, this paper takes out of sample forecast and study more after this. In case Germany, France, Italy and the Netherlands, the relative importance of monetary policy and Industrial production Is different from these countries. In case Italy and the Netherlands, monetary policy is primary factor at stabilizing for volatility of stock price. In case Korea, increasing monetary policy and industrial production is positively affected stock market. It is that the positive effect of stock price is caused by mollifying monetary policy and economic growth. Specially, this conclusion is similar to US. In Korea, gradual increase in monetary and industrial production is necessary to stability of stock market. It is different to previous results on basis of increasing stock price of money in long period.

• 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.

• Korean Business Review
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• v.20 no.2
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• pp.15-27
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• 2007

This paper analyzed the relationship between Housing Purchase Price volatility of Seoul and Housing Purchase Price volatility of local large city. Other studies investigates the effect on the observed volatility Observed volatility consists of fundamental volatility and transitory volatility. Fundamental volatility is caused by information arrival and transitory volatility is caused by noise trading. Fundamental volatility is trend component and is modelled as a random walk with drift. Transitory volatility is cyclical component and is modelled as a stationary process. In contrast to other studies, this study investigates the effect on the fundamental volatility and transitory volatility individually. Observed volatility is estimated by GJR GARCH(1,1) model. We find that GJH GARCH model is superior to GARCH model and good news is more remarkable effect on volatility than bad news. This study decomposes the observed volatility into fundamental volatility and transitory volatility using Kalman filtering method. The findings in this paper is as follows. The correlation between Seoul housing price volatility and Busan housing price volatility is high. But, the correlation between Seoul and Daejeon is low. And the correlation between Daejeon and Busan is low. As a distinguishing feature, the correlation between fundamental volatilities is high in the case of all pairs. But, the correlation between transitory volatilities turns out low. The reason is as follows. When economic information arrives, Seoul, Daejeon, and Busan housing markets, all together, are affected by this information.

• Journal of Construction Engineering and Project Management
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• v.7 no.3
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• pp.15-25
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• 2017

The existence of material price volatility in construction projects puts forward substantial risks for all parties involved. Depending on the parties involved in the project, type of contracts, and state of the market various risk management strategies are practiced by contracting parties to manage project risks related to price volatility. Unfortunately, in many cases companies fail to select an adequate approach to better manage volatilities of material prices due to the lack of a decision support system to aid in the selection of an appropriate strategy based on the project characteristics. The aim of this study is to identify critical project factors and align them to documented strategies to manage price volatility based on an extensive literature review and industry interviews. This study found Integrated Project Delivery (IPD) as the ideal strategy with respect to project duration; quantitative risk management methods with respect to the cost; and Price Adjustment Clauses (PAC) with respect to the risk allocation, as the top price volatility management strategies.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.143-150
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• 2020

This study aims to investigate the effect of oil price and exchange rate on the two Vietnamese stock market indices: VN index and HXN index. This study uses the daily data from August 1^st 2000 to October 25^th 2019 of the two Vietnamese stock indices: VN index and HNX index, the two oil price indices: BRENT and WTI, and the two exchange rates: US dollar to Vietnamese dong and Euro to Vietnamese dong. Due to the presence of heteroskedasticity in our data, we use GARCH (1,1) regression model to perform our analysis. Our findings show that the oil price has a significant positive effect on the two Vietnamese stock market indices. In terms of the stock index volatility, both the VN index and HNX index volatilities are negatively impacted by the return of oil price. While the conclusion about the impact of oil price remained consistent through all three robustness tests, the effect of exchange rate on Vietnamese stock market indices is not consistent. We find thatchanges of the USD/VND exchange rate significantly impact the return and volatility of HNX index only in GARCH (1,1) setting. Our analysis also survives a number of robustness tests.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.1-9
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• 2021

UAE has rapidly developed into one of the leading global financial hubs, with significant transformations in its stock exchanges. In its attempt at economic diversification in the last two decades, the country has also taken a lead in the GCC region in introducing extensive reforms to attract FDI to the Emirates. However, oil price volatilities have posed a significant challenge to all oil-exporting countries. The main aim of this study is to explore the impact of economic diversification and oil price on the UAE stock market. The study applies Granger Causality and Vector Autoregressive Model on monthly Abu Dhabi stock exchange index, Dubai Fateh crude oil spot price, and FDI inflows during 2001-19. The short-term interbank rate has been included as a monetary policy variable. The results show a substantial difference between the two phases of reforms. Oil price and Abu Dhabi stock index show bidirectional relationship during 2001-09 but no causality was found during 2010-19. Furthermore, the second phase was characterized by unidirectional causation from FDI to ADX index. This study highlights FDI inflows as a key driver of stock market performance during the last decade and emphasizes the success of the intense reforms in the UAE initiated for the diversification of its economy.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.369-378
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• 2013

The Korean Won-Dollar exchange markets showed radical price movements in the late 1990s and 2008. Therefore it provides good sources for studying volatility phenomena. Using the GARCH option models, I analysed how the prices of foreign exchange options react volatilities in the foreign exchange spot prices. For this I compared the explanatory power of three option models(Black and Scholes, Duan, Heston and Nandi), using the Won-Dollar OTC option markets data from 2006 to 2013. I estimated the parameters using MLE and calculated the mean square pricing errors. According to the my empirical studies, the pricing errors of Duan, Black and Scholes models are 0.1%. And the pricing errors of the Heston and Nandi model is greatest among the three models. So I would like to recommend using Duan or Black and Scholes model for hedging the foreign exchange risks. Finally, the historical average of spot volatilities is about 14%, so trading the options around 5% may lead to serious losses to sellers.