• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.115-122
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• 2015

This article is concerned with count time series taking values in non-negative integers. Along with the first order mean of the count time series, conditional variance (volatility) has recently been paid attention to and therefore various integer-valued GARCH(generalized autoregressive conditional heteroscedasticity) models have been suggested in the last decade. We introduce diverse integer-valued GARCH(INGARCH, for short) processes to count time series and a real data application is illustrated as a case study. In addition, zero inflated INGARCH models are discussed to accommodate zero-inflated count time series.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.176-176
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• 2018

This study presents a regional, probabilistic framework for estimating streamflow via spatial scaling in the Great Lakes basin, which is the largest lake system in the world. The framework follows a two-fold strategy including (1) a quadratic-programming based optimization model a priori to explore the model structure, and (2) a time-varying hierarchical Bayesian model based on insights found in the optimization model. The proposed model is developed to explore three innovations in hierarchical modeling for reconstructing historical streamflow at ungaged sites: (1) information of physical characteristics is utilized in spatial scaling, (2) a time-varying approach is introduced based on climate information, and (3) heteroscedasticity in residual errors is considered to improve streamflow predictive distributions. The proposed model is developed and calibrated in a hierarchical Bayesian framework to pool regional information across sites and enhance regionalization skill. The model is validated in a cross-validation framework along with four simpler nested formulations and the optimization model to confirm specific hypotheses embedded in the full model structure. The nested models assume a similar hierarchical Bayesian structure to our proposed model with their own set of simplifications and omissions. Results suggest that each of three innovations improve historical out-of-sample streamflow reconstructions although these improvements vary corrsponding to each innovation. Finally, we conclude with a discussion of possible model improvements considered by additional model structure and covariates.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.913-921
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• 2021

The objective of the study is to investigate the impact of the COVID-19 pandemic on Saudi Arabia stock market. The study relied on the data of the daily closing stock market price index Tadawul All Share Index (TASI), and the number of daily cases infected with COVID-19 during the period from March 15, 2020, to August 10, 2020. The study employs the Vector Auto-Regressive (VAR) model, the Impulse Response Function (IRF) and Autoregressive Conditional Heteroscedasticity (ARCH) models. The results of the correlation matrix and the Impulse Response Function (IRF) show that stock market returns responded negatively to the growth in COVID-19 infected cases during the pandemic. The results of ARCH model confirmed the negative impact of COVID-19 pandemic on KSA stock market returns. The results also showed that the negative market reaction was strong during the early days of the COVID-19 pandemic. The study concluded that stock market in KSA responded quickly to the COVID-19 pandemic; the response varies over time according to the stage of the pandemic. However, the Saudi government's response time and size of the stimulus package have played an important role in alleviating the impacts of the COVID-19 pandemic on Saudi Arabia Stock Market.

• The Journal of Fisheries Business Administration
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• v.51 no.4
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• pp.85-96
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• 2020

The purpose of this study is to find out the determinants of export in Korean fishery products. For the analysis, laver and tuna, which account for almost half of seafood exports, were selected, and a gravity model widely used in trade analysis was applied. As explanatory variables, GDP, number of overseas Koreans, exchange rate, FTA, and WTO were applied, and fixed effect terms were included to take into account multilateral resistance that hinders trade. The analysis period is from 2000 to 2018, and the Poisson Pseudo Maximum Likelihood (PPML) method was applied to solve the problem of zero observation and heteroscedasticity inherent in trade data. As a result of the analysis, GDP was found to have a significant positive effect on both laver and tuna. The number of overseas Koreans was significant in canned tuna exports, but not in laver and the other tuna products. As the exchange rate increased, the export of laver and tuna for sashimi increased. The impacts of the FTA were confirmed in the exports of dried laver and raw tuna, which supports the results of the previous study. WTO was not significant for laver and tuna. Based on these results, it is necessary to find a way to make good use of the FTA to expand exports of seafood.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.785-799
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• 2018

With the development of renewable energy sector, the importance of solar energy is continuously increasing. Solar radiation forecasting is essential to accurately solar power generation forecasting. In this paper, we used time series models (ARIMA, ARIMAX, seasonal ARIMA, seasonal ARIMAX, ARIMA GARCH, ARIMAX-GARCH, seasonal ARIMA-GARCH, seasonal ARIMAX-GARCH). We compared the performance of the models using mean absolute error and root mean square error. According to the performance of the models without exogenous variables, the Seasonal ARIMA-GARCH model showed better performance model considering the problem of heteroscedasticity. However, when the exogenous variables were considered, the ARIMAX model showed the best forecasting accuracy.

• Korean Journal of Agricultural Science
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• v.45 no.4
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• pp.859-870
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• 2018

Dried red peppers are a staple agricultural product used in Korean cuisine and as such, are an important aspect of agricultural producers' income. Correctly forecasting both their supply and demand situations and price is very important in terms of the producers' income and consumer price stability. The primary objective of this study was to compare the performance of time series forecasting models for dried red peppers in Korea. In this study, three models (an autoregressive model with exogenous variables [ARX], AR-exponential generalized autoregressive conditional heteroscedasticity [EGARCH], and ARX-EGARCH) are presented for forecasting the wholesale price of dried red peppers. As a result of the analysis, it was shown that the ARX model and ARX-EGARCH model, each of which adopt both the rolling window and the adding approach and use the agricultural cooperatives price as the exogenous variable, showed a better forecasting performance compared to the autoregressive model (AR)-EGARCH model. Based on the estimation methods and results, there was no significant difference in the accuracy of the estimation between the rolling window and adding approach. In the case of dried red peppers, there is limitation in building the price forecasting models with a market-structured approach. In this regard, estimating a forecasting model using only price data and identifying the forecast performance can be expected to complement the current pricing forecast model which relies on market shipments.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.49-59
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• 2022

This paper examined the impact of real exchange rate volatility on trade balance in Malaysia by using quarterly data from year 2000 until 2019. Generalized Autoregressive Heteroscedasticity (GARCH) model was used to extract the volatility component of real exchange rate before examining its impact on trade balance. Furthermore, Autoregressive Distributed Lag (ARDL) model was used to investigate the long-run relationship and short-run dynamic between trade balance, money supply, national income and volatility of exchange rate. Empirical results show the existence of co-movement between variables under study in the long-run. However, the results also suggest that volatility of real exchange rate does not significantly affect trade balance neither in the long-run nor short-run. The risk which is associated in the movement of exchange rate do not influence trader's behaviour toward Malaysia exports and imports. Thus, it should be note that any depreciation or appreciation in Malaysian Ringgit do not have an impact towards trade balance either it is being further improved or deteriorates. Hence, exchange rate volatility may not be too concern for policymakers. This may be partially due to manage floating exchange rate regime that has been adopted by Malaysia eventually eliminated the element of risk in the currency market.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.767-780
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• 2021

The purpose of this research is to analyze the effect of financial elements (asset growth, liability growth, equity growth, revenue growth, and profit growth) on stock price performance and to analyze the growth of Corporate Social Responsibility (CSR) costs as a moderating effect. The technique analysis used is regression analysis. Samples in this analysis are manufacturing firms listed on the Indonesian Stock Exchange (IDX) for the period 2014-2018. The use of regression models for hypothesis testing must fulfill several applicable assumptions such as Normality Test, Heteroscedasticity Test, Multicollinearity Test, Autocorrelation Test, Model Fit Test, Determination Coefficient Test, and Hypothesis Test. Data analysis used two research models, namely model 1 and model 2. Model 1 is without the moderating variable, and model 2 is with the moderating variable, that is, CSR cost growth. Based on the result of the regression analysis, it can be inferred that the asset, revenue, and profit growth have a positive impact on stock price results. Liabilities and equity growth do not affect stock price performance. Operating expense growth has a significant effect on price performance. CSR cost growth can moderate the effect of growth in financial statement elements on stock price performance but is not significant.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.9
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• pp.143-156
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• 2022

This article examines the effect of inward foreign direct investments (FDI) on the export performance of micro, small & medium enterprises (MSMEs) in India, and investigates the spillover impact and absorption capacity of the MSMEs sector. For the first time, the researchers applied the intersectoral linkage approach to investigate the matter and used a panel dataset between 2006 and 2017. The coefficients of forward and backward linkages are estimated by using the Rasmussen method, the study employs a basic linear panel data model, followed by various diagnostic tests to identify the problem of heteroscedasticity, autocorrelation / serial correlation, cross-sectional dependencies, multicollinearity, time-individual specific tests, and unobserved effects. The PCSE model was applied for robust standard error and the Hausman-Taylor IV model to check the robustness of the result generated in the linear panel data model. Despite the high prevalence of forward and backward intersectoral connections and the Lack of absorption capacity of local firms, the results show that FDI has little of an impact on the export performance of micro, small, and medium-sized businesses in India. This study adds to the existing literature on determining local firms' spillover effect and absorption capacity in response to inward FDI.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.135-145
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• 2022

To improve audit quality, certain Chinese auditing firms have added a third-level review by an additional signing auditor to the general evaluation by a signing auditor team consisting of an engagement auditor and a partner. Nonetheless, our research-based on 36,033 firm-year observations from 2004 to 2019 reveals that compared to the general review system, auditor teams under the three-level review system are less likely to issue modified audit opinions when abnormal financial conditions arise. This finding suggests that, while larger auditor teams' knowledge, experience, and information advantages can theoretically sharpen their judgment, their performance is more susceptible to interference from divergent opinions, the diffusion of responsibility, and lower energy invested by individual auditors, ultimately impairing their judgment regarding the audited enterprises' abnormal financial conditions. That is, the three-level review system, which aims to improve audit quality, actually worsens audit quality. This conclusion remains valid after the problems of heteroscedasticity and endogeneity are addressed by using firm-level cluster robust standard errors and two-stage regression. We hope that our research will draw the attention of auditing firms, prompting them to reconsider the rationality of the three-level review system.