• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.139-154
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• 2021

This study's impetus is to explore fresh evidence to answer the question, i.e., whether remittances asymmetrically influence financial development in Bangladesh from 1975 to 2019. The study employs several tests, i.e., nonlinear unit root test, Autoregressive Distributed Lagged (ARDL), NARDL, and asymmetric causality test for establishing the pattern of association. Nonlinear unit root tests confirm that variables follow a nonlinear system of being stationary after the first difference. nonlinearity among variables is investigated by performing the BDS test and nonlinear OLS. Directional causality is investigated through both linear and nonlinear effects of remittance inflows by following the non-granger casualty test. The test statistics of Fpass and tBDM showed the Long-run cointegration in the empirical model and positive effect running from remittances inflow to financial development both in the long-run and short-run. Furthermore, the results of a standard Wald test divulge the presence of long-run and short-run asymmetry. Asymmetry causality test established unidirectional causality due to positive and negative shocks in remittances inflows to Bank-based financial development and feedback hypothesis hold for explaining causality between positive and negative shocks in remittance inflows and Stock-based financial development.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.433-446
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• 2021

This study aims to identify the elasticities and casualties of financial performance and determinants of the mining and extractive companies listed in Jordan's stock market over the 2005-2018 period. The conceptual framework is based on the Resource-Based View theory and Arbitrage Pricing theory is used to describe the relationship between the external environment and the financial performance of the companies. Profitability ratio (return on assets) is utilized as a proxy of financial performance measurement. Meantime, the company's characteristics, macroeconomic variables, and non-economic factors are utilized as independent factors. Data sources are panel data set for mining and extractive companies over the above period. Fully Modified Ordinary Least Square (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Pooled Mean Group (PMG) methods are applied. The empirical findings indicated that company size, sales growth, financial leverage, liquidity, and GDP growth were the critical determinants of mining and extractive companies' financial performance in the Amman Stock Exchange. Thus, the findings conclude that company characteristics and GDP growth mainly drive financial performance. Moreover, the findings reveal that a bidirectional causal elasticity exists between GDP and financial leverage and return on assets (ROA). Sound financial performance can be obtained by paying more attention to GDP growth and firms' characteristics.

• Korea Trade Review
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• v.47 no.5
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• pp.75-93
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• 2022

Purchasing Power Parity (hereafter, PPP) means the purchasing power of two currencies is the same when one is converted into the other one. According to previous studies on PPP, as the volatility of the real exchange rate is smaller, PPP may be more likely to hold. Since New Zealand adopted the inflation targeting policy in December 1989, many countries started to adopt it as their monetary policy frame. Previous studies on inflation targeting found that inflation targeting policy has positive effects on not only achieving price stability but also reducing the volatility of nominal/ real exchange rates. Therefore, in this study, I explored whether inflation targeting policy has positive effects on purchasing power parity subject to 19 OECD countries, applying an Exponential Smooth Transition Autoregressive (ESTAR) model during the sample periods, from 1974:Q1 to 2019:Q4. Based on the ESTAR estimate results, I found limited favorable evidence of PPP for only two countries- England and Switzerland- among 9 inflation targeters, compared to non-inflation targeters, and also I found that favorable evidence of PPP only for these two countries among 9 inflation targeters during post-inflation targeting, but not during pre-inflation targeting. These findings imply that the positive effects of inflation targeting on PPP may be questionable unlike Ding and Kim (2012) and Kim (2014)'s study.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.17-24
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• 2024

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.1
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• pp.1-8
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• 2024

This study analyzed the impact of Higher-order resources on profit sustainability for domestic companies using a mathematical statistical model. Higher-order resources refer to resources that do not directly affect profits but influence other resources that directly contribute to profits. As a result of analysis using 30 years of actual data from more than 650 domestic companies, the average duration of competitive advantage including high-order resources was found to be about twice as long as the period suggested by the autoregressive model excluding high-order resources. Through this, if companies want to earn more profits over a long period of time than their competitors, they must not only possess resources that are more valuable, rare, difficult to imitate, and non-substitutable compared to their competitors, but also that higher-order resources can contribute to changes in these resources over time. It was confirmed that it must lead the long-term profit difference. High-level resources include strategic planning, mergers and acquisitions (M&A) capabilities, and good forecasting.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.293-299
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• 2009

In this article, we consider linear models such as regression, ARIMA (autoregressive integrated moving average), and regression+ARIMA (regression with ARIMA errors) for predicting hourly ozone concentration level in two areas of Daegu. Based on RASE(root average squared error), it is shown that the ARIMA is the best model in one area and that the regression+ARIMA model is the best in the other area. We further analyze the residuals from the optimal models, so that we might predict the ozone warning days where at least one of the hourly ozone concentration levels is over 120 ppb. Based on the training data in the years from 2000 to 2003, it is found that 35 ppb is a good cutoff value of residulas for predicting the ozone warning days. In on area of Daegu, our method predicts correctly one of two ozone warning days of 2004 as well as all of the remaining 364 non-warning days. In the other area, our methods predicts correctly all of one ozone warning days and 365 non-warning days of 2004.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2087-2089
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• 2003

The presence of narrowband interference (NBI) in Direct-sequence code division multiple access (DS/CDMA) systems is an inevitable problem when the interference is strong enough. The improvement in the system performance employs by adaptive narrowband interference suppression techniques. Basically there have been two types of method for narrowband interference suppression estimator/subtracter approaches and transform domain approaches. In this paper the focus is on the type of estimator/subtracter approaches. However, the binary direct sequence (DS) signal, that acts as noise in the prediction process is highly non-Gaussian. The case of a Gaussian interferer with known in an autoregressive (AR) signal or a digital signal and also in a sinusoidal signal (Tone) that included in is paper. The proposed NBI suppression is presence in an adaptive IIR notch filter for lattice structure and more powerful by using a variable step-size algorithm. The simulation results show that the proposed algorithm can significantly increase the convergence rate and improved system performance when compare with adaptive least mean square algorithm (LMS).

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1033-1043
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• 2011

In this paper, we use a nested family of models of Generalized Autoregressive Conditional Heteroscedasticity(GARCH) to verify asymmetric conditional heteroscedasticity in the KOSPI and Won-Dollar exchange rate. This study starts from an investigation of whether time series data have asymmetric features not explained by standard GARCH models. First, we use kernel density plot to show the non-normality and asymmetry in data as well as to capture asymmetric conditional heteroscedasticity. Later, we use three representative asymmetric heteroscedastic models, EGARCH(Exponential Garch), GJR-GARCH(Glosten, Jagannathan and Runkle), APARCH(Asymmetric Power Arch) that are improved from standard GARCH models to give a better explanation of asymmetry. Thereby we highlight the fact that volatility tends to respond asymmetrically according to positive and/or negative values of past changes referred to as the leverage effect. Furthermore, it is verified that how the direction of asymmetry is different depending on characteristics of time series data. For the KOSPI and Korean won-US dollar exchange rate, asymmetric heteroscedastic model analysis successfully reveal the leverage effect. We obtained predictive values of conditional volatility and its prediction standard errors by using moving block bootstrap.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.244-253
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• 2013

In industries, shipping is an important issue in improving the forecasting accuracy of sales. This paper introduces a hybrid method and plural methods are compared. Focusing the equation of exponential smoothing method (ESM) that is equivalent to (1, 1) order autoregressive-moving-average (ARMA) model equation, a new method of estimating the smoothing constant in ESM had been proposed previously by us which satisfies minimum variance of forecasting error. Generally, the smoothing constant is selected arbitrarily. However, this paper utilizes the above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate the smoothing constant. Thus, theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. This method is executed in the following method. Trend removing by the combination of linear and 2nd order nonlinear function and 3rd order nonlinear function is executed to the original production data of two kinds of bread. Genetic algorithm is utilized to search the optimal weight for the weighting parameters of linear and nonlinear function. For comparison, the monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non-monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.154-154
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• 2019

Characterization of reliable field-scale root-zone soil moisture (RZSM) variability contribute to effective hydro-meterological monitoring. Although a promising cosmic-ray neutron probe (CRNP) holds the pontential for field-scale RZSM measurement, it is often restricted at deeper depths due to the non-unique sensitivity of CRNP-measured fast neutron signal to other hydrogen pools. In this study, a merging framework relied on coupling cosmic-ray soil moisture with a representative additional RZSM, was introduced to scale shallower CRNP effective depth to represent root-zone layer. We tested our proposed framework over a densely vegetated region in South Korea covering a network of one CRNP and nine in-situ point measurements. In particular, cosmic-ray soil moisture and ancillary RZSM retrieved from the most time stable location were considered as input datasets; whereas the remaining point locations were used to generate a reference RZSM product. The errors between these two input datasets and the reference were forecasted by a linear autoregressive model. A linear combination of forecasts was then employed to compute a suitable weight for merging two input products from the predicted errors. The performance of merging framework was evaluated against reference RZSM in comparison to the two original products and a commonly used exponential filter technique. The results of this study showed that merging framework outperformed other products, demonstrating its robustness in improving field-scale RZSM. Moreover, a strong relationship between the quality of input data and the performance merging framework in light of CRNP effective depth variation has been also underlined via the merging framework.