• The Korean Journal of Financial Studies
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• v.8 no.1
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• pp.207-230
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• 2002

본고에서는 한국의 증권시장과 일본의 증권시장이 같은 공매시장(Auction markets)의 형태라도 거래되는 시점에 따라 다른 거래제도에 따라 가격이 결정되는 점도 있어 각 시점별로 양 시장의 주가행태를 가격조정모형을 통하여 비교 분석하였다. 일반적으로 한국과 일본 모두 오전 시가 수익률의 분산이 종가 수익률에서의 분산보다 크고 또한 오전 시가 수익률의 분산이 다른 시점의 수익률의 분산 보다 켰다. 한국 및 일본 양 시장에서 시가수익률의 분산과 음의 시계열상관계수에서 공히 거래정지기간에 따른 노이즈 항목과 거래오류영향을 발견하였다. 그러나 한국시장에서 거래오류나 노이즈가 일본시장보다 큼을 알 수 있었다. 두 시장에서 모두 오전개장과 오후개장을 다른 시점과 비교하면 주가의 과다반응을 추정 할 수 있으나 일본시 장의 경우는 한국시장에서 보다 미미하였다. 한국시장의 경우 오후종가가 영과 통계학적으로 크게 다르게 양의 수를 가지고 있음은 시장의 효율성에서 한국시장이 일본시장에 비해 떨어지고 시장 정보에 대하여 주가의 가격조정속도가 늦다고 추정할 수 있었고 이는 한국시장이 아직은 일본에 비해 가격제한폭이나 그밖에 거래제한 요소가 일본보다 크기 때문으로 추정된다. 한국시장에서는 주식수익률의 변동성은 노이즈거래가설(Noise trading hypothesis)이 더 설명력이 있다고 추정되고 일본의 경우 사적정보가설(Private information hypothesis)과 노이즈거래가설(Noise trading hypothesis) 둘 다 설명력이 있지만 전자가 더 우위일 가능성이 있었다. 결론적으로 위의 결과를 종합하면 거래메커니즘 차이가 주가의 행태에 크게 다르게 미친다고는 할 수 없고 단지 주가의 정보전달 속도 및 노이즈 그리고 시장의 효율성에 따라 주가행태에 어느 정도 차이를 일으킨다고 할 수 있었다.

• Explosives and Blasting
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• v.13 no.4
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• pp.73-81
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• 1995

Explosives demolition mothod is allowed for more efficient time-saving and safer demolitioni operations as compared to conventional / mechanical demolition methods. CDI has to minimize the effects of noise, dust and various demolition hazards to the public areas, and residences that are located adjacent to the project site. CDI's explosives demolition work on the Nam san Foreigner's Apartment Complex and chosun trading Co's factory are backed by over 45 years of explosives experience in the demolition of over 6,000 structures worldwide, many of these structures are similar to the Nam san Foreigner's Apt. and Chosun trading's factory in construction and proximity to sensitive adjacent exposures. Recoginized worldwide as the founder of the leader in explosives demolitioni technology, CDI always will applied "State-of-the-Art" explosives techniques to safely and successfully achieve the desired demolition results on these project. CDI has never injured, much less caused any fatality, to either a worker on one of our sites or to a third party during the implosion of high-rise structure.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.163-171
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• 2021

In this paper, the energy transaction system was optimized by applying a resource allocation algorithm and deep reinforcement learning in the distributed power system. The power demand and supply environment were predicted by deep reinforcement learning. We propose a system that pursues common interests in power trading and increases the efficiency of long-term power transactions in the paradigm shift from conventional centralized to distributed power systems in the power trading system. For a realistic energy simulation model and environment, we construct the energy market by learning weather and monthly patterns adding Gaussian noise. In simulation results, we confirm that the proposed power trading systems are cooperative with each other, seek common interests, and increase profits in the prolonged energy transaction.

• The Journal of Asian Finance, Economics and Business
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• v.3 no.3
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• pp.79-91
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• 2016

This paper is the first of its kind using a non-linear approach based on cross-correlation function (CCF) to investigate the information arrival hypothesis in crude palm oil (CPO) futures market. Based on daily data from 1986 to 2010, our empirical results reveal that: First, the volume of volatility is not a proxy of information flow. Second, dependence causality running from current return to future volume in conditional variance exhibit an asymmetric pattern of time span with different signs of correlation between price and volume series. This finding indicates the presence of noise traders' hypothesis of price-volume interaction in CPO futures market. Both findings suggest that this futures market is weak-form inefficiency. In terms of investors' behavior, they tend to change their expectations on current return based on errors made in previous trade in generating abnormal volume in the subsequent period. As implied, it is advisable for the investors devise their future trading strategies according to time span and changes of return.

• The Korean Journal of Financial Management
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• v.9 no.1
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• pp.135-175
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• 1992

본 논문은 주가(株價)의 변동성검증모형(變動性檢證模型)을 한국주식시장(韓國株式市場)에 적용하여 시장의 효율성을(效率性) 검증하였다. 1975년부터 1990년까지 의 17년 기간중 Shiller[81a], Mankiw-Romer-Shapiro[85], West[88b]의 모형을 이용하여 검증한 결과, 높은 수준의 과잉변동성(過剩變動性)이 발견되었다. 그러나 이러한 주가의 과잉 변동성을 시장이 비효율적이라는 증거로 간주하기는 어렵다. 왜냐하면 이들 모형의 가정 중의 하나인 할인율이 일정하다는 가정을 완화시켜 다시 검증한 결과는 오히려 과잉변동성 중에서 많은 부분이 감소하는 것으로 나타났기 때문이다. 다시말해서 주가에 과잉변동성이 존재하는 것은 시장이 비효율적이라기 보다는 기간에 따른 할인율의 변동폭이 큰 데에 원인이 있는 것으로 해석된다. 따라서 할인율(割引率)의 변동원인(變動原因)을 조사하여 주식시장의 효율성을 분석하였다. 주가의 일부는 랜덤웍의 요소에 의해 결정되고, 나머지 일부는 평균회귀(平均回歸) fads(mean reverting fads)에 의해 결정된다고 가정하여 검증한 결과, 후자에 의해 설명된 비율이 매우 높게 나타났다. 즉 한국주식시장에서의 투자자는 교란거래(攪亂去來)(noise trading)나 피드백거래(去來)(feedback trading) 등의 비합리적인 투자행동을 취하고 있으며, 이러한 비합리적인 행동에 의해 주가변동의 $60{\sim}80%$가 설명되는 것으로 보인다.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.15-27
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• 2012

We have studied the realized variance(RV) of intra-day returns and market microstructure noise based on high-frequency stock transaction data for the four largest companies in terms of market capitalization in the KOSPI. First, non-negligible biases are observed for the RV and for the bias-corrected realized variance($RV_{AC_1}$) which is constructed by adjusting RV for the first order autocorrelation in intra-day returns. Bias is more obvious for the RV and the $RV_{AC_1}$ when intra-day returns are sampled more frequently than every 2 minutes. Transaction Time Sampling(TTS) is shown to be better than Calendar Time Sampling(CTS) in terms of biases of the RV and the $RV_{AC_1}$ for the 4 companies. The analysis reveals that market microstructure noise is temporally dependent. Second, by using the Noise-to-Signal Ratio(NSR), we estimate sampling frequencies that are optimal in terms of the Mean Square Errors(MSE) of the RV and the $RV_{AC_1}$. The optimal sampling frequencies are around 200 for RV and is around 5000 for the $RV_{AC_1}$ for all the four stock prices. For the 6 hour transaction period of the Korean stock trading, these correspond to about 2 minutes and 6 seconds.

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.3
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• pp.20-25
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• 1980

A singular value decomposition (SVD) pseudo-inversion method has been applied to the image reconstruction from projections. This approach is relatively unknown and differs from conventionally used reconstructioll methods such as the Foxier convolution and iterative techniques. In this paper, two SVD pseudo-inversion methods have been discussed for the search of optimum reconstruction and restoration, one using truncated inverse filtering, the other scalar Wiener filtering. These methods partly overcome the ill-conditioned nature of restoration problems by trading off between noise and signal quality. To test the SVD pseudo-inversion method, simulations were performed from projection data obtained from a phantom using truncated inversefiltering. The results are presented together with some limitations particular to the applications of the method to the general class of 3-D image reconstruction and restoration.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.9C
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• pp.1316-1322
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• 2004

Recently, the UWB (ultra wide-band) wireless communication technology, which provides high data transmission and is capable of linearly trading between throughput and signal-to-noise ratio (SNR), has drawn much attention for short-range wireless networks. Fully exploiting its notable features and minimizing its interference to coexisting other systems require the knowledge of SNR's at receivers In this paper, we propose an algorithm for estimating the pulse energy to noise ratio Ep/No of UWB signal with utilization of outputs from a correlator at a receiver, and evaluate the performance of the proposed algorithm through computer simulation. According to simulation results, the maximum standard deviation is about 1 13 dB with a block size of 500. Except for Ep/No=O and 2 dB cases with a block size of 500, no errors greater than 3 dB were observed in all the remaining experiments. Generally speaking, it improves as the true Ep/No, increases and as the block size increases A notable feature of the proposed algorithm is that it does not reduce the effective throughput because the estimation process does not require sending additional training signal of any specific format.

• JSTS:Journal of Semiconductor Technology and Science
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• v.3 no.4
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• pp.211-216
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• 2003

This work describes a 3 V 12b 100 MS/s CMOS digital-to-analog converter (DAC) for high-speed communication system applications. The proposed DAC is composed of a unit current-cell matrix for 8 MSBs and a binary-weighted array for 4 LSBs, trading-off linearity, power consumption, chip area, and glitch energy with this process. The low-glitch switch driving circuits are employed to improve linearity and dynamic performance. Current sources of the DAC are laid out separately from the current-cell switch matrix core block to reduce transient noise coupling. The prototype DAC is implemented in a 0.35 um n-well single-poly quad-metal CMOS technology and the measured DNL and INL are within ${\pm}0.75$ LSB and ${\pm}1.73$ LSB at 12b, respectively. The spurious-free dynamic range (SFDR) is 64 dB at 100 MS/s with a 10 MHz input sinewave. The DAC dissipates 91 mW at 3 V and occupies the active die area of $2.2{\;}mm{\;}{\times}{\;}2.0{\;}mm$