• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.189-212
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• 2005

This study examines the interactions between KTB spot and futures markets using the daily prices from March 4, 2002 to January 31, 2005. We use Granger causality test, impulse Response Analysis and Variance Decomposition through vector autoregressive analysis (VAR). However, considering the long-term relationships between the level variables of KTB spot and futures, we introduced Vector Error Correction Model. The main results are as follows. According to the results of Granger-causality test and impulse response analysis, we find that the yields of KTB forward have a great influence on the change of KTB spot but not vice versa. In terms of volatility analysis, there is no inter-dependence between KTB forward and spot markets. In the variance decomposition analysis we find that the short-term KTB forward has much more impact on the KTB spot market than the long-term KTB forward does. We think these results are meaningful for bond investors who are in charge of capital asset pricing valuation, risk management and international portfolio management.

• The Korean Journal of Financial Management
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• v.12 no.2
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• pp.239-271
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• 1995

본 연구는 현재 미국에서 거래되고 있는 세 가지 주가지수선물 상호간의 일중(intradaily) 가격선도(price leadership) 관계에 관한 실증분석이다. 본 연구가 기존의 연구와 다른점은, 기존의 연구가 주가지수선물과 그 기준이 되는 현물 가격사이의 가격 선도 관계에 초점을 두고 있는데 반하여 본 연구는 주가지수선물 시장 사이에서 존재하는 가격 선도관계를 분석하고 있다는 점이다. 실증 분석의 대상이 된 주가지수선물들은 Chicago Mercantile Exchange의 Standard and Poor's 500 Index(S&P 500), New York Futures Exchange의 New York Stock Exchange Composit Index (NYSE), 그리고 Chicago Board of Trade의 Major Market Index(MMI)이다. 만약 이들 시장들이 정보의 전달에 있어서 효율적(informationally efficient) 이라면 이들 가격간에 선도-지연(lead-lag) 현상은 존재하지 않을 것이다. 그러나 어느 한 시장이 새로운 정보를 선물가격에 반영하는데 다른 시장에 비해 상대적으로 느리다면, 이들 시장 상호간에는 가격의 전이(transmission)현상이 존재하게 될 것이다. 이들 선물간의 일중 가격선도 관계 연구는 이러한 시장의 효율성 문제를 밝히는데 의의가 있을 뿐만 아니라, 시장간의 단기적 가격 괴리를 이용하려는 차익거래자들에게도 유용하게 쓰일 수 있을 것이다. 본 연구는 위에서 언급한 각각의 주가지수선물들이 가격 선도성을 가질 수 있는 이유와 관련된 다음과 같은 세 가지 가설을 설정하였다. 첫째 가설은, 가격의 선도성은 거래량과 관련이 있다는 것이다. 즉, 이들 주가지수선물 중 가장 거래량이 많은 S&P 500 선물이 다른 선물을 선도할 것이라는 가설이다. 둘째, 가격의 선도성은 주가지수를 구성하는 주식의 수에 비례한다는 가설이다. 다시 말하면, 보다 않은 수로 구성된 주가지수일수록 정보처리 속도가 빠르다는 가설이다. 따라서, 본 연구에 포함된 주가지수선물 중 가장 많은 수의 주식을 대상으로 하는 NYSE 선물이 다른 선물을 선도할 것이다. 마지막 가설은 정보의 처리는 대형주 혹은 기관선호주(institutionally-favored)들이 주도한다는 것이다. 따라서, 주로 이와 같은 주식들로 구성 된 MMI 선물이 선도성을 가질 수 있다는 것이다. 위의 가설들을 검증하고 시장간의 가격 선도관계를 분석하기 위하여 본 연구는 vector autoregressive(VAR) 모형을 이용하여 충격-반응 함수(impulse response functions)를 계산하고, 분산분해(variance decomposition)를 수행하였다. 또한 가격 상호간에 존재할지도 모르는 공적분(cointegration)관계를 Johansen(1991)과 Jokansen and Juselius (1992) 등이 제시한 다변량 공적분 검정(multivariate cointegration test)를 통하여 분석하였다. 분석기간은 1986년 1월부터 1990년 7월까지이며, 각 주가지수선물들의 5분 간격 data를 사용하였다. 연구결과, 충격-반응 분석은 어느 한 시장에서의 충격(shock)은 다른 시장으로 매우 빠르게 전달되고 있음을 보여 주었다. 그러나 충격의 지속정도는 그 충격의 진원지에 따라 달랐다. 즉, NYSE나 MMI 선물로부터 발생 한 충격은 다른 시장의 가격에 5분 안에 반영을 끝냈지 만 S&P 500 선물에서 발생한shock은 그 이상 지속되었다. 또한, 분산분해 결과 S&P 500 선물이 자기자신 뿐만 아니라 다른 시장의 예상하지 못했던 움직임(unexpected movements)을 설명하는데 가장 큰 설명력(explanatory power)을 가지고 있었다. 결론적으로 S&P 500 선물이 다른 선물을 약 5분 간격으로 선도하였다. 이는 가격의 선도가 거래량과 밀접한 관계가 있음을 보여 주는 것이다.

• Journal of Distribution Science
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• v.11 no.10
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• pp.73-79
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• 2013

Purpose - This study examines the impact of oil price volatility on economic activities in Korea. The new millennium has seen a deregulation in the crude oil market, which invited immense capital inflow into Korea. It has also raised oil price levels and volatility. Drawing on the recent theoretical literature that emphasizes the role of volatility, this paper attends to the asymmetric changes in economic growth in response to the oil price movement. This study further examines several key macroeconomic variables, such as interest rate, production, and inflation. We come to the conclusion that oil price volatility can, in some part, explain the structural changes. Research design, data, and methodology - We use two methodological frameworks in this study. First, in regards to the oil price uncertainty, we use an Exponential-GARCH (Exponential Generalized Autoregressive Conditional Heteroskedasticity: EGARCH) model estimate to elucidate the asymmetric effect of oil price shock on the conditional oil price volatility. Second, along with the estimation of the conditional volatility by the EGARCH model, we use the estimates in a VECM (Vector Error Correction Model). The study thus examines the dynamic impacts of oil price volatility on industrial production, price levels, and monetary policy responses. We also approximate the monetary policy function by the yield of monetary stabilization bond. The data collected for the study ranges from 1990: M1 to 2013: M7. In the VECM analysis section, the time span is split into two sub-periods; one from 1990 to 1999, and another from 2000 to 2013, due to the U.S. CFTC (Commodity Futures Trading Commission) deregulation on the crude oil futures that became effective in 2000. This paper intends to probe the relationship between oil price uncertainty and macroeconomic variables since the structural change in the oil market became effective. Results and Conclusions - The dynamic impulse response functions obtained from the VECM show a prolonged dampening effect of oil price volatility shock on the industrial production across all sub-periods. We also find that inflation measured by CPI rises by one standard deviation shock in response to oil price uncertainty, and lasts for the ensuing period. In addition, the impulse response functions allude that South Korea practices an expansionary monetary policy in response to oil price shocks, which stems from oil price uncertainty. Moreover, a comparison of the results of the dynamic impulse response functions from the two sub-periods suggests that the dynamic relationships have strengthened since 2000. Specifically, the results are most drastic in terms of industrial production; the impact of oil price volatility shocks has more than doubled from the year 2000 onwards. These results again indicate that the relationships between crude oil price uncertainty and Korean macroeconomic activities have been strengthened since the year2000, which resulted in a structural change in the crude oil market due to the deregulation of the crude oil futures.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.89-99
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• 2012

This study investigates the Granger-causality between economic growth and demand for electricity in Korea, using two quarterly time-series data (real GDP and electricity consumption) for 1970:Q1 through 2009:Q4. We apply Hsiao's sequential procedure to identify a vector autoregressive model to a decision of the optimal lags in the vector error-correction model because the two time-series data contain unit roots respectively and they are cointegrated. According to the empirical results in this study, we find that Hsiao's approach to the Granger-causality indicates a bidirectional causal relation between economic growth and demand for electricity in Korea. Following the Granger and Engle's approach, we also find the statistical evidence on (1) short-run bidirectional causality between real GDP and electricity consumption, (2) bidirectional strong causality between them, and (3) long-run unidirectional causality running from demand for electricity to economic growth. Our results show an inconsistency with the existing studies on Korea's case; however, the results appear to provide more meaningful policy implications for the Korean economy and its strategy of sustainable growth.

• Journal of Wetlands Research
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• v.18 no.3
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• pp.313-323
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• 2016

Water temperature affects physical and biological processes in ecologies on river system and is important conditions for growth rate and spawning of fish species. The objective of this study is to compare models for water temperature during the summer season for the Fourchue River (St-Alexandre-de-Kamouraska, Quebec, Canada). For this, three different models, which are CEQUEAU, Auto-regressive Moving Average with eXogenous input and Nonlinear Autoregressive with eXogenous input, were applied and compared. Also, future water temperature in the Fourchue river were simulated and analyzed its result based on the CMIP5 climate models, RCP 2.6, 4.5, 8.5 climate change scenarios. As the result of the study, the water temperature in the Fourchue river are actually changed and median water temperature will increase $0.2{\sim}0.7^{\circ}C$ in June and could decrease by $0.2{\sim}1.1^{\circ}C$ in September. Also, the UILT ($24.9^{\circ}C$) for brook trout are also likely to occurred for several days.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.241-241
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• 2012

Semiconductor industry has been taking the advantage of improvements in process technology in order to maintain reduced device geometries and stringent performance specifications. This results in semiconductor manufacturing processes became hundreds in sequence, it is continuously expected to be increased. This may in turn reduce the yield. With a large amount of investment at stake, this motivates tighter process control and fault diagnosis. The continuous improvement in semiconductor industry demands advancements in process control and monitoring to the same degree. Any fault in the process must be detected and classified with a high degree of precision, and it is desired to be diagnosed if possible. The detected abnormality in the system is then classified to locate the source of the variation. The performance of a fault detection system is directly reflected in the yield. Therefore a highly capable fault detection system is always desirable. In this research, time series modeling of the data from an etch equipment has been investigated for the ultimate purpose of fault diagnosis. The tool data consisted of number of different parameters each being recorded at fixed time points. As the data had been collected for a number of runs, it was not synchronized due to variable delays and offsets in data acquisition system and networks. The data was then synchronized using a variant of Dynamic Time Warping (DTW) algorithm. The AutoRegressive Integrated Moving Average (ARIMA) model was then applied on the synchronized data. The ARIMA model combines both the Autoregressive model and the Moving Average model to relate the present value of the time series to its past values. As the new values of parameters are received from the equipment, the model uses them and the previous ones to provide predictions of one step ahead for each parameter. The statistical comparison of these predictions with the actual values, gives us the each parameter's probability of fault, at each time point and (once a run gets finished) for each run. This work will be extended by applying a suitable probability generating function and combining the probabilities of different parameters using Dempster-Shafer Theory (DST). DST provides a way to combine evidence that is available from different sources and gives a joint degree of belief in a hypothesis. This will give us a combined belief of fault in the process with a high precision.

• Environmental and Resource Economics Review
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• v.23 no.2
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• pp.187-224
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• 2014

We present key issues to consider in estimating damages from price-fixing cases and then apply the procedure addressing those issues to a transportation fuel market. Among the five methods of overcharge calculation, the regression analysis incorporating the yardstick method is the best. If the price equation relates the domestic price to the foreign price and the exchange rate as in the transportation fuel market, the functional form satisfying both logical consistency and modeling flexibility is the log-log functional form. If the data under analysis is of time series in nature, then the ARDL model should be the base model for each market and the regression analysis incorporating the yardstick method combines these ARDL equations to account for inter-market correlation and arrange constant terms and collusion-period dummies across component equations appropriately so as to identify the overcharge parameter. We propose a two-step test for the benchmarked market: (a) conduct market-by-market Spearman or Kendall test for randomness of the individual market price series first and (b) then conduct across-market Friedman test for homogeneity of the market price series. Statistical significance is the minimal requirement to establish the alleged proposition in the world of uncertainty. Between the sensitivity analysis and the model selection process for the best fitting model, the latter is far more important in the economic analysis of damage in price-fixing litigation. We applied our framework to a transportation fuel market and could not reject the null hypothesis of no overcharge.

• Journal of Biomedical Engineering Research
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• v.16 no.4
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• pp.507-516
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• 1995

The nonstationary identifier in the DCT domain is suggested in this study for the identification of AR parameters of above-lesion upper-trunk electromyographic (EMG) signals as a means of developing a reliable real time signal to control functional electrical stimulation (FES) in paraplegics to enable primitive walking. As paraplegic shifts his posture from one attitude to another, there is transition period where the signal is clearly nonstationary. Also as muscle fatigues, nonstationarities become more prevalent even during stable postures. So, it requires a develpment of time varying nonstationary EMG signal identifier. In this paper, time varying nonstationary EMG signals are transformed into DCT domain and the transformed EMG signals are modeled and analyzed in the transform domain. In the DCT domain, we verified reduction of condition number and increment of the smallest eigenvalue of input correlation matrix that influences numerical properties and mean square error were compared with SLS algorithm, and the proposed algorithm is implemented using IMS T-805 parallel processing computer for real time application.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.1
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• pp.33-41
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• 2014

Unlike the existing regression analysis, this study anticipated future marine traffic volume using time series analysis and artificial neural network model. Especially, it tried to anticipate future marine traffic volume by applying predictive value through time series analysis on artificial neural network model as an additional input variable. This study used monthly observed values of Incheon port from 1996 to 2013. In order for the verification of the forecasting of the model, value for 2013 is anticipated from the built model with observed values from 1996 to 2012 and a proper model is decided by comparing with the actual observed values. Marine traffic volume of Incheon port showed more traffic than average for May and November by 5.9 % and 4.5 % respectably, and January and August showed less traffic than average by 8.6 % and 4.7 % in 2015. Thus, it is found that Incheon port has difference in monthly traffic volume according to the season. This study can be utilized as a basis to reflect the characteristics of traffic according to the season when investigating marine traffic field observation.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.79-84
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• 2015

In a distribution of digital image, there is a serious problem that is a distribution of the altered image by a forger. For the problem solution, this paper proposes a median filtering (MF) image forensic decision algorithm using a feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value' gradients of original image then 1th~6th order coefficients to be six feature vector. And the reconstructed image is produced by the solution of Poisson's equation with the gradients. From the difference image between original and its reconstructed image, four feature vector (Average value, Max. value and the coordinate i,j of Max. value) is extracted. Subsequently, Two kinds of the feature vector combined to 10 Dim. feature vector that is used in the learning of a SVM (Support Vector Machine) classification for MF (Median Filtering) detector of the altered image. On the proposed algorithm of the median filtering detection, compare to MFR (Median Filter Residual) scheme that had the same 10 Dim. feature vectors, the performance is excellent at Unaltered, Averaging filtering ($3{\times}3$) and JPEG (QF=90) images, and less at Gaussian filtering ($3{\times}3$) image. However, in the measured performances of all items, AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.