• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.20 no.2
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• pp.123-135
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• 2016

This article deals with the pricing of Asian options under a constant elasticity of variance (CEV) model as well as a stochastic elasticity of variance (SEV) model. The CEV and SEV models are underlying asset price models proposed to overcome shortcomings of the constant volatility model. In particular, the SEV model is attractive because it can characterize the feature of volatility in risky situation such as the global financial crisis both quantitatively and qualitatively. We use an asymptotic expansion method to approximate the no-arbitrage price of an arithmetic average Asian option under both CEV and SEV models. Subsequently, the zero and non-zero constant leverage effects as well as stochastic leverage effects are compared with each other. Lastly, we investigate the SEV correction effects to the CEV model for the price of Asian options.

• KIEE International Transactions on Power Engineering
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• v.4A no.3
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• pp.159-166
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• 2004

Forecasting prices in electricity markets is critical for consumers and producers in planning their operations and managing their price risk. We utilize the generalized autoregressive conditionally heteroskedastic (GARCH) method to forecast the electricity prices in two regions of New York： New York City and Central New York State. We contrast the one-day forecasts of the GARCH against techniques such as dynamic regression, transfer function models, and exponential smoothing. We also examine the effect on our forecasting of omitting some of the extreme values in the electricity prices. We show that accounting for the extreme values and the heteroskedactic variance in the electricity price time-series can significantly improve the accuracy of the forecasting. Additionally, we document the higher volatility in New York City electricity prices. Differences in volatility between regions are important in the pricing of electricity options and for analyzing market performance.

• Journal of Integrative Natural Science
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• v.4 no.2
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• pp.121-129
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• 2011

The purpose of this paper is to estimate the fluctuation of an earning rate and risk management using the price index of Korea stocks. After an observation of conception of fluctuation, we can show volatility clustering and fluctuation phenomenon in the Korea stock price index using GARCH model with heteroscedasticity. In addition, the effects of fluctuation on the time-series was evaluated, which showed the heteroscedasticity. MCMC method and Winbugs as Bayesian computation were used for analysis.

• Journal of the Korean Society of Safety
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• v.7 no.3
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• pp.53-59
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• 1992

A general theory is developed which enables closed-cup flash points of mixtures of flammable and non-flammable liquid to be estimated from a knowledge of a certain properties of flammability diagram by thermodynamic method. The estimated equations is shown the effect of both the flame inhibiting properties of the vapor of the non-flammable component and the relative volatility of that component. The vapor phase flame inhibition effect results in a even greater elevation of flash points than the rotative volatility of that component. Especially in cases of similar vapor phase flame inhibition of the non-flammable component, the rotative volatility is affected greater elevation of flash points(extinguishing effect).

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.697-704
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• 2007

It is common in financial time series that volatility(conditional variance) as a measure of risk exhibits asymmetry in such a manner that positive and negative values of return rates of the series tend to provide different contributions to the volatility. We are concerned with asymmetric conditional variances for Korean financial time series especially during the time span of 2000-2001. Notice that these periods suffer from 9-11 disaster in US and collapses of stock prices of dot-companies in Korea. Threshold-ARCH models are considered and a Wald test of asymmetry is suggested. News impact curves are illustrated for graphical representations of leverage effects inherent in various Korean financial time series.

• Journal of the Korean Society of Safety
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• v.18 no.3
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• pp.94-100
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• 2003

Gel-like compounds containing flammable solvents were prepared to use fur cleaning agents in field of innovative industries and general purposes. And experiments were conducted to improve the defects of liquified flammable solvents from the view point of safety and health hazards. Flammable solvents used in this study were several single component flammable solvents(turpentine oil, N-methyl-2-pyrrolidone(NMP), d-limonene) and multi component flammable solvent(gasoline and ethanol). For gelation of flammable solvents, commercially Known as Aerosil(equation omitted) 200 fumed silica and triethanolamine(TEA) were used as gelation agent dispersant. The analyses on properties of gel-like compounds was studied by gelation and viscosity test pH test, volatility test and differential scanning calorimetry(DSC) measurement. The experimental results indicate that gel-like compounds containing flammable solvents have pH stability, high viscosity, volatile organic compounds(VOC) control by the decrease of volatility and odor component generation, fluidity control etc. From the experimental values, it can be predicted that the safety in the working place is improved by manufacturing flammable solvents into gel-like compounds.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.169-180
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• 2017

Multivariate GARCH models are interested in conditional variances (volatilities) as well as conditional correlations between return time series. This paper is concerned with high-frequency multivariate financial time series from which realized volatilities and realized conditional correlations of intra-day returns are calculated. Existing multivariate GARCH models are reviewed comparatively with the realized volatility via canonical correlations and value at risk (VaR). Korean stock prices are analysed for illustration.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.29-42
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• 2018

We combine the integer-valued GARCH(1, 1) model with a generalized regime-switching model to propose a dynamic count time series model. Our model adopts Markov-chains with time-varying dependent transition probabilities to model dynamic count time series called the generalized regime-switching integer-valued GARCH(1, 1) (GRS-INGARCH(1, 1)) models. We derive a recursive formula of the conditional probability of the regime in the Markov-chain given the past information, in terms of transition probabilities of the Markov-chain and the Poisson parameters of the INGARCH(1, 1) process. In addition, we also study the forecasting of the Poisson parameter as well as the cumulative impulse response function of the model, which is a measure for the persistence of volatility. A Monte-Carlo simulation is conducted to see the performances of volatility forecasting and behaviors of cumulative impulse response coefficients as well as conditional maximum likelihood estimation; consequently, a real data application is given.

• Quantitative Bio-Science
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• v.37 no.2
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• pp.73-79
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• 2018

As high frequency (HF, for short) time series is now prevalent in the presence of real time big data, volatility computations based on traditional ARCH/GARCH models need to be further developed to suit the high frequency characteristics. This article reviews realized volatilities (RV) and multivariate GARCH (MGARCH) to deal with high frequency volatility computations. As a (functional) infinite dimensional models, the fARCH and fGARCH are introduced to accommodate ultra high frequency (UHF) volatilities. The fARCH and fGARCH models are developed in the recent literature by Hormann et al. [1] and Aue et al. [2], respectively, and our discussions are mainly based on these two key articles. Real data applications to domestic UHF financial time series are illustrated.

• Journal of Information Technology Applications and Management
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• v.25 no.4
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• pp.157-169
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• 2018

The purpose of this study was to examine the volatility of bitcoin, diagnose if bitcoin are a systematic risk asset, and evaluate their effectiveness by estimating market beta representing systematic risk using GARCH (Generalized Auto Regressive Conditional Heteroskedastieity) model. First, the empirical results showed that the market beta of Bitcoin using the OLS model was estimated at 0.7745. Second, using GARCH (1, 2) model, the market beta of Bitcoin was estimated to be significant, and the effects of ARCH and GARCH were found to be significant over time, resulting in conditional volatility. Third, the estimated market beta of the GARCH (1, 2), AR (1)-GARCH (1), and MA (1)-GARCH (1, 2) models were also less than 1 at 0.8819, 0.8835, and 0.8775 respectively, showing that there is no systematic risk. Finally, in terms of efficiency, GARCH model was more efficient because the standard error of a market beta was less than that of the OLS model. Among the GARCH models, the MA (1)-GARCH (1, 2) model considering non-simultaneous transactions was estimated to be the most appropriate model.