• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.505-512
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• 2016

The paper is concerned with high frequency financial time series. A weighted hybrid volatility is suggested to compute daily volatilities based on high frequency data. Various realized volatility (RV) computations are reviewed and the weights are chosen by minimizing the differences between the hybrid volatility and the realized volatility. A high frequency time series of KOSPI200 index is illustrated via QLIKE and Theil-U statistics.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1163-1170
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• 2015

Various computational methods for obtaining volatilities for financial time series are reviewed and compared with each other. We reviewed model based GARCH approach as well as the data based method which can essentially be regarded as a smoothing technique applied to the squared data. The method for high frequency data is focused to obtain the realized volatility. A hybrid method is suggested by combining the model based GARCH and the historical volatility which is a data based method. Korea stock prices are analysed to illustrate various computational methods for volatilities.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2010.05a
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• pp.53-56
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• 2010

In this study, we conducted an impact assessment of the amount of volatile organic solvents addition on hybrid water-proofing system of urethane waterproof coating material and modified asphalt sheet. Also, we conducted a comparative assessment of whether modified asphalt sheet is dissolved or not and oil leakage by dissolution in order to perform a comparative analysis of characteristics of the impact on modified asphalt sheet according to the volatility of volatile organic solvents included in urethane waterproof coating material. The test was carried out by adding the same amount of organic solvents into each experimental group which is subject to volatility and non-volatility of organic solvents, respectively. The results of the test showed that in both experimental groups modified asphalt sheet was dissolved when adding over 10 percent of organic solvents regardless of volatility, and oil leakage observed only in the experimental group subject to volatility.

• Journal of the Korean Mathematical Society
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• v.57 no.5
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• pp.1167-1186
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• 2020

This paper considers the case of pricing discretely-sampled variance swaps under the class of equity-interest rate hybridization. Our modeling framework consists of the equity which follows the dynamics of the Heston stochastic volatility model, and the stochastic interest rate is driven by the Cox-Ingersoll-Ross (CIR) process with full correlation structure imposed among the state variables. This full correlation structure possesses the limitation to have fully analytical pricing formula for hybrid models of variance swaps, due to the non-affinity property embedded in the model itself. We address this issue by obtaining an efficient semi-closed form pricing formula of variance swaps for an approximation of the hybrid model via the derivation of characteristic functions. Subsequently, we implement numerical experiments to evaluate the accuracy of our pricing formula. Our findings confirm that the impact of the correlation between the underlying and the interest rate is significant for pricing discretely-sampled variance swaps.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.75-87
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• 2023

We designed to employ an Artificial Intelligence learning model to predict real estate prices and determine the reasons behind their changes, with the goal of using the results as a guide for policy. Numerous studies have already been conducted in an effort to develop a real estate price prediction model. The price prediction power of conventional time series analysis techniques (such as the widely-used ARIMA and VAR models for univariate time series analysis) and the more recently-discussed LSTM techniques is compared and analyzed in this study in order to forecast real estate prices. There is currently a period of rising volatility in the real estate market as a result of both internal and external factors. Predicting the movement of real estate values during times of heightened volatility is more challenging than it is during times of persistent general trends. According to the real estate market cycle, this study focuses on the three times of extreme volatility. It was established that the LSTM, VAR, and ARIMA models have strong predictive capacity by successfully forecasting the trading price index during a period of unusually high volatility. We explores potential synergies between the hybrid artificial intelligence learning model and the conventional statistical prediction model.

• Journal of the Korean Mathematical Society
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• v.59 no.6
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• pp.1153-1170
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• 2022

In this paper, we investigate the portfolio optimization problem under the SVCEV model, which is a hybrid model of constant elasticity of variance (CEV) and stochastic volatility, by taking into account of minimum-entropy robustness. The Hamilton-Jacobi-Bellman (HJB) equation is derived and the first two orders of optimal strategies are obtained by utilizing an asymptotic approximation approach. We also derive the first two orders of practical optimal strategies by knowing that the underlying Ornstein-Uhlenbeck process is not observable. Finally, we conduct numerical experiments and sensitivity analysis on the leading optimal strategy and the first correction term with respect to various values of the model parameters.

• East Asian mathematical journal
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• v.40 no.1
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• pp.63-74
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• 2024

Timer options are financial instruments proposed by Société Générale Corporate and Investment Banking in 2007. Unlike vanilla options, where the expiry date is fixed, the expiry date of timer options is determined by the investor's choice, which is in linked to a variance budget. In this study, we derive a pricing formula for hybrid options that combine timer options, digital options, and power options, considering an environment where volatility of an underlying asset follows a fast-mean-reverting process. Additionally, we aim to validate the pricing accuracy of these analytical formulas by comparing them with the results obtained from Monte Carlo simulations. Finally, we conduct numerical studies on these options to analyze the impact of stochastic volatility on option's price with respect to various model parameters.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.201-207
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• 2023

In this paper, we propose an optimal model for mid to long-term price prediction of agricultural products using LGBM, MLP, LSTM, and GRU to compare and analyze the three strategies of the Multi-Step Time Series. The proposed model is designed to find the optimal combination between the models by selecting methods from various angles. Prior agricultural product price prediction studies have mainly adopted traditional econometric models such as ARIMA and LSTM-type models. In contrast, agricultural product price prediction studies related to Multi-Step Time Series were minimal. In this study, the experiment was conducted by dividing it into two periods according to the degree of volatility of agricultural product prices. As a result of the mid-to-long-term price prediction of three strategies, namely direct, hybrid, and multiple outputs, the hybrid approach showed relatively superior performance. This study academically and practically contributes to mid-to-long term daily price prediction by proposing an effective alternative.

• Korean Chemical Engineering Research
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• v.56 no.1
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• pp.29-41
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• 2018

Separating a mixture having an azeotrope or low relative volatility with single distillation column is difficult. Separating water-acetic acid mixture and water-ethanol mixture with a distillation column consumes a lot of energy. Pervaporation membrane can be used to separate the mixture in the concentration region where separation is difficult with distillation. We simulated a distillation-membrane hybrid process where membrane is located on the head of the distillation column for efficient separation of water-acetic acid and water-ethanol mixture. Permeability data were obtained from experiments and literature. We formulated an optimization problem for the process with total annual cost (TAC) as an objective function and major design variables as optimization variables. Major optimization variable affecting TAC of the hybrid process was shown to be distillate concentration. We also suggested a simplified optimization procedure to get a close-to-optimal solution.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2001-2006
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• 2008

The most concerning issue of these days is the energy crisis by increasing threat of dependency on foreign oil and its volatility itself. In the situations, the PHEV is drawing attention for the next generation's car which could give a chance to decrease the dependency on foreign oil. As well as, the Korean electric power infrastructure is a strategic national asset that is under utilized most of the time. With the proper changes in the operational paradigm, it could generate and deliver necessary energy to charge the PHEVs which could penetrate the market within few years. In doing so, it would reduce greenhouse gas emissions, improve the economics of the electricity industry, and reduce the energy dependency. This paper investigate the technical potential and impacts of using the existing idle capacity of the electric infrastructure in conjunction with the emerging PHEVs technology to meet the majority of daily energy needs of the Korean LDV fleet.